Post on 25-May-2020
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under
grant agreement No 646.531
Demonstration results: Evaluation and opportunities
D3.4
2015 The UPGRID Consortium
Real proven solutions to enable active demand and distributed
generation flexible integration, through a fully controllable
LOW Voltage and medium voltage distribution grid
Demonstration in real user environment:
Iberdrola – Spain
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PROGRAMME H2020 – Energy Theme
GRANT AGREEMENT NUMBER 646.531
PROJECT ACRONYM UPGRID
DOCUMENT D3.4
TYPE (DISTRIBUTION LEVEL) ☒ Public
☐ Confidential
☐ Restricted
DUE DELIVERY DATE 30/09/2017
DATE OF DELIVERY 30/09/2017
STATUS AND VERSION v10
NUMBER OF PAGES 201
WP / TASK RELATED WP3 / Task 3.4
WP / TASK RESPONSIBLE IBERDROLA / IBERDROLA
AUTHOR (S) IBERDROLA (Ana González, Raúl Bachiller)
PARTNER(S) CONTRIBUTING IBERDROLA (Marta Elorduy, Ainara Fernández,
Roberto González), GE (Javier Sánchez, Mónica
Pintado, Pablo Sanguino, José Miguel Campanario,
Domingo López), ZIV (Laura Marrón, Sonia
Martínez), TECNALIA (Eduardo García, Sergio Gil,
Joseba Jimeno, José Oyarzabal, Sandra Riaño,
Nerea Ruiz, Izaskun Mendia), EVE (Iñaki Bóveda)
OFFICIAL REVIEWER/S EDP Distribuição (Gonçalo Faria), IEN (Aleksander
Babs)
FILE NAME UPGRID_WP3_D3.4_Demonstration results_v10
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DOCUMENT HISTORY
VERS. ISSUE DATE CONTENT AND CHANGES
v00 24/02/2017 Table of Contents (ToC)
v01 14/06/2017 Version of the Introduction and Benefits overview chapters
v02 14/07/2017 Chapter 5 and 7 contributions. Chapters consolidated
v03 09/08/2017 Version with chapters introductions
v04 04/09/2017 Chapter 3 contributions.
v05 06/09/2017 Chapter 4 and Annexes contributions: First draft completed
v06 15/09/2017 Chapter 4 and Annexes: version consolidated
v07 17/09/2017 Version for official review
v08 24/09/2017 Version with consolidated internal WP3 comments
v09 28/09/2017 Version considering Official Reviewers comments
V10 30/09/2017 Final version
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EXECUTIVE SUMMARY
The present deliverable is focused on the performance evaluation of the UPGRID Spanish demonstrator
in different low voltage (LV) domains (e.g. grid operation and maintenance (O&M), new business
processes and Consumer empowerment) conducted over the best part of 2017. The systems and tools
tested were designed, developed and deployed mainly during the first two years of the demonstrator
(2015-2016). This is described in three deliverables (D3.1 [1], D3.2 [2] and D3.3 [3]), which are
summarised as follows, and being the demonstrator streams of work those shown in Figure 1.
FIGURE 1: SPANISH DEMONSTRATOR LINES OF WORK
The UPGRID Spanish demonstrator has been focused on developments aimed at gathering detailed,
enriched and accurate representation of the LV network, covering components, topology, status,
operation, connectivity, performance, loads, etc., in a real time basis. This accurate and reliable LV
network diagram representation is the key to rely O&M decisions on it. This diagram is the foundation of
the Low Voltage Network Management System (LV NMS) deployed in the demonstrator. The latter
system has been deployed in form of two different solutions: the LV NMS Desktop solution for control
centres and the LV NMS Mobile solution (e.g. running in tablets) for LV Field Crews. The LV NMS is not a
standalone system but it has been integrated with others that are already in operation. This has
required the definition of new interfaces with, for example, the Geographic Information System (GIS),
Advanced Metering Infrastructure (AMI) and Supervisory Control And Data Acquisition (SCADA). More
detail can be found in [1].
The development of LV grid remote control operation over smart metering PoweRline Intelligent
Metering Evolution (PRIME) technology is the second key content of the document. Two main
conclusions have been drawn from laboratory tests [1]. Existing PRIME infrastructure can be used, not
only to retrieve metering data from smart meters, but also to support Internet Protocol (IP) traffic which
can serve multiple purposes. One of the main applications is adding remote control operation in LV. In
addition, a standard protocol, such as Simple Network Management Protocol (SNMP), can be used to
retrieve statistics about PRIME networks performance and bandwidth usage which can help grid
Operators to analyse and optimise both metering and remote control traffic.
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FIGURE 2: GENERAL VIEW (NOT ZOOMED) OF PART OF THE DEMONSTRATOR LV NETWORK MODEL
Other additional demonstrator supportive enhancement covered in this deliverable is the analysis of
smart meter events, which is aimed at demonstrating that the existing AMI can be exploited beyond
billing purposes. This work has made possible to develop a methodology and tools to perform a more
rational, automated structuring and offline processing of smart meter events to support maintenance
field work.
The main monitoring, operation and control capabilities developed in the Spanish demonstrator are
depicted in Figure 3 specifically addressed to support LV network O&M (more information in [2]). The
implementation relies on the LV visibility and control enhancements described in [1]. Finally, but not the
least, the demonstrator has also developed a Consumer capacity building web-based tool aimed at
rationally managing energy consumption. This work is summarised in [3].
FIGURE 3: SUMMARY OF DEMONSTRATOR’S MAIN DEPLOYED CAPABILITIES TO FACILITATE AN ADVANCED LV
MONITORING AND OPERATION
All this work exploits four of the most relevant UPGRID Function Objectives [4]: “Monitoring and control
of LV network”, “Smart metering data utilization”, “Network management methodologies for network
Tracing feautures Comprehensive event
management Outage management -
Service restoration Smart meter querying
Planned jobs management
LV infrastructure remote control
(PRIME)
Access and use of historic
information
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operations” and “Novel approaches to asset management”. In fact, [4] has been used as a solid
reference for establishing the demonstrator scope and boundaries. Table 2 (page 29) presents the cross-
match between the demonstrator sub-functionalities and the main lines of work developed in the
demonstrator.
The content of this deliverable showcases the benefits of the Spanish demonstrator which are
highlighted in a qualitative way but also supported by operational field results. Moreover, the main
opportunities identified along the demonstrator were gathered as well as those that are bound to pave
the way for future developments.
The LV NMS, both Desktop and Mobile solutions, have entered in operation in the demonstrator area.
This has allowed to test functionalities in a bigger scale than during the development phase [1][2].
Successful outcomes have been achieved. The Field Crews have benefitted from this solution (see Figure
4) providing them with a real time view of LV network diagram to investigate LV network incidents; a
way to confirm that the supply restoration has been achieved; allowing them to update the network
topology when carrying out LV fuses switching, as well as temporary operations (i.e. cuts and jumpers).
Improvements were identified, based on the field experience, and are already being included in a
system specification (intended to be fully deployed for the entire Iberdrola LV network), that is under
elaboration, and which relies on the UPGRID acquired knowledge.
FIGURE 4: IBERDROLA FIELD CREW USING THE LV NMS MOBILE SOLUTION
PRIME functionalities results, after field testing, also have confirmed the expected results advanced by
the laboratory tests [2]. An architecture that allows multiple applications (i.e. smart meter management
and remote control) on top of a PRIME subnetwork has been designed, developed, validated and
deployed in the field in the scope of the UPGRID Spanish demonstrator project. Regarding this
functionality, the deliverable describes the tests performed, the results of the field deployment (see
Figure 5) and its main benefits, and applicability based on the experience obtained. Additionally,
introducing these services requires a better knowledge and controllability of the PRIME subnetwork. The
field deployment done in the line of obtaining a manageable PRIME subnetwork through SNMP protocol
is also described.
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The structured analyses of smart meters events have allowed to identify useful conclusions to be
applied for maintenance purposes (e.g. identify and solve voltage magnitude issues and inventory data
base inconsistencies). Moreover, the opportunity arisen of the new developed software tool that
performs a more detailed smart meter voltage profile analyses is considered quite to be interesting.
FIGURE 5: INSTALLATION OF PRIME GATEWAY DEVICES AT FIELD (LEFT). WEB TOOL FOR MONITORING PRIME
SUBNETWORK (RIGHT)
A special mention must be made about a group of innovative components included in [2]. These
components take the opportunity of evaluating the application of algorithmic and artificial intelligence
techniques to the new set of data that delivered by the smart grids. This can provide new services or
improve those already existing. With this aim, a reference implementation of several components has
been developed with the main objective of checking and comparing their performance as
complementary services or with respect to other grid O&M processes. These components are basically
the Overload forecasting system ([2]), Medium Voltage (MV) State Estimation ([5],[2]), Simultaneity
Factor Estimation ([6] and [2]), Demand Response Simulator ([6] and [2]), Enhanced Outage
Management ([5] and [2]) and Load and Generation Forecasting ([5] and [2]).
The electricity distribution LV grid involved in the Spanish demonstrator covers approximately 2.150
secondary substations (SSs) and 400.000 Consumers. Geographically, it is located in Bilbao and part of
its surroundings (North of Spain). During the demonstrator implementation, the area was extended up
to approximately twice the initial size [4], highlighting the scalability of the network modelling process.
The UPGRID Spanish demonstrator is built on top of the Bidelek Sareak project (http://bidelek.com), a
Bask-Government supported initiative.
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TABLE OF CONTENTS
EXECUTIVE SUMMARY _________________________________________________________________ 4
TABLE OF CONTENTS __________________________________________________________________ 8
LIST OF FIGURES _____________________________________________________________________ 12
LIST OF TABLES ______________________________________________________________________ 19
LIST OF EQUATIONS __________________________________________________________________ 20
ABBREVIATIONS AND ACRONYMS ______________________________________________________ 21
1. INTRODUCTION ___________________________________________________________________ 24
1.1 DEMONSTRATOR OBJECTIVES ________________________________________________________________ 25
1.2 DEMONSTRATOR CONTRIBUTIONS____________________________________________________________ 27
1.3 DESCRIPTION OF THE DEMONSTRATOR LOCATION ______________________________________________ 32
2. DEMONSTRATOR BENEFITS AND OPPORTUNITIES OVERVIEW: PRESENT AND NEAR FUTURE _____ 36
3. LV GRID OBSERVABILITY AND OPERATION IMPROVEMENT ________________________________ 41
3.1 INTRODUCTION____________________________________________________________________________ 41
3.2 EVALUATION ______________________________________________________________________________ 46
3.2.1 SOUND LV NETWORK DIAGRAM GENERATION ___________________________________________________________ 47
3.2.2 LV DIAGRAM MAINTENANCE __________________________________________________________________________ 53
3.2.3 LV NMS INTEGRATION WITH EXISTING SYSTEMS: INTERFACES _____________________________________________ 57
3.2.4 LV INCIDENT MANAGEMENT: LV O&M __________________________________________________________________ 64
3.3 OPPORTUNITIES ___________________________________________________________________________ 66
3.4 CONCLUSIONS_____________________________________________________________________________ 67
4. USABILITY OF LV SMART METERING PRIME TECHNOLOGY FOR REMOTE CONTROL _____________ 69
4.1 INTRODUCTION____________________________________________________________________________ 69
4.2 MULTISERVICE PRIME SUBNETWORK (LV REMOTE CONTROL OVER LV SMART METERING PRIME
TECHNOLOGY): EVALUATION AND CONCLUSIONS __________________________________________________ 72
4.2.1 LV CONTROL TRAFFIC OVER PLC PRIME - FIELD VALIDATION CONDITIONS ___________________________________ 73
4.2.2 UPGRID CABINET FOR LV REMOTE CONTROL - FIELD DEPLOYMENTS________________________________________ 73
4.2.3 USE CASE 1 : SS WITH EXISTING RTU - RESULTS, PERFORMANCE AND TOOLS _________________________________ 75
4.2.4 USE CASE 2 : SS WITHOUT REMOTE ACCESS - RESULTS, PERFORMANCE AND TOOLS __________________________ 78
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4.2.5 USE CASE 3: LV BACKUP FEEDER SMART-SWITCH APPLICATION - LIMITATION FOUND IN THE DEMONSTRATOR
AREA ____________________________________________________________________________________________________ 80
4.2.6 APPLICABILITY AND NEW OPPORTUNITIES_______________________________________________________________ 82
4.3 MANAGEABLE PRIME SUBNETWORK (SNMP MONITORING): EVALUATION AND CONCLUSIONS _________ 83
4.3.1 REQUIREMENTS FOR A MANAGEABLE PRIME SUBNETWORK ______________________________________________ 83
4.3.2 FIELD VALIDATION CONDITIONS - RESULTS, PERFORMANCE AND TOOLS ____________________________________ 84
4.3.3 TEST RESULTS AND PERFORMANCE_____________________________________________________________________ 88
4.3.4 APPLICABILITY AND NEW OPPORTUNITIES_______________________________________________________________ 88
4.4 CONCLUSIONS ABOUT PRIME BASED FUNCTIONALITIES __________________________________________ 90
5. LV NETWORK OBSERVATION AND MAINTENANCE BASED ON SMART METER EVENT PROCESSING
AND ANALYSIS ______________________________________________________________________ 92
5.1 INTRODUCTION____________________________________________________________________________ 92
5.2 EVALUATION AND CONCLUSION______________________________________________________________ 93
5.2.1 MOST CONVENIENT TYPE OF SMART METER EVENTS FOR ENHANCING LV MAINTENANCE AND OTHER PRACTICAL
PROCEDURE DETAILS ______________________________________________________________________________________ 93
5.2.2 SELECTION OF MAIN ANALYSIS FUNCTIONALITIES ________________________________________________________ 94
5.2.3 DETECTION OF MISSED EVENTS ________________________________________________________________________ 96
5.2.4 FIELD APPLICABILITY OF SMART METER EVENT ANALYSIS OUTCOMES ______________________________________ 97
5.2.5 DETAILED ANALYSIS OF VOLTAGE MAGNITUDES ISSUES AT SUPPLY POINTS: VIRTUAL REGISTER _______________ 99
5.2.6 SUPERVISION METERS MEASUREMENTS TO COMPLEMENT THE EVENT ANALYSIS ___________________________ 102
5.2.7 REFINEMENT OF SUPERVISION METERS INVENTORY: INCONSISTENCIES DETECTION_________________________ 102
5.2.8 CONVENIENCE OF INTERACTIVE RESULT REPRESENTATION _______________________________________________ 104
5.3 OPPORTUNITIES __________________________________________________________________________ 106
6. CONSUMER EMPOWERMENT TOOL __________________________________________________ 107
6.1 INTRODUCTION___________________________________________________________________________ 107
6.2 EVALUATION _____________________________________________________________________________ 107
6.2.1 METERING DATA GATHERING: TECHNICAL SOLUTION____________________________________________________ 107
6.2.2 WEB TOOL FUNCTIONALITIES _________________________________________________________________________ 108
6.2.3 SOCIETAL RESEARCH _________________________________________________________________________________ 118
6.3 CONCLUSIONS AND OPPORTUNITIES _________________________________________________________ 119
7. ADDITIONAL LV OPERATION OPPORTUNITIES: INNOVATIVE SOFTWARE-BASED COMPONENTS __ 121
7.1 IMPROVING OVERLOAD FORECASTING _______________________________________________________ 121
7.1.1 OBJECTIVE__________________________________________________________________________________________ 121
7.1.2 EVALUATION _______________________________________________________________________________________ 122
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7.1.3 CONCLUSIONS AND FUTURE OPPORTUNITIES___________________________________________________________ 122
7.2 IMPROVING MV STATE ESTIMATION _________________________________________________________ 123
7.2.1 OBJECTIVE__________________________________________________________________________________________ 123
7.2.2 EVALUATION _______________________________________________________________________________________ 123
7.2.3 CONCLUSIONS AND FUTURE OPPORTUNITIES___________________________________________________________ 124
7.3 IMPROVING DSO DECISIONS BASED ON DEMAND SIDE ESTIMATION_______________________________ 124
7.3.1 OBJECTIVE__________________________________________________________________________________________ 124
7.3.2 EVALUATION _______________________________________________________________________________________ 124
7.3.3 CONCLUSIONS AND FUTURE OPPORTUNITIES___________________________________________________________ 125
7.4 IMPROVING LOAD DISTRIBUTION BASED ON THE SIMULTANEITY FACTOR ESTIMATION _______________ 126
7.4.1 OBJECTIVE__________________________________________________________________________________________ 126
7.4.2 EVALUATION _______________________________________________________________________________________ 126
7.4.3 CONCLUSIONS AND FUTURE OPPORTUNITIES___________________________________________________________ 126
7.5 ENHANCING THE OUTAGE MANAGEMENT AND THE SUPPORT FOR THE MAINTENANCE CREWS
COMPONENT OBJECTIVE ______________________________________________________________________ 127
7.5.1 OBJECTIVE__________________________________________________________________________________________ 127
7.5.2 EVALUATION _______________________________________________________________________________________ 128
7.5.3 CONCLUSIONS AND FUTURE OPPORTUNITIES___________________________________________________________ 128
7.6 LOAD AND GENERATION FORECASTING IN SECONDARY SUBSTATION ______________________________ 128
7.6.1 OBJECTIVE__________________________________________________________________________________________ 128
7.6.2 EVALUATION _______________________________________________________________________________________ 129
7.6.3 CONCLUSIONS AND FUTURE OPPORTUNITIES___________________________________________________________ 129
8. BUSINESS PROCESSES IMPACT ______________________________________________________ 130
8.1 LV OPERATION AND MAINTENANCE__________________________________________________________ 130
8.2 PRIME MULTISERVICE _____________________________________________________________________ 132
8.3 PRIME MANAGEABLE: REAL TIME AMI FAULTS DETECTION_______________________________________ 133
9. CONCLUSIONS ___________________________________________________________________ 134
REFERENCES _______________________________________________________________________ 138
LV NMS MONITORING INFORMATION DISPLAYED_________________________________ 140 ANNEX I.
MULTISERVICE PRIME SUBNETWORK: USE CASE 1 FIELD DEPLOYMENT _______________ 145 ANNEX II.
ANNEX II.1 UPGRID CABINET MODEL 1___________________________________________________________ 145
ANNEX II.2 SIMULTANEOUS AMI AND IP OVER PRIME TRAFFIC_______________________________________ 145
ANNEX II.3 REMOTE CONTROL TRAFFIC OVER PLC PRIME ___________________________________________ 150
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MULTISERVICE PRIME SUBNETWORK: USE CASE 2 FIELD DEPLOYMENT ______________ 156 ANNEX III.
ANNEX III.1 UPGRID CABINET MODEL 2 __________________________________________________________ 156
ANNEX III.2 IP OVER PRIME AS AN ALTERNATIVE TO A SS WITHOUT REMOTE ACCESS ____________________ 156
MANAGEABLE PRIME SUBNETWORK (SNMP MONITORING) FIELD DEPLOYMENT RESULTS162 ANNEX IV.
MANAGEABLE PRIME SUBNETWORK (SNMP MONITORING) DETAILED EXAMPLE – SS ANNEX V.
200000750 ________________________________________________________________________ 181
SMART METER ANALYSIS AND PROCESSING: MACROS TOOLS ______________________ 184 ANNEX VI.
SMART METER ANALYSIS AND PROCESSING: VIRTUAL REGISTER RESULTS____________ 185 ANNEX VII.
ANNEX VII.1 MEASUREMENTS FROM SS_1 (UNDERVOLTAGE) _______________________________________ 185
ANNEX VII.2 MEASUREMENTS FROM SS_2 (UNDERVOLTAGE) _______________________________________ 188
ANNEX VII.3 MEASUREMENTS FROM SS_3 (UNDERVOLTAGE) _______________________________________ 189
ANNEX VII.4 MEASUREMENTS FROM SS_4 (UNDERVOLTAGE) _______________________________________ 191
ANNEX VII.5 MEASUREMENTS FROM SS_5 (UNDERVOLTAGE) _______________________________________ 194
ANNEX VII.6 MEASUREMENTS FROM SS_6 (OVERVOLTAGE) _________________________________________ 195
ANNEX VII.7 MEASUREMENTS FROM SS_7 (OVERVOLTAGE) _________________________________________ 198
ANNEX VII.8 MEASUREMENTS FROM SS_8 (OVERVOLTAGE) _________________________________________ 199
ANNEX VII.9 MEASUREMENTS FROM SS_9 (OVERVOLTAGE) _________________________________________ 200
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LIST OF FIGURES
FIGURE 1: SPANISH DEMONSTRATOR LINES OF WORK ________________________________________ 4
FIGURE 2: GENERAL VIEW (NOT ZOOMED) OF PART OF THE DEMONSTRATOR LV NETWORK MODEL ___ 5
FIGURE 3: SUMMARY OF DEMONSTRATOR’S MAIN DEPLOYED CAPABILITIES TO FACILITATE AN
ADVANCED LV MONITORING AND OPERATION ______________________________________________ 5
FIGURE 4: IBERDROLA FIELD CREW USING THE LV NMS MOBILE SOLUTION _______________________ 6
FIGURE 5: INSTALLATION OF PRIME GATEWAY DEVICES AT FIELD (LEFT). WEB TOOL FOR MONITORING
PRIME SUBNETWORK (RIGHT) ___________________________________________________________ 7
FIGURE 6: THE FOUR KEYSTONES OF THE SPANISH DEMONSTRATOR ___________________________ 26
FIGURE 7: SPANISH DEMONSTRATOR LINES OF WORK THAT COVER THE SPANISH DEMONSTRATOR
OBJECTIVES _________________________________________________________________________ 27
FIGURE 8: LOCATION OF THE SPANISH DEMONSTRATION: BILBAO (VIZCAYA, BASQUE COUNTRY)_____ 32
FIGURE 9: GEOGRAPHIC AREA COVERED BY THE DISTRIBUTION NETWORK OF THE SPANISH
DEMONSTRATOR (DELIMITED BY THE RED AND BLUE LINES). IN RED, THE AREA DEFINED AT THE
BEGINNING OF THE DEMONSTRATOR – BILBAO (URBAN). IN BLUE, EXTENSION OF THE DEMONSTRATOR
AREA - BARACALDO (SEMI-URBAN) ______________________________________________________ 33
FIGURE 10: THREE DIFFERENT TYPES OF LV SMART METERS DEVICES USED IN THE DEMONSTRATOR __ 34
FIGURE 11: GENERAL VIEW (NOT ZOOMED) OF PART OF THE DEMONSTRATOR AREA (LV NETWORK
MODEL) ____________________________________________________________________________ 41
FIGURE 12: COMPARISON OF NETWORK ELEMENTS BETWEEN THE GIS (LEFT) AND THE LV NMS
NETWORK (RIGHT) ___________________________________________________________________ 42
FIGURE 13: EXAMPLE OF A SS MODEL VISUALISATION IN THE MV SCADA ________________________ 42
FIGURE 14: SIMPLIFIED DIAGRAM SHOWING MAIN INTERFACES BETWEEN SYSTEMS _______________ 43
FIGURE 15: LV NMS SOLUTIONS DEPLOYED IN THE UPGRID SPANISH DEMONSTRATOR: DESKTOP AND
MOBILE ____________________________________________________________________________ 44
FIGURE 16: EXCEL SHEET EXTRACT WHERE LV INCIDENT INFORMATION HAS BEEN RECORDED FOR BEING
ANALYSED FOR EVALUATION PURPOSES __________________________________________________ 47
FIGURE 17: EXAMPLE OF A SS INTERIOR SCHEMATIC SUCCESSFULLY GENERATED. VALIDATION BETWEEN
GIS INFORMATION (ABOVE) AND LV NMS NETWORK MODEL (BELOW) __________________________ 49
FIGURE 18: EXAMPLE OF LINE ASSIGNED TO AN INCORRECT LV SWITCHBOARD. GIS INFORMATION
(ABOVE). LV DIAGRAM GENERATED FROM GIS DATA (BELOW) ________________________________ 50
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FIGURE 19: EXAMPLE OF MISSING LV FEEDER AFTER THE DATA IMPORTING______________________ 51
FIGURE 20: RELATED CUSTOMER CALLS FOR AN OUTAGE ____________________________________ 54
FIGURE 21: OUTAGE HISTORY FOR AUDITING PURPOSES _____________________________________ 54
FIGURE 22: SWITCHING LOG DETAILING THE TEMPORARY OPERATIONS TO RESTORE AN INCIDENT ___ 55
FIGURE 23: THE SAME TEMPORAL ELEMENTS SHOWN IN THE MOBILE SOLUTION _________________ 55
FIGURE 24: FUSE OPERATED ON TABLET. THE FUSE ON THE RIGHT HAS BEEN REMOVED ____________ 56
FIGURE 25: VISUALISATION ON DESKTOP DIAGRAM OF A REMOVED LV FUSE (THE ELEMENT NOT
COLOURED ON THE RIGHT HAND SIDE) AND THE LV CIRCUIT DE-ENERGISED (IN WHITE). A TOOLTIP
DISPLAYS THE FUSE STATUS PER PHASE. __________________________________________________ 56
FIGURE 26: SS MV MEASUREMENT ON THE SCADA__________________________________________ 58
FIGURE 27: SS MV MEASUREMENT ON THE SAME SS OF FIGURE 30 REPRESENTED ON THE LV NMS ___ 59
FIGURE 28: MEASUREMENT REPORT ON SS SUPERVISION METERS EXTRACTED FROM THE MDMS ____ 59
FIGURE 29: SS SUPERVISION METER MEASUREMENTS (ON DEMAND) SHOWN IN THE LV NMS LV
NETWORK DIAGRAM THROUGH THE LV NMS-OMS-AMI INTERFACE ____________________________ 60
FIGURE 30: CONSUMER CALL DISPLAYED ON THE DIAGRAM ALONG WITH INCIDENT NEAR THE FUSE BOX
SYMBOL____________________________________________________________________________ 61
FIGURE 31: CONSUMER SMART METER EVENT DISPLAYED ON THE DIAGRAM AS A PSEUDO CALL NEAR
THE FUSE BOX SYMBOL _______________________________________________________________ 61
FIGURE 32: GEOSPATIAL ANALYSIS TOOL (GSA) REPRESENTATION EXAMPLE. LEGEND: SS = STARS, FUSE
BOXES = CIRCLES WHICH SIZE DEPEND ON THE NUMBER OF SMART METER EVENTS, LV FEEDERS = GREY
SEGMENTS) _________________________________________________________________________ 63
FIGURE 33: GEOSPATIAL ANALYSIS TOOL (GSA) REPRESENTATION EXAMPLE. SS’S ARE CIRCLES WHICH
SIZE DEPEND ON THE NUMBER OF LV NMS INCIDENTS AND GRAPHS WITH INCIDENT CATEGORIES
CLASSIFICATIONS ____________________________________________________________________ 63
FIGURE 34: SS NETWORK ARCHITECTURE WITH AN EXISTING RTU (USE CASE 1). INITIAL SCENARIO (ON
THE LEFT). SCENARIO THAT INCLUDES GTPS TO TEST REMOTE CONTROL TRAFFIC OVER PRIME (ON THE
RIGHT). CCT = DATA CONCENTRATOR, IBD = IBERDROLA _____________________________________ 70
FIGURE 35: PRIME GTPS INSTALLED IN A SS ________________________________________________ 70
FIGURE 36: SCREENSHOT OF THE SNMP WEB TOOL INTERFACE - DATA ACCESS SHOWING NODES
CONNECTED IN THE DEMONSTRATION AREA. NUMBER OF TERMINALS (IN GREEN). NUMBER OF
SWITCHES (IN BLUE) __________________________________________________________________ 71
FIGURE 37: SCREENSHOT OF THE SNMP WEB TOOL INTERFACE – CONFIGURATION MENU __________ 72
FIGURE 38: PORTABLE CABINET TYPE 1 INSTALLED IN FIELD DEPLOYMENT _______________________ 74
FIGURE 39: PORTABLE CABINET TYPE 2 INSTALLED IN FIELD DEPLOYMENT _______________________ 74
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FIGURE 40: BASIC UPGRID ARCHITECTURE OF REMOTE CONTROL OVER PLC PRIME TESTING ________ 75
FIGURE 41: UPGRID INSTALLATION AT TORRE ABANDOIBARRA 2 SS ____________________________ 76
FIGURE 42: IMAGES OF THE REMOTE CONTROL TRAFFIC TEST PERFORMED AT TORRE ABANDOIBARRA 2
SS_________________________________________________________________________________ 76
FIGURE 43: ARCHITECTURE FOR USE CASE 2 SS WITHOUT REMOTE ACCESS DEPLOYMENT (CCT = DATA
CONCENTRATOR, GTP = PRIME GATEWAY, METER = SMART METER) ___________________________ 78
FIGURE 44: USE CASE 2 TEST ENABLING WAN ACCESS FROM THE METER ROOM OF GERNIKAKO
LORATEGIA 3 ________________________________________________________________________ 79
FIGURE 45: LV GRID TOPOLOGY WITH BACKUP FEEDER REQUIRED (FB = FUSE BOX, SS = SECONDARY
SUBSTATION) _______________________________________________________________________ 81
FIGURE 46: LV GRID TOPOLOGY AVAILABLE IN THE DEMONSTRATOR AREA (FB = FUSE BOX, SS =
SECONDARY SUBSTATION) _____________________________________________________________ 81
FIGURE 47: FIELD VISITS TO VALENTIN DE BERRIOTXOA SS WHERE USE CASE 3 DEPLOYMENTS IN THE
FIELD WAS DISCARDED ________________________________________________________________ 81
FIGURE 48: FIELD SS MONITORED SNMP WEB TOOL (CCT = DATA CONCENTRATOR, IBD = IBERDROLA, FW
= FIRMWARE, SS = SECONDARY SUBSTATION)______________________________________________ 84
FIGURE 49: SCREENSHOT OF THE SNMP WEB TOOL PROVISIONING INTERFACE ___________________ 85
FIGURE 50: SCREENSHOT OF THE SNMP WEB TOOL PROVISIONING INTERFACE II __________________ 85
FIGURE 51: SCREENSHOT OF THE SNMP WEB TOOL SCHEDULER INTERFACE ______________________ 86
FIGURE 52: SCREENSHOT SHOWING ADDED TASKS __________________________________________ 86
FIGURE 53: SCREENSHOT OF RECOLLECTED DATA FROM A REAL DATA CONCENTRATOR. NUMBER OF
TERMINALS (IN GREEN). NUMBER OF SWITCHES (IN BLUE) ___________________________________ 87
FIGURE 54: SCREENSHOT OF RECOLLECTED DATA FROM A REAL DATA CONCENTRATOR WITH NOISE
ISSUES. NUMBER OF TERMINALS (IN GREEN). NUMBER OF SWITCHES (IN BLUE) __________________ 87
FIGURE 55: OFFLINE SMART METER EVENT ANALYSIS WITHIN AMI ARCHITECTURE ________________ 92
FIGURE 56: EVENTS ANALYSIS FLOW CHART _______________________________________________ 93
FIGURE 57: FIELD ANALYSIS OF OVERVOLTAGE EVENTS ______________________________________ 98
FIGURE 58: VOLTAGE CURVE ELABORATED BY THE VIRTUAL REGISTER FOR A PARTICULAR SUPPLY POINT
(SMART METER) AND SHOWN THROUGH THE TOOL GUI ____________________________________ 100
FIGURE 59: MEASUREMENTS FROM FB_1 (PART 1)_________________________________________ 101
FIGURE 60: OVERVOLTAGE REPORT RESULTS AT SUPERVISORY METER _________________________ 103
FIGURE 61: EXTRACT OF THE RESULTS OBTAINED AFTER EXECUTING THE VBA MACRO TOOL _______ 103
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FIGURE 62: BAR CHART [NUMBER OF UNDERVOLTAGE EVENTS PER WEEK IN VIZCAYA (2016) ON BASIS
OF ITS DURATION] __________________________________________________________________ 105
FIGURE 63: MAP [NUMBER OF UNDERVOLTAGE EVENTS PER WEEK IN VIZCAYA (2016) ON BASIS OF ITS
DURATION] ________________________________________________________________________ 105
FIGURE 64: TABLE [NUMBER OF UNDERVOLTAGE EVENTS PER WEEK IN VIZCAYA (2016) ON BASIS OF ITS
DURATION] ________________________________________________________________________ 106
FIGURE 65: COMPONENTS ADAPTED FOR THE SPANISH DEMONSTRATOR ______________________ 121
FIGURE 66: MV VOLTAGES (VIA ICCP INTERFACE) __________________________________________ 140
FIGURE 67: MV ENERGISATION STATUS (SS ENERGISED/DE-ENERGISED). IN THIS CASE THE UPPER “FAKE”
SWITCH IS OPENED AND ALL THE LV CIRCUITS DOWNWARDS ARE WITHOUT ENERGY SUPPLY (WHITE
COLOUR) __________________________________________________________________________ 140
FIGURE 68: DISTRIBUTION TRANSFORMER SUPERVISION METER EVENTS ARE DISPLAYED WITH A
FLASHING MARK (İEVENTO!) NEAR THE TRANSFORMER SYMBOL _____________________________ 141
FIGURE 69: CONSUMER SMART METER EVENTS ARE DISPLAYED AS A PSEUDO CONSUMER CALL ON
NEAR THE FB SYMBOL________________________________________________________________ 141
FIGURE 70: DISTRIBUTION TRANSFORMER SUPERVISION METER INSTANTANEOUS VALUES ARE
DISPLAYED NEAR THE TRANSFORMER SYMBOL ____________________________________________ 142
FIGURE 71: PENDING MAINTENANCE WORK INDICATION (“AO” TEXT) _________________________ 142
FIGURE 72: CONSUMER SMART METER INSTANTANEOUS VALUES AFTER AN ON DEMAND
MEASUREMENTS REQUEST ___________________________________________________________ 143
FIGURE 73: SCHEDULED WORK INDICATION (“SCHEDULE” TEXT) ______________________________ 143
FIGURE 74: CONSUMER SUPPLY POINT SYMBOL (FUSE BOX) _________________________________ 144
FIGURE 75: EXAMPLE OF AN INCIDENT REPORT PREPARED BY THE LV NMS REPORTING TOOLS______ 144
FIGURE 76: PORTABLE CABINET TYPE 1 TO BE USED FOR UPGRID TESTING ______________________ 145
FIGURE 77: SCREENSHOT FROM THE AMI DATA CONCENTRATOR BEFORE THE TESTS _____________ 145
FIGURE 78: METERS REGISTERED TO THE UPGRID GTP ACTING AS BASE NODE (MASTER) __________ 147
FIGURE 79: ZIV PRIME MANAGER TOOL USED FOR GTP PRIME PLC DATA ANALYSIS _______________ 147
FIGURE 80: IMAGES OF THE REMOTE CONTROL TRAFFIC TEST PERFORMED AT TORRE ABANDOIBARRA 2
SS________________________________________________________________________________ 150
FIGURE 81: INITIAL SETUP AT TORRE ABANDOIBARRA 2 SS BEFORE THE TESTING _________________ 151
FIGURE 82: REMOTE CONTROL TRAFFIC TEST SETUP AT TORRE ABANDOIBARRA 2 SS _____________ 151
FIGURE 83: FINAL UPGRID FIRMWARE VERSION FOR MULTISERVICE CAPABILITIES OVER GTP _______ 152
FIGURE 84: UPGRID MASTER GTP CONFIGURATION, INTEGRATED INTO THE PORTABLE CABINET ____ 152
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FIGURE 85: REMOTE WEB CAPTURE OF THE SS UNDER TEST _________________________________ 154
FIGURE 86: IBERDROLA SPECTRUM CONFIGURATION CHANGE TO ALLOW LOCAL SCADA ACCESS ____ 154
FIGURE 87: RTU SIMULATED IN A PC OVER WINPCPAW, INSTALLED ALSO AT THE SS UNDER TEST____ 154
FIGURE 88: WIRESHARK CAPTURE OF 104 CONTROL TRAFFIC OVER IP OVER PLC PRIME ___________ 155
FIGURE 89: PORTABLE CABINET TYPE 2 TO BE USED FOR UPGRID TESTING ______________________ 156
FIGURE 90: SCREENSHOT FROM THE AMI DATA CONCENTRATOR BEFORE THE TESTS _____________ 157
FIGURE 91: USE CASE 2 TESTS SETUP AT MIRIBILLA 6 SS REPRESENTING A SS WITHOUT WAN COVERAGE
_________________________________________________________________________________ 157
FIGURE 92: SCREENSHOT FROM THE GTP IN THE METER ROOM (SLAVE) REGISTERED TO THE GTP IN THE
SS (MASTER) _______________________________________________________________________ 158
FIGURE 93: USE CASE 2 TEST PERFORMED AT MIRIBILLA 6 SS_________________________________ 159
FIGURE 94: USE CASE 2 TEST ENABLING WAN ACCESS FROM THE METER ROOM OF GERNIKAKO
LORATEGIA 3 _______________________________________________________________________ 159
FIGURE 95: CONNECTION PROCESS TO THE AMI DATA CONCENTRATOR FROM THE METER ROOM___ 160
FIGURE 96: SCREENSHOT FROM THE AMI OPERATION SYSTEM DURING THE GTP WAN ACCESS _____ 161
FIGURE 97: REAL DATA CONCENTRATOR A: TERMINALS AND SWITCHES ________________________ 163
FIGURE 98: REAL DATA CONCENTRATOR B: TERMINALS AND SWITCHES ________________________ 163
FIGURE 99: REAL DATA CONCENTRATOR C: TERMINALS AND SWITCHES ________________________ 164
FIGURE 100: REAL DATA CONCENTRATOR D: TERMINALS AND SWITCHES _______________________ 164
FIGURE 101: REAL DATA CONCENTRATOR E: TERMINALS AND SWITCHES _______________________ 165
FIGURE 102: REAL DATA CONCENTRATOR F: TERMINALS AND SWITCHES _______________________ 165
FIGURE 103: REAL DATA CONCENTRATOR G: TERMINALS AND SWITCHES _______________________ 166
FIGURE 104: REAL DATA CONCENTRATOR H: TERMINALS AND SWITCHES _______________________ 166
FIGURE 105: REAL DATA CONCENTRATOR J: TERMINALS AND SWITCHES _______________________ 167
FIGURE 106: REAL DATA CONCENTRATOR K: TERMINALS AND SWITCHES _______________________ 168
FIGURE 107: REAL DATA CONCENTRATOR L: TERMINALS AND SWITCHES _______________________ 168
FIGURE 108: REAL DATA CONCENTRATOR M: TERMINALS AND SWITCHES ______________________ 169
FIGURE 109: REAL DATA CONCENTRATOR N: TERMINALS AND SWITCHES _______________________ 169
FIGURE 110: REAL DATA CONCENTRATOR O: TERMINALS AND SWITCHES_______________________ 170
FIGURE 111: REAL DATA CONCENTRATOR P: TERMINALS AND SWITCHES _______________________ 170
FIGURE 112: REAL DATA CONCENTRATOR Q: TERMINALS AND SWITCHES ______________________ 171
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FIGURE 113: REAL DATA CONCENTRATOR R: TERMINALS AND SWITCHES _______________________ 171
FIGURE 114: REAL DATA CONCENTRATOR S: TERMINALS AND SWITCHES _______________________ 172
FIGURE 115: REAL DATA CONCENTRATOR T: TERMINALS AND SWITCHES _______________________ 172
FIGURE 116: REAL DATA CONCENTRATOR U: TERMINALS AND SWITCHES _______________________ 173
FIGURE 117: REAL DATA CONCENTRATOR V: TERMINALS AND SWITCHES _______________________ 173
FIGURE 118: REAL DATA CONCENTRATOR W: TERMINALS AND SWITCHES ______________________ 174
FIGURE 119: REAL DATA CONCENTRATOR X: TERMINALS AND SWITCHES _______________________ 174
FIGURE 120: REAL DATA CONCENTRATOR Y: TERMINALS AND SWITCHES _______________________ 175
FIGURE 121: REAL DATA CONCENTRATOR Z: TERMINALS AND SWITCHES _______________________ 175
FIGURE 122: REAL DATA CONCENTRATOR AA: TERMINALS AND SWITCHES______________________ 176
FIGURE 123: REAL DATA CONCENTRATOR AB: TERMINALS AND SWITCHES ______________________ 176
FIGURE 124: REAL DATA CONCENTRATOR AC: TERMINALS AND SWITCHES ______________________ 177
FIGURE 125: REAL DATA CONCENTRATOR AD: TERMINALS AND SWITCHES______________________ 177
FIGURE 126: REAL DATA CONCENTRATOR AE: TERMINALS AND SWITCHES ______________________ 178
FIGURE 127: REAL DATA CONCENTRATOR AF: TERMINALS AND SWITCHES ______________________ 178
FIGURE 128: REAL DATA CONCENTRATOR AG: TERMINALS AND SWITCHES______________________ 179
FIGURE 129: REAL DATA CONCENTRATOR AH: TERMINALS AND SWITCHES______________________ 179
FIGURE 130: LOCATION FOR THE SS AND THE DATA CONCENTRATOR __________________________ 181
FIGURE 131: THE TWO LV SWITCHBOARD OF THE SELECTED SS _______________________________ 181
FIGURE 132: DATA CONCENTRATOR ____________________________________________________ 182
FIGURE 133: SNMP CONFIGURATION IN DATA CONCENTRATOR ______________________________ 182
FIGURE 134: PROVISIONING OF THE NODE IN THE WEB TOOL ________________________________ 183
FIGURE 135: PROVISIONED NODE ______________________________________________________ 183
FIGURE 136: QUALITY OF PRIME NETWORK DATA STORED IN WEB TOOL AFTER 4 DAYS ___________ 183
FIGURE 137: MAIN SHEET OF ONE OF THE DEVELOPED MACROS. IT CONTAINS THE EXECUTION
CONFIGURATION PARAMETERS (LEFT) AND THE SUMMARY OF RESULTS (RIGHT)_________________ 184
FIGURE 138: EXTRACT OF THE EXCEL TABLE RESULTED FROM EXECUTING THE MACRO THAT ANALYSES
THE TIME OUT OF VOLTAGE LIMITS AT FB LEVEL___________________________________________ 184
FIGURE 139: MEASUREMENTS FROM FB_1 (PART 1)________________________________________ 186
FIGURE 140: MEASUREMENTS FROM FB_1 (PART 2)________________________________________ 187
FIGURE 141: MEASUREMENTS FROM FB_2 _______________________________________________ 188
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FIGURE 142: MEASUREMENTS FROM FB_3 _______________________________________________ 189
FIGURE 143: MEASUREMENTS FROM FB _4 (PART 1) _______________________________________ 190
FIGURE 144: MEASUREMENTS FROM FB_4 (PART 2)________________________________________ 191
FIGURE 145: MEASUREMENTS FROM FB_5 _______________________________________________ 192
FIGURE 146: MEASUREMENTS FROM FB_6 (PART 1)________________________________________ 193
FIGURE 147: MEASUREMENTS FROM FB_6 (PART 2)________________________________________ 193
FIGURE 148: MEASUREMENTS FROM FB_7 _______________________________________________ 194
FIGURE 149: MEASUREMENTS FROM FB_9 _______________________________________________ 194
FIGURE 150: MEASUREMENTS FROM FB_8 _______________________________________________ 195
FIGURE 151: MEASUREMENTS FROM FB_10 ______________________________________________ 195
FIGURE 152: MEASUREMENTS FROM FB_11 (PART 1)_______________________________________ 196
FIGURE 153: MEASUREMENTS FROM FB_11 (PART 2) ______________________________________ 197
FIGURE 154: MEASUREMENTS FROM FB_11 ______________________________________________ 197
FIGURE 155: MEASUREMENTS FROM FB_13 (PART 1)_______________________________________ 198
FIGURE 156: MEASUREMENTS FROM FB_13 (PART 2)_______________________________________ 199
FIGURE 157: MEASUREMENTS FROM FB_14 ______________________________________________ 200
FIGURE 158: MEASUREMENTS FROM FB_15 ______________________________________________ 201
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LIST OF TABLES
TABLE 1: UPGRID SPANISH DEMONSTRATOR PARTNERS _____________________________________ 24
TABLE 2: HIGH LEVEL CROSS-MATCHING OF THE SPANISH DEMONSTRATOR SUB-FUNCTIONALITIES [4]
AND ITS MAIN LINES OF WORK. IN GREEN, CASES WITH DIRECT MATCH; IN ORANGE, CASES WITH
PARTIAL MATCH) ____________________________________________________________________ 29
TABLE 3: NUMBER OF MAIN LV EQUIPMENT IN THE DEMONSTRATION AREA _____________________ 35
TABLE 4: INTERFACE SUMMARY INVOLVED IN THE LV NMS INTEGRATION _______________________ 43
TABLE 5: CAPABILITIES FOR ADVANCED LV NETWORK MONITORING AND OPERATION FROM THE
OPERATOR PERSPECTIVE PROVIDED BY THE LV NMS DEPLOYED IN THE UPGRID SPANISH
DEMONSTRATOR (MORE DETAIL IN [2])___________________________________________________ 45
TABLE 6: DATA IMPORTING RESULTS AFTER THE FIRST LOAD __________________________________ 49
TABLE 7: DATA IMPORTING RESULTS _____________________________________________________ 50
TABLE 8: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE
SOUND LV NETWORK DIAGRAM GENERATION _____________________________________________ 52
TABLE 9: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE
LV DIAGRAM MAINTENANCE ___________________________________________________________ 57
TABLE 10: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE
LV NMS INTEGRATION WITH EXISTING SYSTEMS____________________________________________ 64
TABLE 11: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE
LV INCIDENT MANAGEMENT ___________________________________________________________ 66
TABLE 12: USE CASES FOR FIELD TESTING _________________________________________________ 69
TABLE 13: TYPE OF SMART METER EVENTS FOR LV NETWORK MAINTENANCE ENHANCEMENT _______ 94
TABLE 14: MAIN ANALYSIS TOOL DEVELOPED IN THE DEMONSTRATOR FOR EVENT ANALYSIS ________ 95
TABLE 15: EXAMPLE OF GRAPHICAL PRESENTATION OF RESULTS AFTER EXECUTING THE MACRO TOOL
(LEFT). SUMMARY OF RESULTS OBTAINED AFTER A SUITABLE INCIDENT HAPPENED ON 12/02/2016
THAT AFFECTED 14 SSS (RIGHT). ________________________________________________________ 96
TABLE 16: WORST CASES OF UNDERVOLTAGE EVENTS (AT FB BASE) DETECTED IN THE VIZCAYA AREA _ 97
TABLE 17: WORST CASES OF OVERVOLTAGE EVENTS (AT FB BASE) DETECTED IN THE VIZCAYA AREA __ 98
TABLE 18: SS_1 - METERS FROM WORST FB UNDERVOLTAGE ________________________________ 100
TABLE 19: NUMBER OF SUPERVISION METERS POTENTIAL WRONG LABELLED ___________________ 103
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TABLE 20: SUMMARY OF THE MAIN TEST PERFORMED FOR EVALUATING THE TECHNICAL WEB TOOL
PERFORMANCE _____________________________________________________________________ 109
TABLE 21: PERFORMANCE TEST PASS REPORT_____________________________________________ 111
TABLE 22: LIST OF SS INVOLVED IN THE FIELD TEST OF THE MANAGEABLE PRIME SUBNETWORK ____ 162
TABLE 23: SS_1 - METERS FROM WORST FB UNDERVOLTAGE ________________________________ 185
TABLE 24: SS_2 SMART METERS FROM WORST FB (UNDERVOLTAGE) __________________________ 188
TABLE 25: SS_3 - METERS FROM WORST FB (UNDERVOLTAGE) _______________________________ 189
TABLE 26: SS_4 METERS FROM WORST FB (UNDERVOLTAGE) ________________________________ 191
TABLE 27: SS_5 - METERS FROM WORST FB (UNDERVOLTAGE) _______________________________ 194
TABLE 28: SS_6 SS - METERS FROM WORST FB (OVERVOLTAGE) ______________________________ 195
TABLE 29 SS_7 SS - METERS FROM WORST FB (OVERVOLTAGE) _______________________________ 198
TABLE 30 SS_8 - METERS FROM WORST FB (OVERVOLTAGE) _________________________________ 199
TABLE 31 SS_9- METERS FROM WORST FB (OVERVOLTAGE)__________________________________ 200
LIST OF EQUATIONS
EQUATION 1: SIMULTANEITY FACTOR....................................................................................................... 126
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ABBREVIATIONS AND ACRONYMS
ADMS Advanced Distribution Management System
AMI Advanced Metering Infrastructure
CCT Data Concentrator
CUPS Universal Supply Point Codes
Código Universal de Punto de Suministro (in Spanish)
D Deliverable
DG Distributed Generation
DLMS Distribution Line Message Specification
DMS Distribution Management System
DPF Distribution Power Flow
DSO Distribution System Operator
DTU Distribution Territorial Unit
EV Electric Vehicle
EVE Ente Vasco de la Energía
FB Fuse Box
FTP File Transfer Protocol
FW Firmware
GE General Electric
GIS Geographic Information System
GPRS/3G General Packet Radio Service / Third Generation
GSA Geospatial analysis tool
GTP PRIME Gateway
GUI Graphical User Interface
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HIS Historical Information System
HTTP Hypertext Transfer Protocol
ICMP Internet Control Message Protocol
IEC International Electrotechnical Commission
IP Internet Protocol
IPv4 Internet Protocol version 4
KPI Key Performance Indicator
LV Low Voltage
LV NMS Low Voltage Network Management System
MDMS Meter Data Management System
MIB Management Information Base
MV Medium Voltage
NMS Network Management System
O&M Operation and Maintenance
OFS Overload Forecasting System
OID Object Identification
OMS Outage Management System
PBN PRIME Base Node
PLC Power Line Communication
PRIME PoweRline Intelligent Metering Evolution
QoS Quality of Supply
RTD Research through design
RTU Remote Terminal Unit
SAIDI System Average Interruption Duration Index
SAIFI System Average Interruption Frequency Index
SCADA Supervisory Control And Data Acquisition
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SM Smart Meter
SME Small and Medium Enterprises
SNMP Simple Network Management Protocol
SNR Signal to noise disturbances
SS Secondary substation
SW Software
TDU Territorial Distribution Unit
ToC Table of Contents
VBA Visual Basic for Applications
VLAN Virtual Local Area Network
WAN Wide Area Network
WMS Work Orders Management System
WP Work Package
XML eXtensible Markup Language
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1. INTRODUCTION
The UPGRID Spanish demonstrator started in April 2015. It has been led by Iberdrola Distribución
Eléctrica with the support of GE, ZIV, Tecnalia and EVE, see Table 1. The demonstrator has been
conducted in two main phases. The first one included the design, development, deployment and test
(from a technical point of view) of all demonstrator concepts. The second phase, reported in the present
deliverable, deals with the entrance into operation on the demonstrator solutions and the impacts on
current Distribution System Operator (DSO) processes.
The Spanish demonstrator has been built on top of Bidelek Sareak project taking advantage mainly of
the experience of using an earlier version of the LV NMS, the smart metering infrastructure and the
distribution grid modernization deployed within this project.
TABLE 1: UPGRID SPANISH DEMONSTRATOR PARTNERS
Iberdrola Distribución Eléctrica has been the DSO responsible of the Spanish demonstrator and it has collaborated in all developments and tests. Its distribution network around Bilbao has been used for testing the deployed concepts1.
General Electric (GE) has collaborated on developing and testing the following concepts: sound LV network modelling, LV NMS (desktop and mobile solutions) and interfaces for LV NMS integration.
ZIV has collaborated on developing and testing PLC PRIME functionalities. Moreover, ZIV is one of the equipment providers (e.g. smart meter and supervision solution in SSs) which are installed in the demonstration area.
Tecnalia has collaborated on smart meter event processing and analysis, and facilitated the integration of some innovation components. Tecnalia has also coordinated local Consumer workshops in the demonstration area.
Ente Vasco de la Energía (EVE) has been in charge of developing and testing the Consumer capacity building web-based tool.
The Spanish demonstrator has four deliverables (D), each document associated with one specific task:
Task 3.1_D3.1 – Tools suit for the Advanced Real Time LV network representation [1]
Task 3.2_D3.2 – Tools suite for the smart control and operation of the LV Grid [2] Task 3.3_D3.3 – Consumer Capacity building web-based system [3]
Task 3.4_D3.4 – Demonstration results: Evaluation and opportunities (present document)
The main outputs of Task 3.1, Task 3.2 and Task 3.3 are a set of tools and developments. This implies
that D3.1, D3.2 and D3.3 summarise what has been done in the associated tasks providing means for
evaluating implementations and development results without a live demonstration. To be precise, D3.1
presents mainly the work on gathering a real time basis detailed, enriched and accurate representation
1 In some particular cases it has been extended to other areas as explained in Chapter 5.
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of the LV network; D3.2 describes the main monitoring, operation and control tools developed in the
demonstrator to support these activities in LV, and D3.3 shows the Spanish demonstrator developments
aimed at creating a Consumer capacity building web-based tool. It is worth mentioning that, in order to
overcome the fact that D3.1, D3.2 and D3.3 are confidential deliverables (i .e. not publically accessible),
summaries of concepts (i.e. objectives, contributions, technical basis, etc.) are provided along this
document to facilitate contextual information to understand the demonstrator background and test
results.
D3.4 is organised into seven chapters. The present section, Chapter 1, provides background information
about the demonstrator in terms of objectives, contributions and location. The Spanish demonstrator
benefits and opportunities overview is in Chapter 2. Chapter 3 contains a description of what are the LV
grid operation improvements introduced by the demonstrator. Chapter 4 evaluates the analysis tools
and performance of the deployed remote control over LV smart metering PRIME technology. Chapter 5
presents results regarding the LV network observation and maintenance based on smart meter event
processing and analysis. Moreover, experiences on using the tools from end-users and future
opportunities collected during the demonstrator operation period are distributed in the previous
chapters Chapter 6 reports on the Consumer empowerment tool evaluation. Chapter 7 advances
opportunities derived from innovative software-based components. Chapter 8, supported by contents
of previous chapters, details business processes impacts. Main conclusions reported along the different
chapters are summaries in Chapter 9. Finally, more detailed information (e.g. images, tables, graphs, ...)
about some of the tests conducted are gathered in a series of Annexes at the end on the document.
1.1 DEMONSTRATOR OBJECTIVES
The Spanish demonstrator is driven by four keystones, see Figure 6: data collection from the field, data
transformation into information, application of this information into both O&M and Consumer
empowerment, and last, but not less important, reconsideration of business processes. The
demonstrator has been aimed at assessing these subjects in detail and assembling all of them together
to achieve the expected objectives and impacts.
On one hand, the demonstrator gathers in real or near-real time, a detailed, enriched and accurate
representation of the LV network (e.g. covering components, topology, status, operation, connectivity,
performance, loads and connected generation, etc.). This model is being supported by measurements of
already deployed smart devices in the field (e.g. smart meters and distribution transformer meter
supervision) and those that are being or will be rolled out in a short and medium term (e.g. advanced LV
supervision). The resulted advanced sound LV network representation is the basis, mainly, for the LV
NMS, also developed and deployed in the demonstrator. This overcomes the current DSO lack of both
visibility and detailed knowledge on the LV part of the distribution grid. Moreover, exploring and
optimising the existing electricity and communication infrastructure to allow a rationally evolution of it
is in fact another key aspect of the demonstrator. In this regards, the PLC PRIME functionality extension
for control capability, in addition to metering and billing purposes is one example of that. All the latter
aspects, among others, are paving the way for a MV approach to manage the LV network in terms of
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accurate and near-to-real time visibility, and system integration approach. This is a relevant evolution
that brings new opportunities for the electricity system to deal with short and medium-term challenges
(e.g. Distributed Energy Resources (DER) penetration increase, AMI data tsunami, DSO role as market
enabler...), and for Consumer service improvements (e.g. power supply restoration time improvement,
more accurate and immediate information, ease Consumer participation in the market…).
FIGURE 6: THE FOUR KEYSTONES OF THE SPANISH DEMONSTRATOR
Finally, but not less important, demonstrator developments, once proved efficient, can only be
interiorised and consolidated if current DSOs processes are adjusted in a reasonable manner (without
ruling out the impact that regulation and policy aspects have on them). For this reason, these aspects
should be considered as well.
The Spanish demonstrator objectives are aligned with the previously mentioned premises as follows:
Take advantage of the present smart metering deployment towards the LV grid full sensing and
remote actuation (improving visibility, controllability and operation). Have a sound LV network representation to be the basis for network management tools.
Develop a dispatch tool to support LV network operations: LV NMS. Improvement and extension of the Power Line Communication (PLC) PRIME-based
communications: PRIME multiservice network (remote control operation of LV grid) and PRIME-manageable network (analyse both metering and remote control traffic).
Advanced assistance and support to the grid maintenance crews and grid Operators. Improve the global quality of the LV grid and the services provided to the Consumers. Demonstrate in real user environment improvements in LV networks monitoring and control. Establish a channel to make the Consumer an active, informed and skilled actor in the smart LV
grid.
Field data collection
Data into Information
Info for O&M enhancement and consumer empowerment
Bussiness models
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Identify new DSO processes or how existing ones should be adapted to accommodate UPGRID solutions.
The evaluation of innovative technological approaches ([5][6][7][8]) to some grid O&M processes taking advantage of the new data options the smart grids make possible.
These objectives have been tackled by four work thematic lines (see Figure 7) within the demonstrator
which are summarised next. Specific technical aspects are described in [1][2][3].
LV Network Management System (LV NMS): Developing and implementing new functionalities in a system for advanced distribution network management which earlier first version was tested in
the LV pilot of the Bidelek Sareak project. The system deployed in the Spanish demonstrator is called PowerOn Advantage2. There is a solution for desktop computers (control dispatch) and
other for mobile field crew devices (i.e. tablets). PRIME based functionalities: Developing multiservice PRIME subnetworks (remote control over
IP) and manageable PRIME subnetworks (enhance monitoring capabilities). Smart meter events processing and analysis: Exploring and applying the information derived
from the analysis of existing events generated by smart meters for LV grid maintenance. Consumer empowerment: Improvement of Consumer awareness building a web-based tool.
FIGURE 7: SPANISH DEMONSTRATOR LINES OF WORK THAT COVER THE SPANISH DEMONSTRATOR OBJECTIVES
1.2 DEMONSTRATOR CONTRIBUTIONS
The Spanish demonstrator technical developments are based on the list of UPGRID sub-functionalities
identified in [4] and presented in Table 2. In the UPGRID context, a sub-functionality is defined as an
implementation and/or process (i.e. hardware and/or software) aimed at providing a service to achieve
a purpose facilitated by standards and right technological choices to attain expected impacts. Then,
2 The final LV NMS deployed in demonstrator is based on a newer version of PowerOn product: PowerOn Advantage v6.2.2 (GE) and PowerOn Mobile v6.3.2.SP1 (Yambay), hereinafter called LV NMS Desktop and Mobile solutions respectively. It is an Advanced Distribution Management System (ADMS) solution combining Distribution Management System (DMS) and Outage Management System (OMS) capabilities .
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Table 2 presents a high level cross-matching of the Spanish demonstrator sub-functionalities and its
main lines of work presented in section 1.1. This includes some of the innovative components developed
transversally in the UPGRID project that have been implemented as well [5][6][7][8].
The analysis of Table 2 shows that main contributions are related to “Monitoring and control of LV
network”, “Network management methodologies for network operation” and “Smart metering data
utilisation”. Most of the sub-functionalities are supported by the LV NMS. This is aligned with the key
challenges faced by DSOs nowadays for managing the LV grid:
Ability to collect the correct information in a timely manner.
Ability to process information and motivate decision regarding:
o Network performance
o Consumer service standards
o Future network design options
o Asset management and maintenance regimes
Understanding the dynamic operation of DER connected at LV.
Managing the DER to maintain and secure a compliant network.
Provide a range of services to Consumers that facilitate more cost effective operational options.
For a successful LV management solution, as intended in the Spanish demonstrator, the following
functions and features are required: collect data from multiple sources, share information across
multiple systems (using standard protocols as much as possible), make relevant information available to
the business processes, deliver a continuous process of design, and plan & operate based on a
consistent set of rules and parameters. This leads to a series of expected contributions on different
areas in a short and medium term aimed at addressing the abovementioned challenges. The most
relevant ones are summarised as follows:
Process operation getting efficiency benefits from exploiting the developed sound LV network
digital representation.
New LV network operation and maintenance schemes (e.g. decentralised approaches).
Investigating and solving LV faults having provided up-to-date information to Field Crews (e.g.
mobile solutions).
Adding new capabilities to systems/tools (e.g. LV NMS) derived from the demonstrator
experience.
Broaden the application of PLC PRIME supported on a new LV remote control profile, makes the LV
network more predictable and manageable.
A more rational, automated structuring and processing of the smart meters events improving
distribution grid management processes.
Interface experience using CIM as best practice for future projects.
Get conclusions and criteria about the possible improvement brought by the applicability of
innovative technology to some specific grid O&M processes.
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TABLE 2: HIGH LEVEL CROSS-MATCHING OF THE SPANISH DEMONSTRATOR SUB-FUNCTIONALITIES [4] AND ITS MAIN LINES OF WORK. IN GREEN, CASES WITH DIRECT MATCH; IN
ORANGE, CASES WITH PARTIAL MATCH)
CLUSTER, FUNCTION OBJECTIVES & UPGRID SUB-FUNCTIONALITIES
SPANISH DEMONSTRATOR WORK SCOPE
LV Network Management
System
PRIME based functionalities
Meter Events Analysis and
Processing
Consumer
empowerment
Innovative
components
Cluster 3: Network operations
D7 Monitoring and control of LV networks
D7.1 Operation (control and multiservice) of LV grid devices using PLC-PRIME for
different remote control applications (Concept test)
D7.2 Queries to request advanced meter data to support operation
D7.3 Improvement the LV Network Management System visualisation by integrating data measurements from inside SS (e.g. transformer meter,
advanced LV supervision)
D7.4 Improvement the LV Network Management System visualisation by integrating data measurements from LV network devices (e.g. Consumers SM, EV charging points, DER)
D7.5 Integration of the MV power transformer status from the MV systems to the LV Network Management System
D7.7 Integration of measurement data to support power flow analyses in LV Network Management System
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CLUSTER, FUNCTION OBJECTIVES & UPGRID SUB-FUNCTIONALITIES
SPANISH DEMONSTRATOR WORK SCOPE
LV Network
Management System
PRIME based functionalities
Meter Events
Analysis and Processing
Consumer empowerment
Innovative components
D9 Network management methodologies for network operation
D9.1 Define a sound LV network (schematic diagrams and parameters of
components)
D9.2 Use CIM for LV network modelling and data exchange between e.g. AMI, GIS, MV SCADA, LV Network Management System
D9.3 Interface to manage PRIME subnetwork with Simple Network Management
Protocol (SNMP)
D9.5 Visualisation of data from LV Management Network System in a geographical context
D9.6 Internal DSO business processes review in relation with Outage Management
D10 Smart metering data utilisation
D10.1 Integration and processing of meter events or/and other sources (e.g. telecom data) in the Outage Management System (OMS)
D10.3 Algorithm to determine connectivity of SM to the grid (identification of phase and line to which each SM is connected to)
Cluster 4: Network planning and asset management
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CLUSTER, FUNCTION OBJECTIVES & UPGRID SUB-FUNCTIONALITIES
SPANISH DEMONSTRATOR WORK SCOPE
LV Network
Management System
PRIME based functionalities
Meter Events
Analysis and Processing
Consumer empowerment
Innovative components
D11 New Planning approaches for distribution networks
D11.1 Data analytics based on historical network state data to assist network
planning
D12 Novel approaches to asset management
D12.1 Data analytics based on historical network state data to assist maintenance
D12.4
Deploy some mobile devices (e.g. tablet, smart phone) for accessing and
visualise remotely information from LV system (e.g. geographical context, assets and outage location) to support grid crews
Cluster 5: Market design
D13 New approaches for market design
D13.1 Web portal for increasing the Consumer awareness in order to leverage their
participation in electricity markets
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1.3 DESCRIPTION OF THE DEMONSTRATOR LOCATION
The UPGRID Spanish demonstrator is carried out in part of the distribution grid operated by Iberdrola
Distribución Eléctrica, hereinafter Iberdrola, in the province of Vizcaya (in the Basque Country, North of
Spain) as shown in Figure 8. Geographically, it is located in Bilbao (urban area3) and part of its
surroundings (semi-urban area), delimited by the red and blue lines respectively in Figure 9. The LV
electricity distribution grid involved in the Spanish demonstrator covers approximately 2.150 SS and
400.000 Consumers. During the demonstrator implementation, the area was extended (in blue in Figure
9) up to approximately twice the initial size [4]. The main reasons that motivated that extension were
the opportunity of having a more representative sample of data and proving the scalability of
demonstration concepts related to the use of the LV NMS and highlighting the scalability of the network
modelling process.
FIGURE 8: LOCATION OF THE SPANISH DEMONSTRATION: BILBAO4 (VIZCAYA, BASQUE COUNTRY)
3 The zone classification is defined by the regulation (RD 1955/2000) [11]. Urban area: Groups of municipalities of one province with more than 20.000
supply points, including capitals of province, even if they do not reach the latter figure. Semi-urban area: Groups of municipalities of one province with a
number of supply points between 2.000 and 20.000, excluding capitals of province. Rural area: Groups of municipalities of one province with a number of
supply points less than 2.000. Urban network: Semi-urban network.
4 The capital and main city of Vizcaya is Bilbao with 345.122 inhabitants (2014) and 41.60 km².
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FIGURE 9: GEOGRAPHIC AREA COVERED BY THE DISTRIBUTION NETWORK OF THE SPANISH DEMONSTRATOR (DELIMITED
BY THE RED AND BLUE LINES). IN RED, THE AREA DEFINED AT THE BEGINNING OF THE DEMONSTRATOR – BILBAO (URBAN).
IN BLUE, EXTENSION OF THE DEMONSTRATOR AREA - BARACALDO (SEMI-URBAN)
The LV grid involved in the Spanish demonstrator is deployed and managed radially, as the rest of the
Iberdrola LV grid. This happens even when (sometimes) LV network areas that are fed from different SSs
are interconnected through fuse boxes (FBs) in tie points to supply a LV feeder from another SS. The
latter cases might happen when an incident has made the supply restoration impossible from the same
LV feeder or feeder head. Beside this, most of the underground cables are embedded under tube, and
could be easily connected to any others in parallel by just connecting or disconnecting them in manholes
(i.e. cuts and jumper operations). The supply points are connected using a FB which usually feeds, for
example a building with several Consumers downstream. Residential Consumers are normally single
phase fed (phase + neutral or 2 phases) having in most cases, at least, a three-phase meter for building
services (e.g. lifts); while commercial Consumers are typically three phase fed (3 phase + neutral).
The AMI deployment in the demonstrator area to meet the legal mandate for a smart metering in Spain
[16] was mainly completed prior to start the UPGRID project (in some locations even two years before).
This has resulted in 95% of SSs with data concentrators installed to allow smart meters to be effectively
integrated in the AMI system. Only 12% of SSs have 6 Consumers or less connected to and they have not
been equipped yet. These latter cases will have an “ad hoc” solution that will be installed along 2017
and 2018. Taking advantage of the legal AMI deployment, SSs have been adapted with additional
capabilities (i.e. LV measurements, MV supervision and MV automation). This results in having all these
SSs with measure devices at the LV side of distribution transformers and 303 of them with MV
supervision (collecting power measurements, currents and voltages in all MV cabinets except one).
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Additionally, there is MV remote control in 216 SSs (capacity to operate some devices remotely) and
fault pass indicators in some SSs. It is important to note that the latter SSs are also supervised.
As explained, most of the SSs involved in the UPGRID Spanish demonstrator have supervision meters at
the LV side of the distribution transformer providing measurements at the LV switchboard level.
Moreover, there are approximately 495 SSs with Advanced LV supervision that is an Intelligent
Electronic Device (IED), comparable to a smart meter that provides additional measurements (e.g.
voltage, current, powers, etc.) per LV feeder and feeder phase5. This solution also provides connectivity
information to know accurately in which LV feeder each smart meter is connected to6. As a summary,
Figure 10 shows the three different types of smart meters devices that exist in the demonstrator area
according to their location in the LV grid. The collected information, among other, is used to enrich the
sound LV network representation as explained later in the deliverable.
FIGURE 10: THREE DIFFERENT TYPES OF LV SMART METERS DEVICES USED IN THE DEMONSTRATOR
Regarding the field equipment involved in the demonstrator, it is concluded that, apart from the new
generation of devices developed to support IP capabilities over PRIME network (PRIME Gateways
5 At the moment of writing this deliverable, the LV NMS was prepared for receiving measurements on demand provided by these devices but the AMI Head System interface (see Chapter 3) was not programed yet to do that.
6 The connectivity information gathered by 50 of the devices already at field at the beginning of the demonstrator [4] was uploaded (manually) in the LV NMS during the first loading of network data into the LV NMS (technical tests). This allowed preparing the LV NMS for using this detailed information . However, the network extension modelling (Figure 9) entailed uploading the full network data again and the connectivity information provided by the advanced LV supervision was not entered again. Preparing the LV NMS for using detailed connectivity information has been an enhanced that should continue with the necessary automatic uploading process. For test proposes, the connectivity information used has been: GIS information (feeder connectivity) and assigning one third of Consumer per feeder phase. In spite of possible differences, the fact of being able to use more detailed information during incident management is how the demonstrator provides new value.
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(GTPs), see Chapter 4) the rest were either deployed before UPGRID7 or not funded by the project.
Then, most of the information categories sent to the AMI Head System exited before starting the
demonstrator (as stated in key performance indicators (KPIs) 10, 11, 12 and 138 [9]). Therefore, one of
the main contributions of the Spanish demonstrator is to use data already available from field devices
rather than installing new devices. Table 3 shows the number of main LV equipment in the
demonstration area.
TABLE 3: NUMBER OF MAIN LV EQUIPMENT IN THE DEMONSTRATION AREA
Smart meters 370.971
Transformers supervision meters 3.490
Advanced LV supervision meters 495
GTPs 4
7 Only some units might have been installed during the demonstration period but it has been due to the mandate AMI roll out and not within the demonstrator scope.
8 KPI 10: MONITORING INFORMATION CATEGORIES, KPI 11: AVAILABLE INFORMATION CATEGORIES, KPI 12: CHARACTERISED INFORMATION CATEGORIES and KPI 13: AVAILABILITY OF INTELLIGENT NETWORK COMPONENTS.
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2. DEMONSTRATOR BENEFITS AND OPPORTUNITIES
OVERVIEW: PRESENT AND NEAR FUTURE
It is difficult to account each demonstrator for benefits on one individual sub-functionality (Table 2)
since most of them are interrelated. As it has been shown in Figure 6, the Spanish demonstrator is based
on four keystones which serve as a high level thread to showcase the benefits. The following list
provides a qualitative overview of the expected demonstrator benefits (present and near-future) which
are developed, consolidated and evaluated in more detail in the rest of chapters and annexes of this
deliverable. This is much related to the impact on the business process described in Chapter 8.
Increase of AMI infrastructure value: leveraging field data and DSO value increase
The demonstrator contributes boosting the use of existing and new field measurements mainly from
already or planned deployed monitoring LV equipment, for example, smart meters, distribution
transformer supervision meters and advanced LV supervision meters. The legal mandate on smart
metering is opening new opportunities beyond metering and billing. This is aligned with the prevailing
trend, apart from billing purposes, to use the smart meters to monitor and improve the LV grid
[12][13]. The demonstrator has developed integrated solutions (e.g. sound LV network, LV NMS, LV
control over the PRIME infrastructure and processing of smart meters events) based on existing
technologies. This increase in monitoring and controllability of the LV network paving the way to new
opportunities and services to address current and near future electricity distribution challenges and
roles. In this way, the demonstrator is also contributing to facilitate the consolidation and
enhancement of the network as the backbone of the electricity sector and the DSO as market enabler.
This provides additional value to current investments and incentive new ones.
Have a sound LV network representation
The main benefits are:
Provision of accurate and up-to-date information to distribution centre Operator and Field
Crews: accurate knowledge about the LV network.
Availability of new graphical information (e.g. real LV feeders routing, SS interior schematic and
assets attributes).
Detection of information inconsistencies in existing systems (e.g. GIS).
Use of CIM to import automatically data to create/update the sound LV network representation.
The demonstrator has developed a consistent and reliable LV network representation build on top of
work started in the Bidelek Sareak project. It did not exist before. New information and capabilities
have been developed during UPGRID (e.g. automatic data import processes, new pieces of equipment
information added, development of new interfaces and CIM modelling improvement). This model is
complemented with LV and MV field measurements and it is the basis for the LV NMS network
graphical visualization.
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Dispatch tool (Desktop and Mobile solution) to support LV network O&M: new monitoring,
controllability and operation capabilities
The main benefits are:
System integration approach: avoid data redundancy and leveraging existing data.
Implementation of centralised and decentralised solutions for LV network O&M.
Integration of meter events (online) for enhancing Outage Management.
Managing the LV diagram, asset search & query, information and field work management.
The LV NMS is aimed at leveraging the sound LV network representation building new functionalities on
top of that. The interfaces stablished between the LV NMS and other existing systems (e.g. GIS, SCADA,
OMS, AMI, etc.) provide key information to Operators allowing them taking more precise O&M
decisions than before. Some examples are: LV and MV measurements displayed on the LV network
model, being able to request LV measurements (smart meters and supervision meters) on demand,
being aware of LV incidents based on spontaneous smart meters events in addition to Consumer calls,
use smart meter connectivity information for more accurate incident reports and distribution power
flow (DPF), being able to consider planned works and display graphically the impact of MV faults on the
LV network.
Being able to request three phase smart meters measurements on demand from the LV NMS is useful
to narrow down the location of the LV incident (and thus reducing the time required for that) and check
the restoration of the service. Requesting supervision meter measurements support this process as
well.
Moreover the LV NMS provides the benefit of representing network topology changes. This ensures
that any modification (e.g. cuts and jumpers implemented at field during an incident management to
succour one feeder from another) is recorded conveniently by Field Crews in a centralised way. Before,
this kind of information was nowhere with the attendant risk of future field works. In this sense, having
integrated the connectivity information in the system allows the Operator knowing where the
Consumers are connected at any moment. A part from the other advantages pointed out regarding this
feature, it permits keeping updated the connectivity records after performing cuts and jumpers at field.
Then certain Consumers will start appearing connected on the feeder that provides the service to them
from that moment.
Additionally, being able to trace graphically LV circuits based on, for example voltage levels and
energised status up to the corresponding LV feeder head, provides some benefits such as checking in
advance that a cut and jumper at field is compatible regarding voltage level (i.e. if a LV feeder can be
fed by other).
Improve the different factors that impact on global quality of the LV grid: Consumer oriented
The main benefits are:
Reduce power supply restoration time.
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More accurate and immediate information regarding an incident.
Individual quality of service improvement.
The LV NMS provides the capability to respond more efficiently to LV incidents. Previously there was
almost no information from the LV network. Basically, the only information to realise about the
existence of a LV failure was the Consumer calls. It is worth mentioning that Consumers could be aware
of an electricity supply outage in minutes, days, months or even longer times, depending mainly if they
were or not at home. Now, thanks to the work developed in the demonstrator, the Operator has the
capability of knowing about it before receiving the first call. Then, when Consumers affected by an LV
incident call, they can even receive information about the current situation.
Other type of incidents that can be detected faster without waiting for Consumer calls are the ones
related to a MV fuse blown. This scenario is difficult to detect since there is no trigger in the
corresponding SSs. In these situations the electricity system can still be online by the two unaffected
phases. With UPGRID it is possible to identify one phase fault. The potential impact of this benefit
would not be only on LV Consumers but also on MV indexes such as System Average Interruption
Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI) indexes. Current
regulation starts counting the LV incidents duration from the moment of receiving the first Consumer
call regardless when the incident happens. Then, Consumers have not a value for the early
identification of the incident.
Additionally, thanks to: the connectivity information provided and processing as result of the advanced
LV supervision solution, the sound LV network representation and the LV NMS, more detailed and
accurate LV incident management reports are elaborated now. Before UPGRID, for example, the report
of an LV incident on a single LV feeder phase counted all LV Consumers connected at that LV feeder as
affected (even being the real number of them lower) during a time period equal to the total incident
duration (i.e. since the first call up to the moment the service is totally restored). However, thanks to
UPGRID, it is possible to add into the report more detailed information considering the number of
Consumers who are really affected and the real time each of them have been without service (i.e.
similar approach followed in MV).
A more rational, automated structuring and processing of the smart meter events (offline): assist
maintenance
The main benefits are:
Detection of potential cases of improvement: voltage deviations.
Detection of repetitive incidents: predictive maintenance.
Detection of data base inconsistencies.
This is another example how the demonstrator exploits data from existing field devices for enhancing
the LV maintenance. In this case, smart meters have been registering events but they have not been
processed yet. Now, the demonstrator has started analysing them in order to explore strategies and
approaches to extract new benefits. In this regards, a selected group of events has been identified
among all existing ones for being considered more interesting for LV maintenance (e.g. undervoltage
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and overvoltage). Thanks to the developed tools and graphical representation of results (graphs and
maps) the maintenance responsible has the opportunity to identify LV network areas that are
registering, a priori, anomalous number of events easily. Moreover, having defined different levels of
data aggregation based on network assets (e.g. SSs, FBs and individual smart meters), the process to
determine the location, cause and then selecting an appropriate solution (e.g. adjust transformer tap
changer positions, change cable type, perform a remote detailed voltage monitoring of selected smart
meters, correct asset data base inconsistences, etc.) is facilitated.
Improvement and extension of the PLC PRIME-based communications: remote control operation of LV
grid and manageable subnetwork
The PRIME manageable subnetwork opens the way to use AMI deployments for further applications
(mainly focused on network operation optimization and remote control capabilities over the LV grid)
and not only for billing purposes. The proposed PRIME network monitoring enhancement (i.e. PRIME
communication channel usage characterization) is a prerequisite to evaluate best approaches to
introduce new applications. Additionally, the regular AMI operation is improved as well since the
performance information included in the SNMP web tool developed for UPGRID project allows
detecting real time issues, for example in AMI data concentrators. This could have also a positive
impact on LV incident management and then on Consumers quality of supply (QoS) since changes in the
PRIME subnetwork status regarding their elements (e.g. smart meters) can be observed.
The LV remote control can start to be assumed within the smart grid functions. MV remote control is
well integrated in the electricity grid operation, although not present in all SSs. Its purpose is to get
information of the grid as well as to operate the grid elements (e.g. switches) remotely and safely.
Remote controllable points in the LV grid will allow the same mode of operation in this LV segment of
the grid.
A practical benefit of this is the reduction of uncertainties regarding LV feeder capacity. As result, the
hosting capacity can be increased. The fact of having more detailed and sound information allows DSOs
to be less conservative regarding the amount of distributed generation (DG) connected into the
network since there would be the certainty that the QoS is not jeopardised. This, together with the
capability of controlling these generation units thought LV grid remote control operation over PRIME
infrastructure, can allow increasing the hosting capacity even more since there is a means, at a
particular moment if required, to reduce the power injected into the network (if approved by
regulation).
Moreover, IP over PRIME implementation can be used by the DSOs as an alternative transmission mean
for SSs where other options are not cost effective. Additionally, remote control for smart-switching is
applicable as well. New LV elements, such as LV smart-switches can be remotely controlled using IP
over PRIME in order to switch to backup lines connected to alternative SSs.
Establish a channel to make the Consumer an active, informed and skilled actor in the smart LV grid
In this way Consumers can discover how they use energy and how they can make savings, using real
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consumption data collected by the smart meters. In short, the aim of the tool is to ensure that users
have enough technically and economically reliable information to allow them to take responsible
decisions to help reduce their electricity consumption. This contributes positively on the active
participation of Consumers and realise the socio-economic benefit that smart grids are envisage to
bring with new opportunities. Dissemination activities are important to make known these kinds of
tools by the end-users.
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3. LV GRID OBSERVABILITY AND OPERATION IMPROVEMENT
The main objective of this chapter is to report on LV grid observability and operation improvement
derived from the work developed in the UPGRID Spanish demonstrator. First, the introduction provides
information to understand main aspects of the LV NMS which are described in more detail in [1][2].
Second, improvements are evaluated based on field experience in the demonstrator and near-future
opportunities are pointed out.
3.1 INTRODUCTION
The LV NMS is an Advanced Distribution Management System (ADMS) solution combining Distribution
Management System (DMS) and Outage Management System (OMS) capabilities. It uses the sound LV
network representation as a graphical basis. This resource provides to the LV NMS a near-real time
detailed, enriched and accurate representation of the LV network (covering components, topology,
status, operation, connectivity, performance, loads and generation connected, etc.). It includes: the
geographic (Figure 11) and schematic (e.g. figure 11, on the right) diagrams, a connectivity model,
measurements and assets information (attributes) to allow the system Operator to visualise, monitor
and control the current state of the LV network through the LV NMS.
FIGURE 11: GENERAL VIEW (NOT ZOOMED) OF PART OF THE DEMONSTRATOR AREA (LV NETWORK MODEL)
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The LV network representation is not a static model. Otherwise it would not represent the state of the
grid on near-real time. The two features that enable this capability are: the use of field measurements
(e.g. smart meter measurements, distribution transformer MV and LV measurements and advanced LV
supervision) and data model import process that ensure Operators be aware of topological changes. The
main data source of this model is the Iberdrola’s GIS. The automatic importation process from the latter
alphanumeric system is an important step forward that determines the quality of the achieved network
representation (i.e. electrical and topological referential integrity). The use of the reference Common
Information Model (CIM) supports this process, for example, facilitating the modelling of new network
elements (non-existing previously in the Iberdrola’s GIS) that have been added to the model (e.g. SS
internal elements and geometry creation for LV cables (routing)) as shown in Figure 12.
FIGURE 12: COMPARISON OF NETWORK ELEMENTS BETWEEN THE GIS (LEFT) AND THE LV NMS NETWORK (RIGHT)
It is important to understand the reasons why having the LV network representation together with the
monitoring enhancements are key resources. For that, it is necessary to explain that distribution
network Operators using MV SCADA do not visualise what is happening beyond the MV side of the
distribution power transformers. There is not such graphical model for LV in the latter system as it can
be observed in Figure 13. Figure 13 (left) shows the graphical representation of a SS in the MV SCADA
where the yellow cross represents the MV side of the distribution transformer. Beyond that, there is not
LV information to be provided to the Operator. Thanks to the UPGRID demonstrator there is now a
comprehensive, accurate and reliable LV network model that, together with the functionalities
developed on top of it (LV NMS), allows relying O&M decisions on it.
FIGURE 13: EXAMPLE OF A SS MODEL VISUALISATION IN THE MV SCADA
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It is worth noting that the LV NMS deployment has not been conceived as a standalone system but fully
integrated with Iberdrola’s third party systems. This integration has been made through a series of
existing and new interfaces with, for example: GIS, AMI, OMS, SCADA and CIS. Figure 14 shows a
schematic representation of the interfaces involved in the LV NMS integration.
FIGURE 14: SIMPLIFIED DIAGRAM SHOWING MAIN INTERFACES BETWEEN SYSTEMS
Table 4 provides a summary of the interfaces involved in the system architecture shown in Figure 14.
TABLE 4: INTERFACE SUMMARY INVOLVED IN THE LV NMS INTEGRATION
Interface Main information involved
GIS to Model Integration Platform Alphanumeric LV network information. Geographic model
update. Data conversion and correction to ensure integrity.
Model Integration Platform to LV NMS LV model update file exchange (CIM).
SIC to LV NMS Consumer information (smart meter id, connectivity, power
contracted and type of Consumer).
OMS to LV NMS
- LV Consumer incident calls (to initialise an incident record).
- MV energisation status (to visualise the impact of MV incident in
the LV network).
LV NMS to OMS Order completion (detailed incident completion report).
LV NMS to AMI (via OMS)
- Meter measurements on demand requests (polling): smart
meters, SS supervision meters, advanced LV supervision meters9
- Historic smart meter measurements on demand request.
AMI to LV NMS (via OMS) Spontaneous smart meter events (to initialise an incident record).
9 As soon as the AMI Head End is prepared to perform on demand measureme nts to these devices that are being installed.
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Interface Main information involved
Outage Planning System to LV NMS Planned LV supply outages (visualisation of the work order in the
LV diagram).
SCADA to LV NMS Voltage measurement at the MV side of the distribution
transformers.
WMS to LV NMS Planned maintenance (to be aware of all maintenance pending
work planned).
The LV NMS deployed in UPGRID project is based on a newer version of the PowerOn product, PowerOn
Advantage v6.2.2. and PowerOn Mobile v6.3.2.SP1 (hereinafter called LV NMS Desktop and Mobile
solutions respectively). Figure 15 shows a simplified architecture scheme of the LV NMS solutions
deployed in the UPGRID Spanish demonstrator.
FIGURE 15: LV NMS SOLUTIONS DEPLOYED IN THE UPGRID SPANISH DEMONSTRATOR: DESKTOP AND MOBILE
Table 5 describes the main capabilities for advanced LV network monitoring and operation provided by
the LV NMS deployed in the UPGRID Spanish demonstrator. Annex I contains a set of figures showing
examples of how the LV NMS user is provided with real or near real time data reflected on the LV
network diagram thanks to the interfaces indicated in Table 4. The LV NMS Graphical User Interface
(GUI) has been defined and designed based on the system Operator’s criteria, requirements and roles
(e.g. colour coding). This also includes the availability of geospatial reporting and analytics tools.
Moreover, these screenshots represent most of the capabilities (Desktop and Mobile solution) listed on
Figure 15.
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TABLE 5: CAPABILITIES FOR ADVANCED LV NETWORK MONITORING AND OPERATION FROM THE OPERATOR PERSPECTIVE
PROVIDED BY THE LV NMS DEPLOYED IN THE UPGRID SPANISH DEMONSTRATOR (MORE DETAIL IN [2])
Capability name Desktop / Mobile
Mobilisation of LV network diagram: Mobilisation of Iberdrola LV network diagram including
map background. Changes to the model including switching and connectivity model updates
available to Desktop and Mobile users in real time.
Desktop and Mobile
Access to asset information: Operational asset model, allowing crews to access up-to-date
asset data from the LV network diagram. Desktop and Mobile
Asset search: Ability to search for assets based on the LV network diagram. Search engine can
use key words such as asset alias or name. Desktop and Mobile
Asset model update: Updates to the LV NMS asset model are available to LV Field Engineers in
real time providing the Mobile device has an active network connection. Desktop and Mobile
Access to Consumer connectivity: Access to Consumer information from the LV network
diagram (supply point identification, address, connected phases, etc.). Desktop and Mobile
Access to meter data: Access to smart meter and supervision data from the LV network
diagram. Desktop and Mobile
Offline usage of LV NMS Mobile solution: Being offline, LV Field Engineers are able to: access
the latest network diagram downloaded by their device when it was last online, search for
assets, and access the last available information of any given asset.
The connection state of the LV NMS Mobile device, including whether the system is displaying
real time or offl ine data, is clearly visible to the field user.
Mobile
Tracing capability: It allows Operators to run pre-configured and ad-hoc traces to quickly
understand the context of the wider network both upstream and downstream of where they
are currently working.
Desktop
Establish the source of power: It allows Operators to quickly establish the source(s) of power
currently energising each network asset. Desktop
Access to asset historic information: It enables Operators to quickly access historic information
for each asset including load/voltage curves. It is possible to overlay historic and real time
information showing the current performance of an asset relative to its histor ical one.
Desktop and Mobile
Annotations on network asset: It provides the Operator with the ability to attach descriptive
text. Desktop
Alarm and event management: Operators have access to a comprehensive Alarm and Event
Management module that provides a consolidated view of all events on the system, including
SCADA alarms, metering events, etc.
Desktop and (Mobile10
)
Use of historic values: The LV NMS uses historical values (for instance, maximum values during
a period) to support network planning and development. Desktop
Network switching: Online update of network connectivity with planned jobs and outages from
both the Desktop and Mobile solutions. Temporary connections (i.e. Cuts and Jumpers), with
the real geospatial layout of the temporary assets in the field, are registered graphicall y and
their connectivity updated.
Desktop and Mobile
10 From the Mobile solution it is possible to visualise the smart meter events on the diagram.
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Capability name Desktop / Mobile
Restoration decision support: Selection of the actions to restore service supply, checking the
real time and historical values of power and voltage in the affected feeders and other feeders
that can assist the restoration process.
Checking the success of restoration in the field by querying the status of the smart meters of the
potentially affected Consumers and supervision meters.
Desktop and Mobile
Analysis of distributed generation: Specific analysis of distributed generation along LV feeders,
with the possibil ity of issuing voltage settings. Desktop
The UPGRID LV NMS solution has been built upon a prior LV prototype (deployed under the Bidelek
Sareak project11) and extends the interfaces and capabilities. During the demonstration implementation
the network area was extended (Figure 9) and the LV model has been completed including components
attributes in conformance to the reference CIM standard model.
3.2 EVALUATION
The LV NMS solution has been evaluated for over 6 months during normal operation of the LV network.
The scope presented in this section is based on the data loading process of the LV network area shown
in Figure 9 and the analysis of 583 LV incidents registered in the LV NMS. The bulk of the incidents are
related to issues related to SS fuses, feeder fuses and connection in LV risers (“botellas” in Spanish). 52
out of the total number of incidents have been managed by Field Crews using the Mobility tool). These
incidents are those managed by the staff who received the training course about the system and within
working hours. The incident data collected has been also included in an Excel sheet for facilitating its
analysis, see Figure 16.
For operational testing a total of 6 Mobile devices has been deployed. 4 units for the two Field Crews
equipped in the demonstrator (2 in Bilbao and 2 in Baracaldo areas), and 2 devices for internal testing
and monitoring purposes. The Desktop solution has been used by 2 people in Bilbao and 1 in Baracaldo.
Different training courses12 (end 2016, start 2017) have been carried out for the LV NMS users before
placing the solution in operation. Field Crews and control room staff have different privilege access on
to the Desktop solution. Experiences and feedback from them have been collected in order to identify
future enhancements based on the field use of the tool. In general terms the LV NMS has been
favourably received based on the surveys distributed among the participants. Moreover, the feedback
received during the courses and while is being in use, demonstrate that end-users13 are motivated and
willing to continue using the solution. This is a good indication of the usefulness of the system and paves
11 The Bidelek Sareak project is the demo base for the UPGRID Spanish demonstrator (http://bidelek.com/)
12 End of 2016 and beginning of 2017.
13 In this context, end-user is referred to LV operation and maintenance people who used the LV NMS (Desktop and Mobile solution).
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optimistically the ambition of extending the LV NMS deployment to other LV network areas in a near-
future as envisaged at the beginning of the demonstrator.
FIGURE 16: EXCEL SHEET EXTRACT WHERE LV INCIDENT INFORMATION HAS BEEN RECORDED FOR BEING ANALYSED FOR
EVALUATION PURPOSES
BASED ON THE DATA GATHERED AND EVALUATION CHECKS PERFORMED DURING THE OPERATIONAL USE OF THE LV NMS
IN THE DEMONSTRATION AREA, THE MAIN TOPICS THAT HAVE BEEN EVALUATED ARE PRESENTED IN THE SUBSECTIONS
BELOW. THIS STRUCTURE ALSO FOLLOWS THE LIST OF USE CASES DEFINED IN [2]: MAINTAIN LV DIAGRAM, INTRODUCE
TEMPORARY CHANGES, MANAGE UNPLANNED LV INCIDENTS, MANAGE PLANNED LV WORKS, INVESTIGATE LV INCIDENTS,
RECONFIGURE LV FEEDER AND CREATE LV INCIDENT OR ALARM FROM SMART METER EVENTS. DETAILED DESCRIPTION
AND TESTS OF THE INDIVIDUAL CAPABILITIES (
Table 5) developed in the LV NMS that support these topics were done during the elaboration of [2]. For
each of these topics, a series of specific improvements are pointed out as well which are linked to
business benefits processes.
3.2.1 SOUND LV NETWORK DIAGRAM GENERATION
The data importing validation has consisted in checking that the graphical representation in the LV NMS
matches with the GIS information. To perform this check, a number of randomly selected SSs were used.
The validation process has been done manually since it has been the first time this kind of data loading
was performed in Iberdrola. This, together with the fact that not only the GIS information is represented
graphically in the LV network diagram generated in the demonstrator, has made the testing process
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even more laborious. This additional information has been added to enhance the value of the resulted
model.
The LV network was loaded in LV NMS using CIM files. The CIM mechanism only allows the export of
connected elements in a circuit. Therefore, any exported element will be properly connected to the
correct elements upstream.
A series of main aspects have been examined to validate the correctness of the data importing process
following a kind of check list. This counts the new information not included in the GIS: LV feeders are not
physically represented and SS internals are not available in that system.
Check that LV feeders are connected to the correct LV switchboard (there are two colours
depending on the feeder voltage level. Each LV switchboard should have circuits with feeders of
the same colour).
Checks that all LV feeders (with their FBs) included in the GIS appear in the new LV network
diagram as well.
Check that LV feeders are not crossed among them.
Connectivity check. From FB to their corresponding distribution transformer.
Auto-transformer check. In order to check that they are properly connected.
Load break switch (“cajas seccionadoras”, in Spanish) check. To ensure its position is properly
placed on its corresponding network route.
Association between feeder and network routes (trenches) check. To ensure that cables are
properly associates to their network routes.
Connected supply point (FB) check. To check the number of supply point corresponding feeder.
Check for tracking total number of exported objects to the LV NMS.
Most of the cases checked have been well represented in the LV network diagram (e.g. Figure 17). This
means that the algorithm created for overcome the lack of graphical representation for feeders and SS
interiors have performed well in the demonstrator. The network diagram was not manually edited after
the automatic load from CIM. Given that network is being incrementally updated, all manual editing
would be lost when loading the next incremental update on the LV NMS. This highlights the reliability of
the algorithm developed to model the LV network. Moreover, descriptive attributes that were selected
to be displayed in popup windows associated to network elements are shown correctly based on the
comparisons done with the information in GIS.
Table 6 present a summary of ratios related to the goodness of the automatic data importing process
achieved during the first data loading considering the demonstrator area. These ratios, although not
high, would be permissible values taking into account the novelty of the process and the developed time
on the interface during the demonstrator. However for near-future data uploading, considering the
solution into operation should be close to 99%.
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TABLE 6: DATA IMPORTING RESULTS AFTER THE FIRST LOAD
SSs loaded successfully in the LV NMS solution 2.154 (77% success rate)
FBs loaded in the LV NMS solution 50.994 (90% success rate)
Data loading time (full demo area network) 7 days to export the CIM files from Model Integrator [1] and import them into the LV
NMS
FIGURE 17: EXAMPLE OF A SS INTERIOR SCHEMATIC SUCCESSFULLY GENERATED. VALIDATION BETWEEN GIS
INFORMATION (ABOVE) AND LV NMS NETWORK MODEL (BELOW)
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Moreover, smart meter did not reside in the original GIS system and were loaded separately and
automatically into the new LV network diagram adding additional graphical information about the type
of Consumers [1] (e.g. Figure 74, number of Consumers, existence of generation, sensitive consumer
and single or three phase smart meter). The validation check has consisted in comparing the loaded data
with registers on the SIC and GIS. This has been done with a number of cases chosen randomly. No
errors were identified.
TABLE 7: DATA IMPORTING RESULTS
Number of distribution transformer supervision meters loaded 3.083
Number of Consumer smart meters loaded 372.416
During the evaluation of the data importing process result, some issues were identified and reported for
either being quickly solved or taking into account for future LV network diagram loading. Some
examples are presented next. In Figure 18 it is possible to observed how line 6 (400 V) is connected to
an incorrect LV switchboard (231 V) after the data loading; and Figure 19 shows the case on a missing LV
feeders and its associated FBs. In some punctual cases, it was observed that certain FBs lacked of their
geographic coordinates on the corresponding data base table.
FIGURE 18: EXAMPLE OF LINE ASSIGNED TO AN INCORRECT LV SWITCHBOARD. GIS INFORMATION (ABOVE). LV DIAGRAM
GENERATED FROM GIS DATA (BELOW)
The auto-transformer check has allowed identifying issues in their representation. It is worth
mentioning that this kind of transformers where not included in the GIS. The needed amendments were
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implemented refining the graphical representation of these elements inside the SSs. Feedback was also
received from field about some missing lines or not exported data.
FIGURE 19: EXAMPLE OF MISSING LV FEEDER AFTER THE DATA IMPORTING
During the process of correcting wrong cases, it was identified the need of testing specific elements
faster without waiting for a new network loading. The latter is because data exporting process is
creating CIM data based on SSs, so it is possible to specify a specific SS or a set of them to export. But
import process from legacy systems, such as the GIS used in the demonstrator, was getting the whole
network for an area, which is usually composed of thousands of SS. Therefore, it was necessary to speed
the import process up just creating a new approach that extracts specific SS elements, its corresponding
circuits and related elements only. In this way, the same algorithm used for the import process ca n be
run, but allowing getting just the substation selected. In this way, this extraction approach gets any
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related element to a specific set of SS, creating a new set of files in the original format created from GIS,
but containing required elements only.
It is worth mentioning that in the demonstrator two different versions of the system have co-existed:
one for operation and one for development purposes. Taken into account all the information collected
during the evaluation, the incremental export process was modified to add these elements and resolve
the issue when possible. While the incremental load process was tested, they were confirmed as
resolved in most cases. From the manual evaluation process, issues related to 25 SSs were identified as
incorrect, and from these issues, they all were properly resolved after a subsequent incremental
import/export execution (in a development environment). This means that the new success ratios in
future network data loading should be closer to the desired ones for getting the process into production
(i.e. almost 100%).
One lesson learnt during the tests is that, since the LV network of the demonstrator includes more than
2.000 SSs, testing them all manually to verify if they are properly loaded is quite difficult and time
consuming. It is not possible to test every single combination of possible connected elements in the
network, since number of possible combinations is huge, especially for incremental load process. So,
identifying appropriate cases in order to maximise tested elements and minimise manual testing and
validation are required for future loading process.
Another result of the validation process is that the LV NMS developed in the demonstrator has been
able to manage successfully the increase of data arisen after approximately doubling the original
intended LV network extension covered by the system (see Figure 9). This demonstrates the scalability
of reliability of the network load process. Moreover, this extension has allowed identifying a series of
aspect to take into account before adding any other area:
Unique object identifiers: network areas to load could have duplicated object identifiers, in such a
case, some system should ensure that objects can be identified properly, even if they have
duplicated identifiers in their original repositories.
Exchange formats like CIM includes fields from different models: different areas could have
different data models, CIM schema should be extended to include them all if needed.
Data exchange formats should be the same: for the current demonstrator inputs from SXP format
(stands for Simple XML) and output to CIM format. An approach using different formats would
need to re-write several important pieces of code.
Table 8 seeks to reflect a summary of the main improvements process related regarding the sound LV
network generation and the business benefits that pose.
TABLE 8: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE SOUND LV
NETWORK DIAGRAM GENERATION
Improvements process related Business benefits - Diagram exchange availability in CIM data
format. - Visibility of the LV diagram.
- Operational real time view of the LV grid available within the organization.
- The better the quality of the diagram, the
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- Data quality improvement. - Module to extract specific parts of the LV
network from the Iberdrola’s original data (i.e. incremental data loading).
- Scalability of the load.
better efficiency in the operations and control.
- Future CIM compliance systems will be able to import the whole diagram or pieces of it (circuits).
- Loading specific parts of the network is very helpful for testing purposes, decreasing time required for incremental load tests.
3.2.2 LV DIAGRAM MAINTENANCE
The validation process regarding the LV diagram maintenance has been focused mainly on checking if
temporally network changes (i.e. cut and jumpers)14 have been registered correctly on the LV NMS
(network diagram and reports). To do that, the 52 LV incidents which have been managed using the LV
NMS Mobile solution have been reviewed, they all involved temporal changes. This temporally
operations are done in order to restore a consumer who has been affected by an incident on the LV
network as quickly as possible (later on they are removed when the permanent solution is carried out).
The same is done with switching operations. In this case, some incidents required, for example opening
/ closing LV fuse inside the SS (at the LV switchboard).
There are two scenarios:
1) The Desktop solution user needs to create the operation from the Desktop user and instruct (send)
the operation to the LV Field Crew who is supplied with a mobile device. Then the Field Crew
acknowledge and confirm on it. All information should be recorded graphically and in the
corresponding work log of the LV NMS.
2) The Field Crew is who create the operation without any Desktop user involvement. This means
that once the element status is modified at field it can be reflected (manually) on the LV network
diagram. When the operation is set as confirmed, the fuse is shown on the diagram reflecting its new
state and the new energisation status of LV feeders affected by that operation is also automatically
displayed on the diagram.
The second scenario was implemented based on the feedback received from LV NMS users during early
training courses during the development phase. Moreover, in both Desktop and Mobile, the user should
be able to search for all temporary operations and navigate to the diagram from each of them.
An example of the evaluation process is shown from Figure 20 to Figure 22. In these figures it is possible
to observe that the cut and jumper is sent by the Desktop user to the Mobile solution is registered
correctly and when it is performed by a Field Crew is also visualised automatically by the Desktop
14 Temporal elements (e.g. cuts and jumpers) are carried out on the field in order to quickly restore consumers who have been affected by an outage. These temporal elements remain for a short period of time (typically less than 36 hours) until a permanent fix can be carried on th e network.
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solution. While Figure 23 is one example monitored in real time when the Field Crew register graphically
the temporally change on the Mobile solution in one of the selected incidents.
FIGURE 20: RELATED CUSTOMER CALLS FOR AN OUTAGE
FIGURE 21: OUTAGE HISTORY FOR AUDITING PURPOSES
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FIGURE 22: SWITCHING LOG DETAILING THE TEMPORARY OPERATIONS TO RESTORE AN INCIDENT
FIGURE 23: THE SAME TEMPORAL ELEMENTS SHOWN IN THE MOBILE SOLUTION
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Figure 24 and Figure 25 present an example of the validation perform checking scenario 1 and 2.
FIGURE 24: FUSE OPERATED ON TABLET. THE FUSE ON THE RIGHT HAS BEEN REMOVED
Information is presented also in a tooltip state on the Desktop diagram and the visualisation indicates
the phases affected.
FIGURE 25: VISUALISATION ON DESKTOP DIAGRAM OF A REMOVED LV FUSE (THE ELEMENT NOT COLOURED ON THE RIGHT
HAND SIDE) AND THE LV CIRCUIT DE-ENERGISED (IN WHITE). A TOOLTIP DISPLAYS THE FUSE STATUS PER PHASE.
This functionality has been well valuated based on the feedback received from field. It is highlighted that
prior to the LV NMS solution these temporary operations were recorded on paper. By having a LV
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network and the ability to add these temporary operations from the Mobile device as they are carried
on the field, the recording of these operations is simplified and the LV network representation is more
accurate, reflecting the real time state of the network. Overall, the system complies with the
expectations.
Additionally, some enhancements have been proposed by the Field Crews after using the Mobile
solution to perform these operations:
The Field Crew informed that sometimes operations are carried out on the neutral cable. These
operations currently cannot be recorded or reflected on the diagrams except as a visual marker.
The LV NMS is not considering the existence of the neutral phase. In real O&M some field work
relies on the use of this phase for temporal solutions. Most of the neutral cable breakage create
overvoltage episode in the network what can even burn Consumer appliances.
When reconfiguring feeders, the Field Crew also expressed the need to easily identify the circuit
for given feeder on the diagram. The feeder name reflects the circuit but further opportunities
exist to allow tracing functionality from the Mobile device.
Being able to open/close fuses in FBs (most probably the system allows it but it was not
considered in the first specification and then not parametrised).
Being able to perform actions in the LV risers since it is one major focus of LV incidents. In this
case the LV risers are graphically represented but they are not operable (capability not included
in the current version of the system).
Table 9 seeks to reflect a summary of the main improvements process related regarding the sound LV
network generation and the business benefits that pose.
TABLE 9: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE LV DIAGRAM
MAINTENANCE
Improvements process related Business benefits
- Record switching changes to the network topology so that the LV diagram is operationally correct either by desktop or mobile interface.
- Record temporary changes to the network topology so that the LV diagram is operationally correct either by desktop or
mobile interface. - Visibility of planned work requiring de-
energisation.
- Accuracy of the LV sound representation - Accurate and timely recording of temporary
operations. - LV Field Crew can record the temporary
actions directly on the mobile device keeping the information stored and available for
other system users.
3.2.3 LV NMS INTEGRATION WITH EXISTING SYSTEMS: INTERFACES
The evaluations performed around this topic have been aimed mainly at checking the integration of the
data coming from the different source systems (see Figure 14 and Table 4) into the LV NMS. That is,
validate that the same information managed in by the source systems is the same that is used by the LV
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NMS and how the performance of the interfaces is. Each interface has been individually tested and left
enabled for the duration of the evaluation, over 6 months. Some interfaces have been executed just one
time (e.g. SIC) while others are active on line (e.g. OMS).
Overall, MV voltages, Consumer calls and spontaneous meter events, and the ability to poll the smart
meters and visualise the results were received in real time. Visual representation on the diagram of the
smart meter events has been achieved. This was accessible from both the Desktop and Mobile user. This
visual information provided the field engineers with a better understanding of the state of the network
in real time. Apart from punctual cases that were solved, the performance observed has met the
expectations. This is especially important for those that are executed in real time.
Validation 1: This evaluation has consisted on checking that measurements displayed in the LV NMS
correspond to those from the data source. In the case of MV voltages, the quality of the measurement
and the timestamps were checked against the data held in the MV SCADA system. Figure 26 and Figure
27 show an example of the validation performed. Other measurements have been checked against the
meter data management system (MDMS), for example, measurements of SS supervision meters. Figure
28 and Figure 29 show one of the cases checked15.
FIGURE 26: SS MV MEASUREMENT ON THE SCADA
15 Important to note that the S14 report (measurements from SS supervision meters) are hourly measurements while the measurement s on demand shown in the LV NMS network model are instant values. For this reason slight differences can be observed.
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FIGURE 27: SS MV MEASUREMENT ON THE SAME SS OF FIGURE 30 REPRESENTED ON THE LV NMS
FIGURE 28: MEASUREMENT REPORT ON SS SUPERVISION METERS EXTRACTED FROM THE MDMS
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FIGURE 29: SS SUPERVISION METER MEASUREMENTS (ON DEMAND) SHOWN IN THE LV NMS LV NETWORK DIAGRAM
THROUGH THE LV NMS-OMS-AMI INTERFACE
Validation 2: This evaluation has consisted in checking how the LV NMS registers as incidents on the LV
network diagram those outages reported by Consumers to the call centre. They should have been
logged firstly in the OMS and then passed onto the LV NMS. The calls should be mirrored in the LV NMS
and displayed on the LV diagram, providing visual information of where potential network issues might
exist. The Consumer call might automatically either generate an incident or be grouped with an existing
incident on the area. This information is also available to the Field Crews on the Mobile device.
The test consisted in the following procedure. First, selecting the LV incidents that responsible staff is
informed (backwards) about the happening by sms. Second, these incidents are traced in the existing
Incident Reporting System where it is possible to see the Consumers and events associated to them.
Third, telephone numbers are traced in the LV NMS to check that the same calls have been associated to
the same incident in the LV NMS.
Based on the 583 real LV incidents reviewed, almost 100% of the cases have been reposted correctly.
Only in one case a strange behaviour was observed without being able to replicate the scenario.
It has been observed that calls from public services (e.g. firefighters, SOS, etc.) are not associated to a
specific FB because they have not an electricity supply contract associated to. It would be necessary to
find a way to overcome these cases.
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FIGURE 30: CONSUMER CALL DISPLAYED ON THE DIAGRAM ALONG WITH INCIDENT NEAR THE FUSE BOX SYMBOL
Validation 2: Similarly, spontaneous events from SSs (distribution transformer supervision meters) or
Consumer smart meters provide useful alerts on possible outages or quality of supply issues on the LV
network. Depending upon the type of event, the LV NMS might handle the event differently. SS meter
events should be visualised on the diagram and they automatically generate a new alarm within LV
NMS. It has been proved based on field records that 751 incidents have been recorded in the LV NMS
triggered by smart meters events. Some of them are also associated to incidents triggered by Consumer
calls.
FIGURE 31: CONSUMER SMART METER EVENT DISPLAYED ON THE DIAGRAM AS A PSEUDO CALL NEAR THE FUSE BOX
SYMBOL
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The validation check has confirmed that the system is able to grouping Consumer calls and smart meter
events. Some cases have not work as expected (e.g. missing events associated to the corresponding
incident or events of the same smart meter but in different moments are associated to the same
incident). This has been observed comparing the grouping performed by the LV NMS and the Incident
Reporting System. Possible causes that are being analysing: configuration issues or algorithm
performance. Moreover, based on the evaluation, it is concluded that the process could still be
enhanced to perform a more refine prediction of the LV incident location. This would entail identifying
the first LV network element in common to all calls and smart meter events. It is consider that this
feature of the tool present an interesting potential for adding value to the Consumers (e.g. DSO can
know quicker the incident existence, it can initiate faster the restoration process, and most probably
shorter the incident time to the Consumer) then it will further explored.
The feedback received from the Field Crews based on the experience collected after managed 52 LV
incidents with the Mobile solution says that being able to perform smart meter measurements on
demand (Figure 72) is a useful feature to:
check if a smart meter has service.
check if a LV incident affect one phase or to the full feeder.
In some case they have reported that this capacity has proved to be useful to locate LV incidents
on the LV network.
Some users have pointed out that it would be good to be able to perform consults to retrieve
information that has not been contemplated in the demonstrator interface architecture approach
(Figure 14). This would allow, for example, consulting the maximum current per feeder before
performing temporally filed works (i.e. cut and jumpers). In this way it could be possible to check it
feasibility (i.e. identify in advanced congestion issues in the affected feeders). Consequently, as a next
system enhancement, it is thought that integrating the aggregated load curves at FBs and feeder level
would be useful for Field Crews. Moreover, user feedback highlighted the length of time required to
receive a polled smart meter values (approximately 3 minutes). An opportunity exists to interface
directly to the AMI system to shorten the time of reply. In the demonstrator it was decided to use the
interface OMS-AMI because it existed before and it was considered as an opportunity to build the
architecture over that infrastructure.
As evaluation of the new analytics tool designed for reporting user friendly data from the LV NMS
databases and other data sources, two geospatial analyses (GSA) have been implemented. These
analysis access databases [1] (spatial data about SSs or FBs) and LV NMS (non-spatial data about smart
meter events or incidents) and combine them to provide useful geospatial reports. Figure 32 and Figure
33 show examples of the customised geospatial analysis.
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FIGURE 32: GEOSPATIAL ANALYSIS TOOL (GSA) REPRESENTATION EXAMPLE. LEGEND: SS = STARS, FUSE BOXES = CIRCLES
WHICH SIZE DEPEND ON THE NUMBER OF SMART METER EVENTS, LV FEEDERS = GREY SEGMENTS)
FIGURE 33: GEOSPATIAL ANALYSIS TOOL (GSA) REPRESENTATION EXAMPLE. SS’S ARE CIRCLES WHICH SIZE DEPEND ON THE
NUMBER OF LV NMS INCIDENTS AND GRAPHS WITH INCIDENT CATEGORIES CLASSIFICATIONS
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Table 10 seeks to reflect a summary of the main improvements process related regarding the LV NMS
integration with other systems and the business benefits that pose.
TABLE 10: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE LV NMS
INTEGRATION WITH EXISTING SYSTEMS
Improvements process related Business benefits - Integration with MV SCADA in the diagram
visualization (e.g. Figure 66). - Integration with MV OMS in the diagram
visualization. - Integration of Consumer call information in
the diagram visualization. - Integration of smart meter event information
in the diagram visualization (e.g. Figure 68). - Integration of smart meter instantaneous
values (e.g. Figure 70 and Figure 72). - Integration with MV system (energised state
of the distribution transformer, e.g. Figure 67).
- Integration with Iberdrola’s OMS system to report incident information when closing the incident.
- Integration with the Planned Work System (GIRED) (e.g. Figure 73).
- Integration with Maintenance Work System (GOT) (e.g. Figure 71).
- Near real time measurements will provide a
more informed view of the state of the network to the user (both Desktop and
Mobile). - Enhancement of the decision making process
and therefore, the QoS. - Reduce the incident resolution time and
therefore, the QoS. - Planned work also managed from LV NMS.
The visualisation of the planned and unplanned work on the same tool provides a better understanding of the state of the network to the LV NMS user.
3.2.4 LV INCIDENT MANAGEMENT: LV O&M
The validation process regarding LV incident management has been focused on analysing the 583 LV
incidents registered in the LV NMS and the 52 LV incidents managed by Field Crews with the LV NMS
Mobile solution. All information related to these incidents has been extracted into an Excel sheet (Figure
16) for an easier data management. The main issues explored are as follows:
Period of time between an incident creation due to a smart event registration and the first
Consumer call related to that incident.
Clearance of the LV incident responsibility.
Total LV incident duration.
Improvement on the LV incident scope (reports).
As evaluated in section 3.2.3, the LV NMS is able to create a LV incident register with a Consumer call
and/or a smart meter event. In the latter case, the incident could have happened without being known
by the Consumer (e.g. for being out of home). An earlier start to restore the electricity, and even the
information (about the incident) that, under this scenario, the DSO could be able to provide to the
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affected Consumer when calling reporting it, are examples of values added that the demonstrator
developments can offer to Consumers. Based on the analyses of the registered incidents that have
events and calls grouped (30 incidents), this differential time that would be saved on the total time of
the LV incident would be, in average, 36 minutes (min.).
Not all calls received from Consumers are related to LV incidents under the responsibility of the DSO
(e.g. downward the smart meter). The call centre pre-filter them but still some of them arrived to the
DSO. The capability developed in the LV NMS Mobile solution to perform smart meters and supervision
meter measurements on demand (ping) allows Field Crews to determine if the incident gets under their
responsibility. This avoids unnecessary Filed Crews displacements in case the incident is not
responsibility of the DSO, what means a saving in time and allows a much optimal use of resources,
having Field Crews much available to respond to incidents. This should provide better service to the
Consumer. Based on the demonstrator experience, 22 out of the 52 incidents managed with the Mobile
solution were not responsibility of the DSO. Using the on demand request of meter measurements, the
Field Crew avoid all these displacements.
In Chapter 2 and in the present one (in more detail in [1][2]), a series of LV NMS capabilities have been
described and how they can impact of the incident time restoration explained. Based on the operational
experience of using the developed system to managed LV incident during the demonstrator, it has been
observed that this time have been reduced in 16 min. This value has been obtained comparing 30
incidents managed with the LV NMS Mobile solution and similar ones in both, season and description,
before using the LV NMS. It is worth mentioned that as time goes a more incidents are managed with
the Mobile solution, more accurate results can be obtained. Moreover, it is believed that the
implementation of the potential improvement received from system users during the demonstrator and
the ending of devices deployment ongoing and its affective integration into the system architecture will
improved even more this timing.
The accuracy on reporting Consumers affected per incident has been evaluated as well. 212 incidents
registered in the LV NMS have been compared with the same incidents but in this case how they would
have been registered in the current incident reporting system. Based on this analysis, the reporting
accuracy has been 32%16. It worth mentioned that this procedure would be improved in the short-term
when the Iberdrola connectivity solution is fully deployed at field. Moreover, tests performed on the
reporting generation process, have allow observing that the consumer information on these reports are
not filled automatically. This is an issue that need to be solved to avoid manual work.
From the feedback received from users who use the Mobile solution at field the following
improvements are identified: being able to record operations on the neutral cable (see section 3.2.1)
16 It has been calculated taken the total time per incident, multiplying the number of Consumers reported (“ambito” in Spanish) by the time of the incident. The result is added up per each incident and divided by the total number of Consumers reported as affected in all incidents (based on the incident reporting system). This is done using the reports before UPGRID and those generated in the demonstrator using the LV NMS.
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and connect Consumers between two phases. In real O&M some field work relies on the use of this
phase for temporal solutions. Moreover, incorporating the capability of performing DPF in the Mobile
solution would provide Field Crews with more flexibility to analysis different scenarios to determine the
most convenient alternative to restore the electricity supply after an incident.
Table 11 seeks to reflect a summary of the main improvements process related regarding the LV
incident management and the business benefits posed.
TABLE 11: SUMMARY OF THE PROCESS IMPROVEMENTS AND BUSINESS BENEFITS IDENTIFIED WITH THE LV INCIDENT
MANAGEMENT
Improvements process related Business benefits
- Manage unplanned LV Outage.
- Manage planned LV Outage. - Investigate LV Outage by requesting on-
demand smart meter values. - Incident completion report. - Feeder re-configuration: Restore supply by
bridging supply from a neighbouring feeder. - Empower field engineers with a mobile
application to access the system. - LV network geospatial analysis and reporting
(e.g. Figure 32, Figure 33 and Figure 75).
- Determine cause of outage and restore power as quickly as possible.
- All relevant information of planned outages will be shown on LV diagram.
- This will speed up the outage investigation and its restoration. This along with the
granularity of Consumers affected per supply point will decrease the Consumers per
minutes lost. - To compare quality of the service (QoS)
against existing system. - Have reports with clear and accurate
information for improve strategic business
decisions.
3.3 OPPORTUNITIES
New opportunities have been identified after analysing the operational field experience of the system
which is as follows:
Using the UPGRID Spanish demonstrator as a reference, extending the network area (i.e. add new
regions or cities) covered by the LV NMS is possible, but some issues identified during the use of the
system should be managed in advance.
It could be possible to improve the incremental data load process identifying in advance which SS
have been modified by users in UPGRID. In this way, a new process could use this information to
filter specific network parts for incremental load (such module for filtering parts of the network has
been developed already in the demonstrator).
Periodic Consumer smart meter readings could be utilised to create load profiles in the LV NMS
system. This could be a monthly update where data collected for a specific month would be fed into
the existing load profile for the associated FB for that month. A better modelling of the load on the
LV network will lead to more power flow studies accurate results.
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During the demonstrator the field work orders (e.g. notifying the closer Field Crew to manage a LV
incident) have been dispatched to the LV NMS Mobile devices manually. An opportunity exists to
automate this process. This should lead to efficiency improvements.
The collaborative methodology between Field Crews could be enhanced, allowing dispatching and
taking back field work orders amongst them using the LV NMS Mobile device without inputs from
the control centre. Being the auto-dispatching of LV field works on of the most relevant opportunity.
That is, Field Crews will receive automatically LV the field work request automatically without the
intervention of the control dispatch. This feature, together with previous points, would enhance the
distributed O&M LV business process distributed approach (see section 8.1).
Implement enhancements to existing interfaces with Iberdrola own systems to allow the
incremental updates of Consumer and meter data, which does not reside in a GIS database.
New functionality could be made available to Field Crews on the LV NMS Mobile solution such as the
ability to run power flow studies and create annotations on the LV diagram.
New functionality could be made available so that LV NMS system automatically gathers
instantaneous smart meter values when incidents are predicted on the network (as a result of
Consumers calls or smart meter events received).
The architectural design of the interfaces (developed in the demonstrator) that allows the LV NMS
interaction with existing Iberdrola systems would need revisiting if this were to be extended. For
example, in order to request spontaneous smart meter values, the interface LV NMS - AMI is not
directly stablished to the latter system, adding a delay of approximately 2-3 minutes in getting back
the smart meter response.
New data sources can be easily added to the analytics tool geospatial so reports can be updated with
new data very quickly. For example, they can be updated with historic data generated along LV NMS
production use, generating more accurate and useful reports.
3.4 CONCLUSIONS
The quality of the GIS source is a requisite for a successful import of the LV network. Using CIM, which
loads LV network circuits rather than individual components, the correct connectivity of the elements in
the source GIS is vital. Based on the demonstrator experience, where the connectivity has some
inconsistencies, the elements are not loaded on the diagram and can lead to a substantial amount of re-
work and analysis. Sanity checks on the GIS source should take place prior to the network loading
exercise, and continue along this iterative process.
After the CIM files were loaded in the LV NMS, no manual tidy up of the imported network took place.
The network shown is how it was loaded in CIM, with the ability to run traces straight away on a newly
loaded circuit. This highlights the robust design of the modelling process.
In order to speed up the incremental updates of the LV network the GIS source should be able to
produce the ‘delta’ of the changes as they happen. Having a modelling tool where different snapshots of
the GIS system are loaded and compared in order to produce the ‘delta’ has proven difficult and time
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consuming. The source GIS system should have this capability if incremental updates of the LV network
were needed. The large number of SSs loaded allows having a realistic estimate of the effort involved
when extending the geographical scope of the LV network, relying on similar quality of the GIS data.
Given that the LV network extension covered by the LV NMS was doubled during the life of the
demonstrator (Figure 9), it has been demonstrated the capacity to scale this solution.
The deployment of multiple interfaces between LV NMS solution and Iberdrola’s existing systems
provides the LV NMS user with relevant information such as planned work, MV measurements, smart
meter events and smart meter polled values. Some of them are real time interfaces and are reflected on
the LV diagram. The LV NMS user, either using the Desktop or Mobile solution, has a full understanding
of the real status of the LV network at any point in time. The LV diagram conveys visual information to
the user who is better equipped to take decisions regarding the network operation. Plus the ability to
poll the smart meters on site from the mobile device enhances the information. Moreover, having a real
time LV network, where temporary operations like cuts and jumpers are maintained and gives an
accurate reflection of energised and de-energised sections of the network for instance helps the O&M
of the LV network. The granularity of the consumer supply points allows the system to accurately
predict the number of Consumers affected after LV incidents. GSA allows O&M responsible to generate
new spatial reports with multiple data sources. This is an improvement that can be used to take more
justified business decisions or a better LV network management.
The results obtained so far regarding LV incident management using the LV NMS show encouraging
insight to back the expectations articulated at the beginning of the demonstrator and there is still room
for improvement and continue working in this direction. Thanks to the feedback received from users
who has used the solution, the system will be improved adding new capabilities.
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4. USABILITY OF LV SMART METERING PRIME TECHNOLOGY
FOR REMOTE CONTROL
The main objective of this chapter is to report on the usability of the LV smart metering PRIME
technology for remote control. First, the introduction provides information to understand main aspects
developed in the demonstrator with this regard. Second, field test results and conclusions are
presented. Annex II, Annex III, Annex IV and Annex V contain more testing details.
4.1 INTRODUCTION
The Spanish demonstrator has two goals in this area. On the one hand, to define the architecture that
allows multiple applications (smart meter management and remote control) on top of a PRIME
subnetwork. On the other hand, to deploy a PRIME Management system (software solution) to monitor
the basic performance parameters of a PRIME subnetwork.
The main result has been the implementation of LV infrastructure remote control over PRIME. This
deployment is based on two lines of work: PRIME as a multiservice subnetwork and PRIME as a
subnetwork than needs to be monitored. It is worth mentioning that the monitoring capability is the
initial requirement to integrate remote control over PRIME.
In the scope of the demonstrator a new generation of devices has been designed and developed aimed
at including, in a compact device which can be plugged in the LV network, IP capabilities over PRIME
network. This equipment is named PRIME Gateway (GTP)17. One of the most interesting applications
provided by IP traffic over PRIME in LV network is remote control. Based on laboratory test results
already conducted [1], it would be feasible to use current PRIME network in order to transmit AMI
(metering) and remote control traffic. Field tests of section 4.2 pretend to confirm these results.
Three different use cases [2] are identified in order in order to evaluate multiservice PRIME subnetwork
performance:
TABLE 12: USE CASES FOR FIELD TESTING
Use case Objective
Use case 1: SS with existing Remote
terminal Unit (RTU)
Measuring the performance of an existing RTU, previously installed in a SS, but now replacing the current transmission media (Ethernet) with PRIME.
This means that the RTU traffic (IEC 60870-5-104) is transported over PRIME. Figure 34 and Figure 35 show installation details.
17 These GTP devices are currently in homologation and acceptation phase. This is required to be fully integrated on the grid. Then, they can be only powered-on for punctual testing within the demonstrator use case described in this section.
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Use case Objective
Use case 2: SS without remote
access
Testing the IP over PRIME capability in terms of bandwidth of IP over PRIME. This would be the transmission mean for the information exchanged
between the AMI Head System and the AMI data concentrator in the SS. This simulates the case of a SS that does not have enough GPRS/3G coverage to let the router establish a good connection with the AMI Head System.
Use case 3: LV backup feeder smart-
switch application
Testing a remote control application to be applied for LV backup lines
switching from one SS to an alternative one. It is referenced as LV backup feeder smart-switch application. This is an alternative future approach that would be applicable in scenarios where mesh LV networks are available.
FIGURE 34: SS NETWORK ARCHITECTURE WITH AN EXISTING RTU (USE CASE 1). INITIAL SCENARIO (ON THE LEFT).
SCENARIO THAT INCLUDES GTPs TO TEST REMOTE CONTROL TRAFFIC OVER PRIME (ON THE RIGHT). CCT = DATA
CONCENTRATOR, IBD = IBERDROLA
FIGURE 35: PRIME GTPs INSTALLED IN A SS
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PRIME PLC specification covers different convergence sublayers definition so different types of traffic
sending are optimized. PRIME 432 convergence sublayer is the most widely deployed as it covers AMI
traffic. Now, there is another sublayer, IPv4 convergence sublayer that was originally defined in PRIME
to send multi-service data. This means that it was left open for future traffic requirements within the
PLC channel. Now, in the scope of UPGRID project, it has been made the development required in order
to support this IP over PRIME transmission (that was already technically described in the specification).
And going one step further measured the performance of this multi-service data transmission option
and how did it impact in current AMI traffic being exchange. Over this development, as a particular case
of multi-service data, remote control traffic is transmitted. For tests oriented to this specific service it
was found during the project that this profile needed specific capabilities that in order to ensure
interoperability will be agreed and specified within the PRIME Alliance [14] based on the ticket opened.
The PLC PRIME subnetwork deployed for smart metering applications is a communication channel that
needs to be monitored. This requirement becomes even more important in the context of the PRIME
multiservice network when new applications are integrated on it. Then, the demons trator has extended
the PRIME subnetwork monitoring capabilities as follows:
Software (SW) evolution that has been installed in 40 AMI data concentrators devices covering
around 14.000 smart meters in the demo area. The SNMP PRIME Management Information Base
(MIB) has been defined and implemented successfully.
A SNMP web tool (i.e. Network Management System) for collecting MIB has been specified,
designed, and installed at Iberdrola premises. It gathers all relevant information from the PRIME
PLC subnetworks connected to.
FIGURE 36: SCREENSHOT OF THE SNMP WEB TOOL INTERFACE - DATA ACCESS SHOWING NODES CONNECTED IN THE
DEMONSTRATION AREA. NUMBER OF TERMINALS (IN GREEN). NUMBER OF SWITCHES (IN BLUE)
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FIGURE 37: SCREENSHOT OF THE SNMP WEB TOOL INTERFACE – CONFIGURATION MENU
The rest of the chapter is divided in two parts in order to evaluate separately the two main goals of the
Spanish demonstrator regarding the PRIME functionalities as introduced above.
4.2 MULTISERVICE PRIME SUBNETWORK (LV REMOTE CONTROL OVER
LV SMART METERING PRIME TECHNOLOGY): EVALUATION AND
CONCLUSIONS
The goal is to define an architecture that allowed multiple applications (e.g. smart meter management
and remote control) on top of a PRIME subnetwork. This section describes what this approach is about,
the benefit compared with previous solutions, tests performed and results of the field deployment done
within UPGRID Spanish demonstrator. The chapter ends with an analysis of the main benefits and its
applicability based on the results obtained. Annex II and Annex III complement the tests with more
detailed data and information.
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4.2.1 LV CONTROL TRAFFIC OVER PLC PRIME - FIELD VALIDATION CONDITIONS
In the scope of the demonstrator a new generation of devices has been designed, named PRIME GTP.
This is a compact device which can be plugged in LV network that in order to enable a multiservice
PRIME subnetwork, implements IP capabilities over PLC PRIME. This IP traffic over PRIME functionality
enables the remote control service that needed to be deployed within UPGRID project. Once validated
in laboratory, field scenarios (Table 12) were selected in order to transmit simultaneously AMI
(metering) and remote control traffic over the same PRIME network.
This deployment requires the installation of multiple PRIME GTP devices . The number and topology of
these deployments are described in the following sections.
In order to clarify field deployment conditions, it is worth noting that permanent installation of devices
in the Iberdrola’s LV network requires a previous homologation and acceptation process. The GTPs are
prototypes developed within the scope the UPGRID Spanish demonstrator and they are in the
homologation phase. Therefore once they were installed, they have been only powered-on for testing
purposes within the project scope. Also, two UPGRID portable testing cabinet types have been designed.
Finally, the LV remote control deployment plan designed initially needed some adjustments. It has
implied integrating these new capabilities into an AMI system already developed and in operation.
Moreover, field SCADA system accessing RTUs have some security conditions that need to be met.
Therefore, this implied some addressing, configurability and remote control traffic exchange limitations
for UPGRID project deployment. Test setups and results are described below.
4.2.2 UPGRID CABINET FOR LV REMOTE CONTROL - FIELD DEPLOYMENTS
4.2.2.1 UPGRID CABINET MODEL 1: INTEGRATING A PAIR OF PRIME GATEWAYS (GTP)
This first cabinet model is used for use case 1 which aim is to measure the performance of an existing
RTU, previously installed in a SS, but now replacing the current transmission media (Ethernet) with
PRIME. Figure 38 shows the portable cabinet type 1 installed in field deployment that has been used for
the LV remote control over PRIME field test scenarios.
Internally, this cabinet has two PRIME base node (PBNs) installed, one of them has a master (base node)
role and the second one has a slave (service node) role. In the scope of UPGRID project the test replaces
the local Ethernet communication with the RTU with an IP over PRIME connection enabled by the two
PBNs installed in the UPGRID portable cabinet.
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FIGURE 38: PORTABLE CABINET TYPE 1 INSTALLED IN FIELD DEPLOYMENT
4.2.2.2 UPGRID CABINET MODEL 2: INTEGRATING A SINGLE PRIME GATEWAY (GTP)
This second cabinet model is used for use case 2, whose aim is to simulate SS locations where the SS
does not have enough GPRS/3G coverage to let the router establish a good connection with the AMI
Head System. In this context GTP capabilities provide an IP connection over PRIME network to an
alternative location (such as a meter room nearby), from which the GTP will be the one making the
bridge to GPRS/3G connecting with the AMI Head System. Figure 39 shows the portable cabinet type 2
installed in field deployment that has been used for the LV remote control over PRIME field test
scenarios.
FIGURE 39: PORTABLE CABINET TYPE 2 INSTALLED IN FIELD DEPLOYMENT
Two GTP devices with service and base node roles
Mounted in a portable cabinet
One PBN GTP device configured as base node
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4.2.3 USE CASE 1: SS WITH EXISTING RTU - RESULTS, PERFORMANCE AND TOOLS
4.2.3.1 TEST SETUP AND CONDITIONS
This scenario has been selected in order to measure the performance of an existing RTU, previously
installed in a SS, but now replacing the current transmission media (Ethernet) with PRIME. This means
that the RTU traffic (IEC 60870-5-104, hereinafter called 104) is transported over PRIME, instead of over
Ethernet.
In this scenario, RTU traffic and AMI traffic will be sharing the PRIME channel . RTU traffic is routed
through the GTP (acting as service node, slave role) via PRIME and AMI traffic from meters is managed
by the second GTP (acting as base node, master role). Tests under this scenario combine RTU traffic in
presence of AMI traffic.
FIGURE 40: BASIC UPGRID ARCHITECTURE OF REMOTE CONTROL OVER PLC PRIME TESTING
There are some requirements in order to select field locations for remote control over PRIME UPGRID
deployment. Two SS with a RTU already installed and integrated in the SCADA system were required.
These two SSs within the scope of the demonstrator area are selected, SS TORRE ABANDOIBARRA 2 and
TORRE ABANDOIBARRA 1.
In the scope of UPGRID project the first step consisted in replacing the local Ethernet communication
with the RTU with an IP over PRIME connection enabled by the two GTPs installed in the UPGRID
portable cabinet. The designed architecture can be seen in Figure 41. GTP A and GTP B have been
installed in the field using a cabinet specially designed for the UPGRID project, cabinet type 1 described
in the section above. It includes GTP A, which is connected to Ethernet port of the SS switch, and GTP B,
which is connected directly to the existing RTU.
Remote control in the Secondary Substation – Original topology
Switch RTU
Ethernet
Remote control in the Secondary Substation – UPGRID test control over PRIME
Switch RTU
GTP Role: base
GTP Role: service
IP over PRIME
LV PLC
Ethernet Ethernet
Remote
control with SCADA
Remote control with
SCADA
UPGRID portable cabinet type 1
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FIGURE 41: UPGRID INSTALLATION AT TORRE ABANDOIBARRA 2 SS
FIGURE 42: IMAGES OF THE REMOTE CONTROL TRAFFIC TEST PERFORMED AT TORRE ABANDOIBARRA 2 SS
It is worth mentioning that the RTU simulates any LV controllable device in the grid because, at the
moment, there are not such devices in the demonstrator area. Then, this is a proof of concept.
Although the designed architecture included remote access from the operation SCADA to the RTU, there
were some addressing and configurability limitations in the deployment systems that made this
approach unfeasible. A summary of the implemented measures required for the tests are presented
next. They are divided in two parts based on the test objectives. Test plan and execution is detailed in
Annex II.
Part_1: Analysis of PRIME PLC performance sharing AMI traffic and IP over PRIME traffic. The
goal of this initial test plan is to evaluate the channel usage sharing the media between AMI data
and remote control data.
- Measure_1: Force AMI traffic and check that results are successful. First iteration is done
without IP over PRIME traffic in parallel. AMI traffic is forced uploading an eXtensible
Markup Language (xml) cycle to the AMI data concentrator.
- Measure_2: Force IP over PRIME traffic and measure the maximum delay of Internet
Control Message Protocol (ICMP) traffic sent. First iteration is done without AMI traffic in
parallel.
GTP B (role
service)
GTP A (role base)
RTU
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- Measure_3: Force AMI and IP over PRIME traffic in parallel and compare the performance
with the two measurements taken with only a type of traffic each time.
Part_2: Analysis of remote control 104 traffic exchange over IP over PRIME. - Measure_1: Force IP over PRIME traffic with ICMP packets to make sure the channel is
established.
- Measure_2: Connect the local SCADA to the RTU through IP over PRIME. This would validate
UPGRID concept for remote control traffic exchange over PLC PRIME. 104 control traffic is
starter although due to addressing limitation further exchange is rejected. Anyway,
bidirectional 104 traffic is exchanged and the concept is validated.
- Measure_3: Connect the local SCADA an RTU simulated in a second PC (WinPCPau18 test
tool).
4.2.3.2 TEST RESULTS AND PERFORMANCE
This section summaries the test results and the performance obtained at field.
Part_1: Analysis of PRIME PLC performance sharing AMI traffic and IP over PRIME traffic.
- AMI traffic in field locations selected with UPGRID GTP portable cabinet installed is
successful. When no IP over PRIME traffic is simultaneously sent, AMI performance is not
affected. This is the expected result and it proves that the implementation is correct and no
regressions are found.
- IP over PRIME traffic in field locations selected with UPGRID GTP portable cabinet forced by
ICMP packets is successful. Answer time with IP over PRIME traffic never exceeds 450
milliseconds (ms). This performance is stable in the time. This proves that multiservice IP
over PRIME exchange over UPGRID implemented GTP devices is successful.
- When both AMI and IP over PRIME traffics are forced to be exchanged in parallel the
performance varies. This test aims to evaluate this impact and confirm if the performance is
good enough for a further deployment. Based on the results presented below, traffic
exchange is successful and the concept is validated. Then, both types of traffic can coexist.
o IP over PRIME traffic with AMI traffic in parallel maintains the same latency 450 ms
with punctual packets arriving around 1.500 ms.
o The results of AMI readings during this test are also successful.
Part_2: Analysis of remote control 104 traffic exchange over IP over PRIME.
- IP over PRIME traffic with ICMP packets are exchanged successfully so the channel is
established. Under these testing conditions there is no packet los s and the average loop
time is 292 ms. This is a good performance for IP over PRIME in field conditions.
- The local SCADA is connected to the RTU through IP over PRIME. Although due to
configurability and addressing limitations of both the RTU and SCADA sys tem successful 104
traffic exchanges is not possible during the testing. Anyway, bidirectional 104 traffic is
18 This is a software tool developed by Iberdrola to perform RTU point -to-point tests monitoring different protocols traffic.
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exchanged and the concept is validated. The result is the same with a RTU simulated in a
second PC (WinPcPau test tool) so only 104 rejection traffic is exchanged.
- 104 traffic exchanges over IP over PLC PRIME are validated although further performance
measurements could not be taken in an operation environment.
4.2.4 USE CASE 2: SS WITHOUT REMOTE ACCESS - RESULTS, PERFORMANCE AND TOOLS
4.2.4.1 TEST SETUP AND CONDITIONS
The aim is to simulate SS locations where the SS does not have enough GPRS/3G coverage to let the
router establish a good connection with the AMI Head System. In this context GTP capabilities provide
an IP connection over PRIME network to an alternative location (such as a meter room nearby), from
which the GTP should be the one making the bridge to GPRS/3G connecting with the AMI Head System.
This scenario has been selected in order to test the capability, in terms of bandwidth, of IP over PRIME.
This would be the transmission medium for the information exchanged between the AMI Head System
and the AMI data concentrator in the SS. Figure 43 shows the network architecture in an SS representing
the test setup to be validated within the demonstrator. Originally, a 3G or GPRS router is the network
element responsible for connecting the AMI Head System with the AMI data concentrator. AMI data
concentrator is the Base Node in PRIME network, and there are only meters as PRIME Service Nodes.
This architecture offers an alternative Wide Area Network (WAN) access through a meter room offered
by the UPGRID GTP as described above.
FIGURE 43: ARCHITECTURE FOR USE CASE 2 SS WITHOUT REMOTE ACCESS DEPLOYMENT (CCT = DATA CONCENTRATOR,
GTP = PRIME GATEWAY, METER = SMART METER)
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FIGURE 44: USE CASE 2 TEST ENABLING WAN ACCESS FROM THE METER ROOM OF GERNIKAKO LORATEGIA 3
The IP traffic exchanged between AMI data concentrator and AMI Head System is:
- Hypertext Transfer Protocol (HTTP) traffic to access the AMI data concentrator web page. - HTTP traffic to transmit and receive Web Services.
If the SS does not have enough GPRS/3G coverage to let the router establish a good connection with the
AMI Head System, DSO would have to deploy an alternative transmission method (often expensive),
such as a proprietary optical fibre link. However, using the GTP capabilities to provide an IP connection
over PRIME network, DSOs can avoid the investment in other transmission methods.
Field testing requirement was one SS with a real or simulated coverage issues that would stop remote
communication. This SS should have a suitable meter room with enough space for an UPGRID portable
cabinet installation. Remote access to elements in the SS (any element such as remote control) would be
offered from the meter room. MIRIBILLA 6-BILBAO SS and its meter room Gernikako Lorategia 3 are
selected within UPGRID demonstrator area for this deployment.
A summary of the implemented measures required for the tests are presented next. Test plan and
execution is detailed in Annex III.
- Measure_1: Remote access to the AMI data concentrator from the meter room where the GTP
with WAN access is installed. This is IP over PRIME traffic.
- Measure_2: Remote access to the AMI data concentrator from the meter room where the GTP
with WAN access is installed while AMI reading data is exchanged. This means that IP over PRIME
traffic and AMI traffic are exchanged simultaneously.
- Measure_3: Remote access to the GTP with WAN access installed in the meter room from the
AMI operation system at Iberdrola premises. This ensures the last step of WAN remote
accessibility.
- Measure_4: Remote access to AMI data concentrator from the AMI operation system at
Iberdrola premises, being the first step the WAN connection to the GTP with WAN access
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installed in the meter room. This measurement is not possible due to routing limitations in
Iberdrola field operation networks.
4.2.4.2 TEST RESULTS AND PERFORMANCE
Based on the main measures to be taken, listed in the section above, and detailed in Annex III, this
section provides a summary of the test results and performance obtained from field testing.
- Remote access to the AMI data concentrator from the meter room where the GTP with WAN
access is installed is possible. Web server accesses are done using IP over PRIME channel and the
performance is slow but successful. This was the expected result so UPGRID concept is validated.
- ICMP packets are sent to the AMI data concentrator from the meter room where the GTP with
WAN access is installed. AMI reading data is exchanged in parallel, so IP over PRIME traffic and
AMI traffic are exchanged simultaneously. This latency test shows that access is possible
although there are some packets that do not arrive to the destiny (13% of the packets in this
scenario are lost). PLC PRIME channel sharing has an impact in the time performance of IP over
PRIME. From the measurements we take loop packet times from a minimum of 325 ms to a
maximum of 3.775 ms. Being the average 1.287 ms. This matches with the expected result,
simultaneity of traffic exchange over PLC PRIME is possible but bandwidth is limited so time
performance is affected.
- Finally, remote access to the GTP with WAN access installed in the meter room from the AMI
operation system at Iberdrola premises is validated. This ensures the last step of WAN remote
accessibility.
4.2.5 USE CASE 3: LV BACKUP FEEDER SMART-SWITCH APPLICATION - LIMITATION
FOUND IN THE DEMONSTRATOR AREA
This scenario has been analysed in order to test a remote control application to be applied for LV backup
feeder switching from one SS to an alternative one. It is referenced as LV backup feeder smart-switch
application. The approach followed here is to use remote control application (IP over PRIME) in order to
manage this feeder switching and therefore SS switching.
This is an alternative future solution that would be applicable in scenarios where mesh LV networks are
available. It requires SS with LV backup feeders where remote control for smart-switching is applicable.
This means that there should be LV points in the network where a second LV feeder from a backup SS
arrives. This kind of LV grid allows switching to a backup SS in case of supply faults or anomalies in its
main SS.
The application connects an IP smart-switch to a GTP device (with IP over PRIME enabled). Therefore
operates the smart-switch over IP over PRIME remotely and makes a LV backup feeder switch. Figure 45
shows the topology required in order to test this LV backup feeder smart-switch application.
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FIGURE 45: LV GRID TOPOLOGY WITH BACKUP FEEDER REQUIRED (FB = FUSE BOX, SS = SECONDARY SUBSTATION)
This LV smart-switch application requires the installation of UPGRID cabinets with GTP devices and IP
smart-switches in intermediate LV feeder points where two feeders from independent SS meet. This grid
topology condition is too specific and therefore it was difficult to deploy this scenario for testing in the
UPGRID Spanish demonstrator area.
FIGURE 46: LV GRID TOPOLOGY AVAILABLE IN THE DEMONSTRATOR AREA (FB = FUSE BOX, SS = SECONDARY SUBSTATION)
This alternative was presented and explained in [2]. However, all SSs analysed in the demonstrator area
have the structure shown in Figure 46, which is not compatible with the required architecture for testing
use case 3. This has been checked during field visits. For example, Figure 47 shows images taken during
a field visit to VALENTIN DE BERRIOTXOA SS where field deployment of this use case was discarded.
Therefore, although this is an interesting application for LV remote control over IP over PRIME it has not
been deployed and tested at field in the scope of UPGRID project.
FIGURE 47: FIELD VISITS TO VALENTIN DE BERRIOTXOA SS WHERE USE CASE 3 DEPLOYMENTS IN THE FIELD WAS
DISCARDED
SS SS FB FB
SS SS FB FB
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4.2.6 APPLICABILITY AND NEW OPPORTUNITIES
This section analyses how a multiservice PRIME subnetwork could be implemented today in field
operation. Short-and medium-term opportunities and benefits are identified as well. These concepts,
once validated, have a direct applicability.
- UPGRID actions that enable a multiservice PRIME network using IP over PRIME allow taking
advantage of the telecommunications infrastructure that is deployed for smart metering
purposes.
- Smart metering is the first application. Then actions are being done in order to support further
functionalities such as remote control in LV. These PLC networks provide a PLC-based
“coverage” area, linked to the LV grid that is able to support additional services and applications
apart from the feature of managing smart meters. Other smart grid related applications, such as
DER integration and electric vehicle (EV), may use the same telecommunications network as
well.
- IP over PRIME development done in order to support remote control over PLC is a standard and
interoperable initiative. PRIME PLC specification covers the multiservice premises it; minor extra
requirements identified are in standardization progress within the PRIME Alliance.
- Three applications (use cases) are analysed within UPGRID, demonstrating the two first. This
feasibility of remote control traffic using IP over PRIME is an opportunity to extend other
concepts.
- Application 1: PLC PRIME allows the transfer of control traffic for remote control applications
coexisting with metering applications. Communicate with an RTU, previously installed in a SS,
but now replacing the current transmission media (Ethernet) with PRIME. This means that the
RTU traffic (IEC 60870-5-104) is transported over PRIME instead of over Ethernet.
- Application 2: PLC PRIME allows the remote access to a SS without WAN coverage through a
meter room with WAN coverage. Extracting metering and control data of the SS though IP over
PRIME to another location where WAN access is possible.
This is applicable in situations where the SS does not have enough GPRS/3G coverage to let the
router establish a good connection with the AMI Head System. Here the DSO has to deploy an
alternative transmission method (often expensive), such as a proprietary optical fibre link.
However, using the GTP capabilities to provide an IP connection over PRIME network, DSOs can
avoid the investment in other transmission methods.
- Application 3: This remote control application can be applied for LV backup feeder switching
from one SS to an alternative one. It is referenced as LV backup feeder smart-switch application.
The approach followed here is to use remote control application (IP over PRIME) in order to
manage this feeder switching and therefore SS switching.
This is an alternative future approach that would be applicable in scenarios where mesh LV
networks are available. It requires SS with LV backup feeders where remote control for smart-
switching is applicable. This means that there should be LV points in the network where a
second LV feeder from a backup SS arrives. This kind of LV grid allows switching to a backup SS
in case of supply faults or anomalies in its main SS.
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4.3 MANAGEABLE PRIME SUBNETWORK (SNMP MONITORING):
EVALUATION AND CONCLUSIONS
The goal is to deploy a PRIME Management system (software solution) to monitor the basic
performance parameters of a PRIME subnetwork. This monitoring capability is the initial requirement to
integrate remote control over PRIME as described in 4.2. This section describes what this approach is
about, the benefit compared with previous solutions, tests performed and results of the field
deployment done within UPGRID project. The chapter ends with an analysis of the main benefits and its
applicability based on the results obtained. Annex IV and Annex V complement the tests with more
detailed data and information.
4.3.1 REQUIREMENTS FOR A MANAGEABLE PRIME SUBNETWORK
PLC PRIME subnetwork deployed for smart metering applications is a communication channel that
needs to be monitored. This is even more important for new applications integration in the multiservice
network concept introduced in the section above.
This PRIME subnetwork monitoring capability implies:
- Software (SW) evolution in the data concentrators deployed in the field. SNMP PRIME MIB has
been defined and implemented in incremental phases during the lifetime of demonstrator. This
network management functionality is implemented as a firmware version that can be installed
and validated in AMI data concentrators already deployed in UPGRID demonstrator area.
- A web tool or SW System (Network Management System) for collecting MIB was specified,
designed, constructed and tested. This web tool has been already installed in Iberdrola premises.
It is connected to the internal Base Nodes of AMI data concentrators and explores its data.
Therefore, the deployment tested in this section is based on the installation of a new firmware version,
in a set of field AMI data concentrators that supports SNMP PRIME MIB. All of these devices are
hardware devices in operation within the demonstrator area that required a SW evolution that was
designed and developed within the scope of UPGRID project. No field action is required for this
deployment as configuration and monitoring is done remotely (saving Field Crews movements). Figure
48 shows the architecture of this field deployment.
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FIGURE 48: FIELD SS MONITORED SNMP WEB TOOL (CCT = DATA CONCENTRATOR, IBD = IBERDROLA, FW = FIRMWARE, SS
= SECONDARY SUBSTATION)
4.3.2 FIELD VALIDATION CONDITIONS - RESULTS, PERFORMANCE AND TOOLS
The scope of the deployment has covered:
- 40 field AMI data concentrators are selected in the demonstrator area (i.e. Bilbao and
Baracaldo).
- This covers 14.000 smart meters (this is the number of Consumers in the demonstrator area that
can benefit from the advanced data analysis that SNMP monitoring offers ).
- UPGRID SNMP PRIME MIB is installed and enabled AMI data concentrators.
- AMI data concentrators are integrated for data collection from the UPGRID web tool
development.
- No field visit required: configuration and monitoring is remotely done.
Based on the specification described in [2], a software evolution is implemented for ZIV PRIME data
concentrators. The internal Base Nodes of these devices are able to offer the required information so
PRIME subnetwork can be monitored. For this use of case, following MIB parameters have been
recovered:
Number of Terminals19 in the subnetwork -> OID: 1.3.6.1.4.1.15732.23.1.2.0 – Unsigned 32
Number of Switches in the subnetwork -> OID: 1.3.6.1.4.1.15732.23.1.3.0 – Unsigned 32
19 Terminals and Switches are status defined in the PRIME specification.
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As said before, in the UPGRID demonstrator, this tool has monitored 40 devices, all of them installed in
field and accessible from the Iberdrola office. Next are the steps that had been followed to gather data
from the Base Nodes:
- First of all field data concentrators are upgraded and configured in order to enable their PRIME
advanced SNMP monitoring capabilities.
- Then, it was necessary to provision the 40 Base Nodes in the tool (see Figure 49 and Figure 50).
FIGURE 49: SCREENSHOT OF THE SNMP WEB TOOL PROVISIONING INTERFACE
FIGURE 50: SCREENSHOT OF THE SNMP WEB TOOL PROVISIONING INTERFACE II
- After that, the available operations as scheduling tasks/actions have been configured. In the task
scheduler the user is allowed to configure both, how often an operation should be applied and
which smart meters are to be accessed for a specific task. In this case, the periodicity of the
recollection is 5 minutes and the smart meters where all of the 40 Base Nodes (see Figure 51).
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FIGURE 51: SCREENSHOT OF THE SNMP WEB TOOL SCHEDULER INTERFACE
- Once the task is configured, it is possible to find information about the provisioned Base Node.
Two tasks are needed, one for the number of Terminals in the network and the other for the
Switches, both tasks are shown in the image below (see Figure 52).
FIGURE 52: SCREENSHOT SHOWING ADDED TASKS
- Figure 53 is a real example from a data concentrator in the demonstrator area. The number of
PRIME registered nodes (smart meters) – green in the figure - and the number of nodes acting as
repeaters – blue in the figure – are represented. These curves are directly offered from the
SNMP web tool, ensuring its usability.
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FIGURE 53: SCREENSHOT OF RECOLLECTED DATA FROM A REAL DATA CONCENTRATOR. NUMBER OF TERMINALS (IN
GREEN). NUMBER OF SWITCHES (IN BLUE)
All the results from the test are available in Annex IV. The test had been done in 40 data concentrator
during 4 days. Since the data concentrators are installed in field, the access is made via firewall. This
firewall is opened 12 hours and then it has to be re-logged. Because of that, some data is lost and not
shown in the graphics. With collected data it is possible to notice issues in the network such as locations
where there is a high level of noise that blocks the signal (SNR or signal to noise disturbances). This
example is shown in Figure 54 . It is possible to observe how, at certain times, the number of Terminals
(green line) and Switches (blue line) is zero (i.e. both lines fall to zero). This will be the time slot where
the noise is louder.
FIGURE 54: SCREENSHOT OF RECOLLECTED DATA FROM A REAL DATA CONCENTRATOR WITH NOISE ISSUES. NUMBER OF
TERMINALS (IN GREEN). NUMBER OF SWITCHES (IN BLUE)
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Besides the information provided above, Annex V describes in detail all the steps for data collection for
a particular example. The SS chosen for this example is located inside the demonstrator area, in Bilbao,
and it provides service to 220 Consumers.
4.3.3 TEST RESULTS AND PERFORMANCE
The performance results obtained from the SNMP monitoring deployment and analysis done in the
UPGRID Spanish demonstrator are as follows:
- The aim was to propose a SNMP based Network Management System (NMS), and the initial
steps for a prototype implementation over a massive PLC PRIME deployment. This goal is
successfully achieved within UPGRID project.
- In the case of the SNMP based tool, SNMP web tool specification and development has been
successful. SNMP MIB integrated into the AMI data concentrators is accessible from the SNMP
web tool system. This has been validated both in laboratory and in the field (demonstrator area).
Each AMI data concentrator has an internal PRIME base node that manages the network. This
analysis tool makes use of the data provided by the PRIME base nodes and enables a
manageable PLC PRIME network.
- The tool has monitored a significant number of devices of the demonstrator area (about 40),
and, at this stage, with the implementation done, PRIME monitoring is ensured.
- Also, regular AMI operation is improved; monitoring allows detecting real time issues. AMI
deployments monitoring is improved. AMI data concentrator performance information is
available now and included in the SNMP web tool developed for UPGRID project.
- Based on the results and future deployments scheduled, further evolution of the tool would be
done in the coming years (outside the scope of UPGRID project). Note that in the results graphics
shown (Figure 54 and Annex IV) only two of the SNMP objects (i.e. number of Terminals and
Switches) are really stored and represented by the SNMP web tool. This monitoring already
improves the manageability and knowledge of the network. But note that the complete
specification designed within the scope of UPGRID project covers tens of objects that will need
to be integrated in this SNMP web tool in the coming months and years.
4.3.4 APPLICABILITY AND NEW OPPORTUNITIES
This section analyses how a manageable PRIME subnetwork through the SNMP system described could
be implemented today in the field operation. Short-and medium-term opportunities are identified as
well. Then, enabling a manageable PRIME subnetwork has the following applicability and benefits:
- The deployment of SNMP based NMS over PLC PRIME networks allows PRIME network
monitoring and remote management.
- The PRIME subnetwork monitoring capabilities that were designed, developed and deployed
within the UPGRID Spanish demonstrator imply several benefits for the DSO and for the final
Consumer, as indicated in the following points.
- This PRIME monitoring is the first step that enables a PRIME multiservice network. This is a key
enabler for the control traffic exchange over PLC PRIME developed also in the scope of UPGRID
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demonstrator area. PRIME network knowledge helps designing the best approach in order to
introduce LV remote control in addition of AMI regular operation.
- DSOs will benefit as they make the most of the investment done for AMI deployments. These
AMI deployments can be used now for further applications, not only for billing purposes. These
applications are mainly focused on network operation optimization and remote control
capabilities over LV grid.
- PRIME communication channel usage characterization is obtained: The AMI evolution requires a
better knowledge of the communication channel enabled over the LV line with PLC PRIME. SNMP
monitoring offers this information required of PRIME PLC channel and bandwidth usage.
- Moreover, the regular AMI operation is improved, as monitoring allows detecting real time
issues namely:
o AMI deployments monitoring is improved. AMI data concentrator performance
information is available now and included in the SNMP web tool developed for
UPGRID project. This allows the DSO to detect in real time issues and anomalies in the
AMI data concentrators developed in the field.
o In case of faults or anomalies, the evolution of these applications would, in medium
term, reduce the faults clearing time and therefore increase the quality of service
offered by the DSO to Consumers. Note that these anomalies would be mainly related
with LV incidents that would mean that groups of smart meters would be powered-off
and therefore disconnected from the PRIME subnetwork. AMI data concentrators
with this SNMP monitoring functionality enabled will be able to realise those sudden
variations in Number of Terminals in the subnetwork. This information will be
available in the SNMP web tool at the system side.
o Note that this data, once available at the system, could be integrated and correlated
with other events and data already being processed at the DSOs system. Applying
data analytics and correlation of this information, with the geographical location of
those smart meters disconnected, could help locating areas with a high probability of
being affected by a LV incident. Note that this would be realised in the very moment
the fault is occurring, so fault management systems could have valuable information
that would improve their operation. Of course this requires further evolution in the
systems and in many elements to be integrated, but of course these are applications
to be studied and developed in the coming years.
o All this comes with a low cost, as this monitoring is enabled with a SW evolution of
the AMI data concentrators already installed in the field. SNMP web tool for data
correlation is also required.
- Then, the monitoring and information for the DSO will impact directly in a better management of
incidents, having a direct impact into the final Consumer.
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4.4 CONCLUSIONS ABOUT PRIME BASED FUNCTIONALITIES
UPGRID Spanish demonstrator contributions improve PRIME subnetwork capabilities. Solutions
validated in the demonstrator have a direct impact into the DSO operation and final Consumer.
The aim has been mainly to take advantage of the telecommunications infrastructure that is deployed
for smart metering purposes offering further information and services that increase its value and
usability. Developments have been done in order to validate these functionalities (e.g. LV remote
control can be enabled over existing AMI deployments).
A multiservice PRIME subnetwork can be enabled, as demonstrated during the deployment and testing
phase. The conclusions of this characterization determine that IP over PRIME is a feasible alternative to
transport RTU control traffic using PLC PRIME as channel.
- Tests plans and measures have been focused on the feasibility of IP transport over PLC-based
Smart Metering networks.
- The aim was to demonstrate the feasibility of IP transport for remote control applications and
the extra-bandwidth of newly deployed PLC-based networks for smart metering. And this has
been achieved.
- The impact on latency due to sharing PRIME channel between AMI data and IP data is
acceptable, so both types of traffic (control and metering) can coexist in a PRIME network.
- Three applications have been analysed within UPGRID; demonstrating two of them. This
feasibility of remote control traffic using IP over PRIME is an opportunity to extend other
concepts.
- PLC PRIME allows the transfer of control traffic for remote control applications coexisting with
metering applications. This was the first use case validated.
- PLC PRIME allows the remote access to a SS without WAN coverage through a meter room with
WAN coverage. Extracting metering and control data of the SS though IP over PRIME to another
location where WAN access is possible. Using the GTP capabilities to provide an IP connection
over PRIME network, DSOs can avoid the investment in other transmission methods. This was
the second use case validated.
This multiservice subnetwork requires a higher level of monitoring and knowledge of the PLC channel.
This is a key element in order to be able to measure the impact of PRIME channel usage in each
situation. Therefore, a manageable PRIME subnetwork deploying SNMP based NMS is designed,
specified, developed and validated within UPGRID project.
- PRIME network monitoring and remote management is improved thanks to this SNMP protocol
deployment.
- This PRIME monitoring is the first step that enables a PRIME multiservice network. This is a key
enabler then for the control traffic exchange over PLC PRIME developed also in the scope of
UPGRID demonstrator area.
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- The aim was to propose a SNMP based NMS, and the initial steps for a prototype
implementation over a massive PLC PRIME deployment. This goal is successfully achieved within
UPGRID project.
- It implied a software evolution in the AMI data concentrators deployed in the field. So their
internal PRIME Base Node is able to gather and registered advanced PRIME PLC monitoring
information.
- It also required the development of an SNMP web tool, to be integrated at a system level
(Iberdrola’s premises). This tool gathers information from AMI data concentrators and
graphically shows their PLC PRIME network stability and performance.
- The development and integration of these elements has been validated both in laboratory and in
the field (demonstrator area). At this stage, with the implementation done, PRIME monitoring is
ensured.
- Also, field deployment confirms that regular AMI operation is improved; monitoring allows
detecting real time issues.
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5. LV NETWORK OBSERVATION AND MAINTENANCE BASED
ON SMART METER EVENT PROCESSING AND ANALYSIS
The main objective of this chapter is to report on the improvements and opportunities that the smart
meter event processing and analysis performed in the Spanish demonstrator brings with regards to LV
network observation and maintenance. First, the introduction provides an overview of the work done in
the demonstrator (further details can be found in [1]); while the second section presents practical
results and conclusions.
5.1 INTRODUCTION
The analysis aims to identify the potential of processing smart meter events to enhance the LV network
maintenance. From a practical point of view, smart meter events provide useful alerts and notifications
about anomalous network situations. Smart meter events cover circumstances related to QoS, demand
response, security failures, fraud, communications and specific issues of network devices. They are
classified in a broad range of more than 150 types [17]. In addition to this classification, events are
prioritised as spontaneous or non-spontaneous. The first ones are reported to the AMI Head System
when the event takes place. By contrast, the second ones are stored in the smart meters until there is a
request (e.g. once per week) from the AMI Head System to retrieve them.
The offline smart meter event analysis within the AMI architecture in the UPGRID demonstrator is
shown at Figure 55. The data concentrator acts as gateway to smart meter data at SS level, being able to
communicate with the DSO Meter Data Management System (MDMS) through different type of
communications. The MDMS holds and processes smart meter data. Within this framework, smart
meter events are retrieved to be analysed offline. It is important to note, as indicated in Chapter 3, that
the LV NMS also handles (online) some smart meters events as alarms or incidents.
FIGURE 55: OFFLINE SMART METER EVENT ANALYSIS WITHIN AMI ARCHITECTURE
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An initial study showed that 700.000 smart meter events are recorded per day (in average) in the
Vizcaya area (Figure 8). Then a set of strategies has been undertaken in the demonstrator to face the
analysis of this huge quantity of information in a semi-automated way as follows:
Developing tools based on Visual Basic for Applications (VBA) to automate event gathering,
filtering and sorting processes, strengthening the accuracy and repeatability of the analysis.
Focusing the study on: specific event types, most convenience timeframe for running the tools
and data aggregation at network asset level.
Improving the detection of hot spots at distribution network taking into account other field
measurements, for instance from supervisory meters installed in each SS.
Due to the early stage of leveraging smart meter events as key data for network maintenance, the
analysis performed has been conceived as an iterative and flexible process, as can be seen at Figure 56.
For this reason it was decided to use the VBA as an easy way to adapt development environment to
deploy and test different approaches. Then, based on that, the analysis has been focused in assessing
the convenience of such a tool for the expected usability and in identifying functionalities for a short-
medium term final tool.
FIGURE 56: EVENTS ANALYSIS FLOW CHART
5.2 EVALUATION AND CONCLUSION
5.2.1 MOST CONVENIENT TYPE OF SMART METER EVENTS FOR ENHANCING LV
MAINTENANCE AND OTHER PRACTICAL PROCEDURE DETAILS
Due to the vast number of events that smart meters report to the MDMS, it was decided to start the
analysis performing a pre-selection of some of them to have a manageable quantity. The first approach,
based on matching residential end-users claims regarding voltage quality with smart meter events
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(reported along 2015 in the areas of Vizcaya, Burgos, Castellón and Madrid20), was proved not to be
useful. This fact leads to the following conclusions:
There is misleading information due to the crucial role of human factor at claims reporting. In
some cases the date of the claim is far away from the date of the reported incident. In other
cases the electrical knowledge of end-users is focused mainly on outages, even scheduled ones.
Most of incidents are related to power interruption, even due to AMI roll out scheduled
interruptions, and these do not give any clues to improve distribution.
Event recording can be improved. For example, smart meters from different manufacturers
record sometimes disparate events during the same incident, some events are not recorded in
the MDMS because there are meters which are not recording any event since certain date, and
in other cases only certain events seems missing.
Thus, it was concluded that another approach was needed. The second attend, that ultimately proved to
be valid, was based on the Iberdrola experience on the distribution network. As explained, the flexible
approach followed in this analysis (Figure 56) has allowed adding any type of event that was believed
useful. Therefore, the selection of smart meter events shown in Table 13 has been proved convenient
for additional LV maintenance enhancements based on the tests performed in the demonstrator which
are presented in the next sections.
TABLE 13: TYPE OF SMART METER EVENTS FOR LV NETWORK MAINTENANCE ENHANCEMENT
Type Group Event Id. Description
Closed quality incidents
3 13 Average voltage between phases is under lower l imit
3 14 Phase 1 voltage is under lower l imit
3 15 Phase 2 voltage is under lower l imit
3 16 Phase 3 voltage is under lower l imit
3 17 Average voltage between phases is over upper l imit
3 18 Phase 1 voltage is over upper l imit
3 19 Phase 2 voltage is over upper l imit
3 20 Phase 3 voltage is over upper l imit
3 22 Long term outage detected at phase 1
Standard
1 1 Meter restart event with missed data
1 2 Meter restart event without missed data
1 3 Supply failure at meter event
1 7 Loss of neutral
5.2.2 SELECTION OF MAIN ANALYSIS FUNCTIONALITIES
Based on the event selection (see Table 13) and the test results shown in next sections a series of
functionalities have been proved as the most useful ones for the expected proposes. A part from
investigating the applicability of offline smart meters events analysis on enhancing LV network
20 Other areas, apart from the demonstration one, have been included in the study for having a bigger variety of data.
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maintenance, the demonstrator has investigated which would be the most convenient and useful
requirements to be included in a near-future tool that would be integrated within the current MDMS.
The main analysis functionalities selected in the demonstrator are presented in Table 14.
TABLE 14: MAIN ANALYSIS TOOL DEVELOPED IN THE DEMONSTRATOR FOR EVENT ANALYSIS
Tool Objective
Search for outage missed events Calculates the percentage of missed events when a specific outage incident
takes place.
Get time out of voltage limits of each SS Calculates the time that each SS is out of voltage limits based on the duration of
the undervoltage/overvoltage events registered by the smart meters associated
to that SS during the defined analysis time period.
Get time out of voltage limits of each FB Calculates the time that each FB is out of voltage limits based on the duration of
the undervoltage/overvoltage events registered by the smart meters associated
to that FB during the defined analysis time period.
Get supervision meter reports Calculates the percentage of hourly voltage measurements that are out of
voltage limits.
Additional practical conclusions have been drawn during the first tool tests:
In spite of the event type selection, the analysis of “loss of neutral” event showed that there was
a considerable quantity of false positives. These events belong to smart meters without neutral
connection due to meters installation issues, even though the power supply and the billing are
correct. For this reason, they are excluded at this stage.
For example, the analysis of events collected in November 2015 pointed out that a single SS
held the majority of them (86 events). What is more, all of them belonged to the same smart
meter. However, voltages measurements at that meter were within the regulatory voltage
limits21. For this reason, “loss of neutral” events were excluded for coming analysis.
It is concluded that a week period is the best time frame to launch the developed macros to
optimise the execution time. Longer periods would be useful for the analysis point of view since
more events can be added by elements (e.g. FB and SS), but the execution of database queries
take much more time, as well as sometimes the number of events exceeds the maximum
number of rows at Excel sheet.
Regarding the aggregation level, higher than the smart meter has revealed as a good approach.
This means grouping the analysed smart meter events at their connectivity elements, such as FB
or SS. In this way, LV network weaknesses are easier to detect, while grouping events makes the
quantity of analysed data more manageable at the same time.
21 There are two types of connections in the studied regions, on basis of the nominal voltage. B01: supplying 133V, with regulatory voltage limits between 123V and 142V (remaining old LV network). B02: supplying 231V, regulatory voltage limits between 215V and 247V.
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5.2.3 DETECTION OF MISSED EVENTS
This analysis is aimed at quantifying outage missed events and then, get an order of magnitude on this.
That is, smart meters that do not send the expected events when they should have. To perform the
study, one of the developed VBA macro has been used when it was known that LV incidents affected all
smart meters connected to certain SSs. After these kinds of incidents, the following events should be
recorded at the MDMS:
Supply failure at meter event.
Meter restart event without missed data or meter restart event with missed data.
Long term outage detected at phase 1. In order to make easier the analysis, only the outage
events at phase 1 are considered, to analyse in the same way the three-phase and monophasic
meters.
Then, an index is obtained for each element: smart meter, FB and SS. The index is calculated in terms of
the missing events after the incidents, as an average of recorded events. This way the closer the index is
to 0, the more events are missed, while the index is 1 if there are not missed events. In addition to this,
some extra information is included, for instance the smart meter read rates. Moreover, a colour code is
used for showing the calculated indexes based on three value intervals.
TABLE 15: EXAMPLE OF GRAPHICAL PRESENTATION OF RESULTS AFTER EXECUTING THE MACRO TOOL (LEFT). SUMMARY
OF RESULTS OBTAINED AFTER A SUITABLE INCIDENT HAPPENED ON 12/02/2016 THAT AFFECTED 14 SSS (RIGHT).
SS_id
nº of smart
meters
Total nº of searched
events received
Index (% of
events received)
1 269 635 0,79
2 390 1069 0,91
3 208 634 1,02
4 339 859 0,72
5 285 761 0,89
6 107 321 1
7 327 74 0,08
8 257 528 0,68
9 97 324 1,11*
10 230 263 0,38
11 358 783 0,73
12 325 683 0,7
13 264 496 0,63
14 9 12 0,44
Average index value 0,72
*Being higher than 1 means that there are smart meters registering the situation (the supply
shortage) more than once during the incident. Since the index is higher than 1 it implies that
there are not missing events what was the purpose of the test.
The conclusion after several incidents analysis is that around the 30% of events are missed. This
percentage could be extrapolated to the total number of events recorded at MDMS. The main reason is
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that the majority of the smart events are non-spontaneous, and they are gathered weekly into the
MDMS, during a certain time-slot. When this time slot ends, those smart meter events which have not
been sent are not recorded.
As a result, it is inferred that a new smart meter firmware configuration would be needed in order to
discard some events, such as communication events, useful during the AMI roll out, but dispensable
when the AMI deployment in an area is consolidated. Another alternative to investigate would be to
propose an event masking mechanism. Most of the smart meters have the capability to define (via
bitmask transported over COSEM object) which event should be logged and which events should be
spontaneously sent (notified) to the data concentrator and AMI Head System. This is more flexible than
events hardcoded in firmware, with no option for masking/unmasking.
Effectively, the amount of events to be retrieved from the smart meters would be reduced, theoretically
avoiding, or at least reducing, the number of missed events that are more useful for LV maintenance.
5.2.4 FIELD APPLICABILITY OF SMART METER EVENT ANALYSIS OUTCOMES
This study has been aimed at testing the truthfulness of transferring the event analysis results to field.
Although the core analysis has been done for Vizcaya (Figure 8) where the demonstration area is
included, three other locations have been added to the study (i.e. Burgos, Castellón and Madrid) with
the aim of covering different climates, rural and urban distribution network areas and including a variety
of Consumers. This has been also done as replicability concepts prove.
The macro tools are used over the set of smart meter events registered in Vizcaya between January
2016 and October 2016 to identify those FBs with worst undervoltage and overvoltages behaviour
(based on the time that their smart meters are out of voltage limits). Table 2 and Table 3 showed these
cases for further exploration (see section 5.2.5). A compromise criterion among the number of weeks
recording undervoltage / overvoltage events, the number of meters recording events and the
percentage of time out of voltage has been selected.
TABLE 16: WORST CASES OF UNDERVOLTAGE EVENTS (AT FB BASE) DETECTED IN THE VIZCAYA AREA
SS_id FB_id %time Out of V
(Average)
Maximum %time
out of V
Number of Meters
(Average)
Number of
weeks
SS_1 FB_1 12% 25% 9,82 44
FB_2 13% 29% 2,85 40
SS_2 FB_3 1% 42% 8,93 43
SS_3 FB_4 12% 29% 8,75 44
SS_4
FB_5 12% 36% 4 40
FB_6 11% 28% 3,98 43
SS_5
FB_7 24% 72% 1,91 44
FB_8 24% 44% 1 44
FB_9 23% 46% 2 44
FB_10 19% 48% 1 43
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TABLE 17: WORST CASES OF OVERVOLTAGE EVENTS (AT FB BASE) DETECTED IN THE VIZCAYA AREA
SS_id FB_id %time Out of V
(Average)
Maximum %time
out of V
Number of
Meters(Average)
Number of
weeks
SS_6 FB_11 12% 25% 9,82 44
FB_12 13% 29% 2,85 40
SS_7 FB_13 1% 42% 8,93 43
SS_8 FB_14 12% 29% 8,75 44
SS_9 FB_15 12% 36% 4 40
This information has been transferred to the maintenance responsible of the demonstrator area in
order to study it in detail and then determining if a field work is required for improving some of the
cases identified. Unfortunately, at the moment of writing this deliverable, this study is still undergoing.
Fortunately, additionally to the Vizcaya analysis, undervoltage and overvoltage smart meter event
processing have been performed in other zones and the potential cases for improvement delivered to
maintenance staff of these areas. They have provided positive feedback about the usability of the
results obtained from this analysis. It is worth mentioning that the involvement and proactive behaviour
maintenance responsible is of great importance to make the most form this kind of information. Some
examples are shown next:
In Castellón (December 2015 data) after the field analysis of the 5 worst overvoltage cases
detected (similar to those presented in Table 16 and Table 17), it was verified that there was a
one-to-one relationship in 100% of cases regarding the distance between the FBs and SSs, and
the time being over the voltage limits (calculated through smart meter events). One of the cases
is presented in Figure 57. The FB with code 984847 is the nearest to the SS (5010000233) and it
is the one being more time over the regulatory voltage limit. By contrast, the FB with code
989160 is the one with fewer events and it is the farthest from the SS.
FIGURE 57: FIELD ANALYSIS OF OVERVOLTAGE EVENTS
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Concerning to undervoltage events, the 17 worst cases were considered within a field analysis as
well. The conclusion is that there is a one-to-one relationship in 80% of cases (15/17) between
the distance to SSs and time FBs are registering smart meters under voltage limits. The two
remained cases were related to connection issues: one for being fed from other FB and the other
because the characteristics of the cable were different (lower section).
It is worth mentioned that on April 2016, some days after registering events, there was a
Consumer claim from due to a breakage of neutral wire. Perhaps, this situation could have been
avoided if the out of voltage events would have been taken into account. Then, the consolidation
of the methodology proposed could be used to perform predictive maintenance and acting in
advance.
In Burgos, for one LV feeder with some FBs registering severe undervoltage episode in one week
(04/01/2016) some corrective measures are analysed, for example change the transformer tap
changer position or, alternatively, change the voltage connection to B02 (nominal voltage 231 V)
and/or change the type of cable in the last stretch of the feeder.
In other cases, no reasons have been found to implement field works after evaluating the event
analysis outcomes.
5.2.5 DETAILED ANALYSIS OF VOLTAGE MAGNITUDES ISSUES AT SUPPLY POINTS:
VIRTUAL REGISTER
Based on the demonstrator effort of analysing in detail those overvoltage and undervoltage cases
identified after executing the macro tools, it was concluded the necessity of checking voltage at
Consumer smart meters in near-real time in more detail to discard “fault positives”. As the majority of
smart meter events are non-spontaneous, they are analysed in hindsight, since they happened some
time ago. This issue hampers the event analysis, the timing replicability of scenarios, and then the
applicability of results at field. In this context, the Virtual Register (software based) functionality
(software based) has been developed and integrated in the MDMS, see Figure 58. It automatised the
process of polling voltages and currents measurements of the selected smart meter22 each 5 minutes
during 48 hours. This way the virtual register increases the supervision of LV grid, providing with real
measurements from suspicious meters in a remote way without the need of installing any temporal
devices at the Consumer premises.
The tool validation results show that from 361 smart meters over which the Virtual Register were
launched, in 90% of the cases there were enough measurements (>90%) to consider the set of data
representative for extracting conclusions.
22 It is worth mentioning that even though smart meters installed at field are able to register voltages and currents, Iberdrola is not sending them (at the present time) to the AMI Head System automatically once per day as happened with other measurements. However, they can be retrieved on demand when the operator requests them.
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Suspicious FBs included in Table 16 and Table 17 have been taken as the basis for testing this new
application. Then, voltage measurements of their smart meters have been gathered using the Virtual
Register.
FIGURE 58: VOLTAGE CURVE ELABORATED BY THE VIRTUAL REGISTER FOR A PARTICULAR SUPPLY POINT (SMART METER)
AND SHOWN THROUGH THE TOOL GUI
The results of this data collection are included in Annex VII. This information should be used by
maintenance responsible to decide if some field work (e.g. change the distribution transformer tap
changer position and change the cable in some section of the LV feeder) is required to improve the
voltage profile. At the moment of writing this deliverable and having provided the demonstrator area
maintenance responsible with this information, no field works have been performed yet.
By way of example, the information of the first SS (SS_1) at Table 16 is included next. There are 2 FBs in
the SS_1, which is within the worst undervoltage cases:
FB_1, with 11 smart meters.
FB_2, with 3 smart meters.
The code of smart meters belonging to each FB is shown in Table 18, while the graphical representation
obtained with the Virtual Register for the six first smart meters on that table are included in Figure 59. It
is shown that all these smart meters have several measurements under the regulatory voltage (i.e.
measurements are below the red line indicated in the plots that indicates the regulatory voltage limit).
The other smart meters graphs are represented in Annex VII.
TABLE 18: SS_1 - METERS FROM WORST FB UNDERVOLTAGE
SS_NAME FB_CODE METER CODE
SS_1 FB_1 ZIV********44
SS_1 FB_1 ZIV********45
SS_1 FB_1 ZIV********49
SS_1 FB_1 ZIV********50 SS_1 FB_1 ZIV********51
SS_1 FB_1 ZIV********52
SS_1 FB_1 ZIV********53
SS_1 FB_1 ZIV********25
SS_1 FB_1 ZIV********27
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SS_NAME FB_CODE METER CODE
SS_1 FB_1 ZIV********51
SS_1 FB_1 ZIV********22
SS_1 FB_2 ZIV********34
SS_1 FB_2 ZIV********36
SS_1 FB_2 ZIV********37
FIGURE 59: MEASUREMENTS FROM FB_1 (PART 1)
Based on the experience gained on the use of this tool so far, it would be advisable to limit the number
of Virtual Register processes that are executed simultaneously on the same SS to avoid stressing the
communication network. In some cases it has been observed that all 5’ measurements were not
retrieved. This number has not been fixed yet. Some other features have been implemented after the
tests. For example:
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A watchdog to avoid initiates a Virtual Register process on a smart meter if there is already one
active process running on it.
The Virtual Register cannot be launched for a smart meter with a not successfully reading rate in
the last month. The measurements retrieved in this case would be mostly incomplete.
5.2.6 SUPERVISION METERS MEASUREMENTS TO COMPLEMENT THE EVENT ANALYSIS
Complementary to the use of smart meter events, hourly voltage measurements from distribution
transformer supervision meters (hereinafter supervision meters) installed in each SS (Figure 10) have
been proved useful in the analysis as well based on the demonstrator experience. For example, it
improves the detection of LV network hot spots at SS level since an anomalous voltage measurement
(out of limits) would determine that the cause is located at a LV feeder head instead of somewhere
downstream.
Moreover, thanks to the validity prove obtained in the demonstrator analysing supervision meter
voltage measurements, a similar functionality has being developed to automatically create reports on
these measurements (i.e. voltage, current and average powers). Then, maintenance responsible can get
information about the % of measurements within certain intervals per phase. This can motivate the
change of distribution transformers if the report shows voltage issues during a significant period of time.
5.2.7 REFINEMENT OF SUPERVISION METERS INVENTORY: INCONSISTENCIES DETECTION
As pointed out in section 5.2.7, it is possible to analyse the percentage of hourly supervision meter
measurements out of regulatory limits (overvoltage and undervoltage) executing one of the macro tools
developed in the demonstrator. This study was performed for May 2016 for Vizcaya area. It was
observed that the vast amount (96%) of supervisory meters have not any voltage issues, but it was
identified that there were some with the 100% of measurements out of voltage limits (Figure 60 shows
graphically the overvoltage analysis result). Thanks to this outcome and a subsequent detailed
investigation, an unexpected improvement was detected. That is, the identification of wrong labelled
supervision meters in the inventory data base. Some were labelled as B0123 when in reality it should be
B02 (reporting overvoltage) and vice versa (reporting undervoltage). Figure 61 shows 5 supervision
meters that register a voltage corresponding to B02 in phase 1 while they have been inventoried as B01
(the same happens with the other two phases even they are not shown).
23 There are two types of connections in the studied regions, on basis of the nominal voltage. B01: supplying 133V, with regulatory voltage limits between 123V and 142V (remaining old LV network). B02: supplying 231V, regulatory voltage limits between 215V and 247V.
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FIGURE 60: OVERVOLTAGE REPORT RESULTS AT SUPERVISORY METER
FIGURE 61: EXTRACT OF THE RESULTS OBTAINED AFTER EXECUTING THE VBA MACRO TOOL
Taken advantage of this finding, and in order to solve the issues in the complete data base, the same
study were performed for all the supervision meters installed in the Iberdrola network (Spain). As result,
418 out of 75.320 cases were identified as potentially wrongly labelled, Table 19. This information has
been transfer to the maintenance responsible of each Territorial Distribution Unit (TDU) to implement
the corresponding corrective actions that involve field visits to verify each case. At the end of February
2016 the number of cases was reduced up to 105 (75%).
TABLE 19: NUMBER OF SUPERVISION METERS POTENTIAL WRONG LABELLED
Geographical location (provinces)
Supervisory meters potentially wrong labelled
Location_1 59
Location_2 21 Location_3 2
Location_4 2 Location_5 13
Location_6 19 Location_7 19
Location_8 30
Location_9 28 Location_10 15
Location_11 25 Location_12 22
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Geographical location (provinces)
Supervisory meters potentially wrong labelled
Location_13 32
Location_14 4 Location_15 5
Location_16 14 Location_17 108
TOTAL 418
5.2.8 CONVENIENCE OF INTERACTIVE RESULT REPRESENTATION
The software tool Tableau Desktop [18] has been introduced to visualise results in an interactive way.
Therefore, the analysis is enhanced with a wide range of graphical features helping locate suspicious
spots (e.g. network areas with higher number of smart meters with undervoltages or overvoltages
issues) at the LV distribution network. Based on the evaluation, it has been concluded that this is a
useful functionality that help in the result interpretation and it is a mean for easier location of
investigated network issues. The main reason is that these kinds of analyses normally cover several
months to have a representative data sample. Then, the use of a graphical representation tool makes
easier drawing meaningful, consistent and practical conclusions. This tool allows aggregating data from
several sheets (generated by the demonstrator VBA macro tools) where, for example, each of them
contains weekly data from the elements, FB or SS.
For example, the number of undervoltage events per week in the Vizcaya region is represented in form
of bar chart, interactive map and table (Figure 62, Figure 63 and Figure 64). Presenting the information
in these modes facilitates the detection of, for instance, those weeks with highest ratios of voltage
issues and its network location:
It can be seen that there are some weeks which are over the average of events with erroneous
dates, such the 8th of August and 24th of October, with 46 and 88 undervoltage events
respectively.
It is also remarkable that the number of events over the maximum time (6 hours) is very
variable, but it is under the 5% of the total number of events.
The filter selected in Figure 63, helps to visualise FBs which smart meters have registered
undervoltage events on at least 40 out of the 44 weeks and with more than the 10% of the week
minutes under the voltage limit. Similarly, Figure 64, present the information on a table format.
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FIGURE 62: BAR CHART [NUMBER OF UNDERVOLTAGE EVENTS PER WEEK IN VIZCAYA (2016) ON BASIS OF ITS DURATION]
FIGURE 63: MAP [NUMBER OF UNDERVOLTAGE EVENTS PER WEEK IN VIZCAYA (2016) ON BASIS OF ITS DURATION]
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FIGURE 64: TABLE [NUMBER OF UNDERVOLTAGE EVENTS PER WEEK IN VIZCAYA (2016) ON BASIS OF ITS DURATION]
5.3 OPPORTUNITIES
Based on the experience gathered on the demonstrator, a series of next steps are identified to continue
developing the presented methodology on smart meter event processing:
Guarantee the standardization of event generation and sending commands at Consumer meters.
Reducing the percentage of missed events to ensure the reliability of events analysis.
Refinement of event processing, according to field performance, to avoid recording false positive
events.
Deep review of events priority to classify them as spontaneous, non-spontaneous or even as
non-recording at MDMS.
An analysis with even broader time horizon in order to eliminate temporalities or to avoid
misunderstanding consumption patterns.
Development of new tools to process the events, overcoming technical limitations of VBA
(execution time, maximum number of sheet lines, etc.).
Moreover, the development of the Virtual Register offers real measurements from specific
costumer meters, which could be very useful to distribution management issues.
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6. CONSUMER EMPOWERMENT TOOL
6.1 INTRODUCTION
A web-based computer tool for managing energy consumption rationally by Consumers has been
developed in the demonstrator.
This work exploits one of the UPGRID Function Objective selected and described in [4], “New
approaches for market design” and, more specifically, UPGRID sub-functionality “Web portal for
increasing the awareness of Consumers in order to leverage their participation in electricity markets”. It
is worth mentioning that smart meter measurements are the main source of data. Then, “Smart
metering data utilization” Function Objective is also relevant since it lays down the basis for the tool.
The UPGRID Spanish demonstrator has enhanced the previous tool version developed in the Bidelek
Sareak project. It has extended the tool scope being the main functionalities the following ones:
Presents structured information on users’ energy consumption (appliances, electricity
consumption, etc.).
Offers Consumers challenges to help reduce their energy consumption (energy saving by
appliance type).
Compares their energy consumption to that of an efficient Consumer.
Helps reduce electricity costs through knowledge of the following day’s electricity price.
In short, the aim of the tool is to ensure that Consumers have enough technically and economically
reliable information to allow them to take responsible decisions to help reduce their electricity
consumption.
6.2 EVALUATION
The following section summaries the results of the tool performance evaluation. The tool capabilities
are described in detailed in [3].
6.2.1 METERING DATA GATHERING: TECHNICAL SOLUTION
The main objective of the Spanish demonstrator in this regard has been to implement an automatic
process to feed the web-based tool automatically with the required data from Consumers. This should
allow connecting and transferring the required data automatically from the Iberdrola metering data
systems to a dedicated data base specifically designed for this purpose. Only the specific information
needed (no more, no less) is collected, and of course previous authorization. The data should be
collected as soon as a Universal Supply Point Codes (CUPSs) is provided. Then, the data is transfer
automatically to the mentioned secure data base from which the tool takes the data to execute the
algorithms. Consumption data should be transferred on a daily while file 2 just once.
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Moreover, the tool is provided with other source of data. It should be able to set out the data provided
by the power Spanish power system via the website of Red Eléctrica de España (Spanish Electricity
Network) (https://www.esios.ree.es/es/pvpc).
The validation process has consisted in checking if the required metering data has been transferred
correctly from the Iberdrola MDMS to the web tool secure data base during a period of 6 months. In
more detail:
Checking proper operation of the application in terms of the internal calculations made.
Checking that all external information (daily and monthly electricity consumption, electricity
prices for the following day, etc.) are properly downloaded.
Testing with real data from different users.
Based on the observations, it may be concluded that the tool meets the objectives for which it has been
designed. Apart from some punctual communication issues (not tool related) that prevented the access
to the File Transfer Protocol (FTP) to take the raw data, nothing beyond expectations has been
observed. The result has therefore been positive.
6.2.2 WEB TOOL FUNCTIONALITIES
As explained, the aim of the web-based tool is to provide Consumers with information on the benefits of
smart grids and the possibilities they offer for incorporating intelligent systems to optimise the supply of
electricity, save energy, reduce costs and enhance security. The website contains a number of useful
tools which Consumers can use to determine their consumption and identify actions that could improve
energy efficiency and financial savings.
The evaluation in this regard has consisted in checking how the web tool performs displaying the
information in each of its options (i.e. menus). Table 21, shows the main functionalities that have been
tested and test objectives. Table 21 summaries the test pass report showing some tool screen shot.
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TABLE 20: SUMMARY OF THE MAIN TEST PERFORMED FOR EVALUATING THE TECHNICAL WEB TOOL PERFORMANCE
Tool
functionality Description Test objectives
Registration and
Login
Users enter their details to get
registered on the web tool. This
information is used to login and start
using the tool.
Check the full registration and login
procedure simulating different
scenarios of data entering to verify the
toll behaviour in such cases (e.g.
information that does not fit the
expected type or number of characters,
some compulsory form field is not
completed, more than one user
entering the same information, etc.).
Recovering access details, in case of
being forgotten, is also checked.
Welcome page To provide an easy and friendly
access to all tool options to the login
user (i.e. main menu, message and
consumption information).
Check the overall layout of the
welcome page to prove that all options
are visible and accessible.
Energy
consumption
display
Display different consumption
figured based of the data coming
from the smart meters (see section
6.2.1). Users have at their disposal a
variety of options view their
consumption based on different
time period (e.g. for a selected day,
daily, monthly and compare
different days).
Check that the information is well
presented for each of the option
available (e.g. no missing units, correct
and clear graphs tittles, colour code
used, etc.).
Check that the information represented
on is well refreshed once the user
changes from one graph to another (i.e.
type of graph of time period).
Setting home
equipment Using the system, users can identify
how their total consumption breaks
down between different equipment,
using theoretical statistics.
Check that the initial full data entering
required for defining the user home
profile works as expected (i.e. set the
different appliances at home and enter
more detailed information about the
appliances). Different data entering
scenarios that could happen in reality
are tested.
Check the correctness of the theoretical
breakdown of the user home
consumption after entering the
required information.
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Tool
functionality Description Test objectives
Comparison
with other
consumers
Users can compare their real
consumption with the theoretical
consumption of an energy-efficient
user with a similar home
arrangement.
Check that the information is well
presented for each of the option
available (i.e. view comparisons and
view associated challenges).
Challenges The primary purpose of the website
is to give electrical energy
consumers enough information to
allow them to reduce consumption
and create a system of rational
energy use. In the web system,
these recommendations take the
form of ‘challenges’.
Check that the information is well
presented for each of the option
available (i.e.. view challenges
completed in last year and
consumption trends, view proposed
challenges, accept proposed challenges
and view pending challenges).
Check that the challenge information is
available in the various sections of the
portal where it should be.
Display of the
energy price for
the following
day
The aim is to provide users with this
information in such a way they can
timing their consumption based on
electricity cost in each period.
Check that the energy price for all
hours on the following day is shown
and clearly presented.
Check that the user average
consumption is compared with energy
prices for the following day.
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TABLE 21: PERFORMANCE TEST PASS REPORT
Tool
functionality Performance test result
Registration and
Login
The tests performed have shown the expected behaviour of the tool after the different scenarios simulated.
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Welcome page
Regarding the information layout on the welcome page, it is thought that “Personal information” could be changed by
“Participant consumption information” for example. Moreover, the “More” push button should be aligned.
During the test, a communication issue happen in the FTP server. This avoided the tool receiving data from the FTP during
about one month (from middle June to middle July). This can be seen in the consumption graph shown below (flat profile
during the first 20 days of July). The reason was a FTP credentials change (out of the control of the demonstrator).
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Energy
consumption
display
During tests, information has been represented correctly when there was data available. However, in a specific internet
browser, the web page zoom needs to be changed to refresh the graphs (otherwise the graphs content are not displayed).
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Setting home
equipment
Tests on the setting home equipment have been successful.
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Comparison with
other consumers
It seems that the note (“estimated data”) on the right hand side graph could create some confusion to the user. The user
might wonder why having real data from smart meters it might be contradictory why the “Me” bar chart for total
consumption needs to be “estimated”.
For the moment, all data gathered from the different types of equipment is estimated. As smart meters are gradually
installed in the homes, the data for the different units will match the real situation.
Information has been well displayed in the different cases (i.e. home settings) simulated during the tests.
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Challenges
Information has been well displayed in the different cases simulated during the tests.
Moreover, energy tips displayed after clicking in each Challenge text are well valuated.
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Display of the
energy price for
the following day
After comparing a set of days chosen randomly the data displayed regarding electricity prices are the same published
officially in www.omie.es from where they are taken.
The colour code used is useful to visualise easily different price period.
Some help popup window could be added to the second graph in case de user has some doubt about it meaning.
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6.2.3 SOCIETAL RESEARCH
This section provides a summary of the collaboration with WP924 setting out the activities carried out in
the social sphere (more detailed information in [3]). Social dissemination activities in the Spanish
demonstration area are being conducted by Tecnalia in coordination with the Task 9.2 – Interaction
Campaigns. The information below on users' knowledge of the energy industry in general, the power
industry in particular, and of smart grids is serving to evaluate concepts and identify requi rements for
inclusion in the web-based tool.
Domestic and business Consumers have been identified as the most relevant and numerous social
groups for the Spanish demonstrator. After evaluating different alternatives to communicate more
effectively with domestic consumers, associations that represent them have been contacted first. By this
strategy the access to consumers has been proved more efficient. Therefore a series of contacts have
been established through the association that represented them as follows: FAAVVB25 (Federación de
Asociaciones Vecinales de Bilbao), Bilbao-Dendak26 and CECOBI27 (Confederación Empresarial de
Comercio de Bizkaia). In the Spanish demonstrator, the active involvement of FAAVVB has played an
essential role to facilitate the participation of domestic consumers.
The social dissemination activities of the demonstrator have been communicated personally to the
neighbourhood associations of the different districts of Bilbao during the federation meetings. A first set
of smart grids and UPGRID “learning pills” were presented and explained. At the moment of writing this
document, five specific workshops have been organised with the participation of citizens living in the
demonstrator area [10].
After an intensive recruitment effort, 146 volunteer participants (consumers residing in Bilbao) were
obtained. Based on the provenance of the sample, three groups have been established: members of
neighbourhood associations (n1=56), employees of companies participating in UPGRID without
knowledge related to smart grids (n2=55), and with knowledge and experience in this area (n3=35).
Results presented in [3] regarding the participant perception of smart grids and web tools are shown
next.
Smart Grids Awareness – Knowledge and Attitudes
The knowledge and attitudes data indicated the necessity of the households to improve their
knowledge and learn more about smart grids. Only 30% of participants have some information
about smart grids.
Regarding the attitudes about smart grids, the results indicated that:
o 46% believe that smart grids allows energy saving.
24 User Engagement, Societal Research and Dissemination of Project Results .
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o 31% believe that smart grids allow greater use of renewable energy.
o 36% believe that the smart grids promote the production and consumption of sustainable
energy.
Concerning behaviours and intention of response related with smart grids, the results indicated
that:
o 53% of participants are interested in having my energy consumption monitored through a
smart meter.
o 90% would like to reduce their energy consumption and still their bill.
o 80% would like to change their energy consumption to promote sustainability (energy
efficiency and use of renewable energy sources).
At the end, 72% of respondents will be available to participate in the future activities of the
project UPGRID.
Information Iberdrola Distribution and EVE services (web tools)
Iberdrola Distribución web tool
75% of the participants in the Spanish demonstrator survey do not know the service (web-tool)
offered by Iberdrola Distribución to consult the hourly consumption online and other figures. The
employees with knowledge in smart grids know this service and have an account (40%).
EVE web tool (Bidelek Sareak project)
More than 80% of the participants in the Spanish demonstrator questionnaire do not know the
online web tool offered by EVE through Bidelek Sareak initiative 28 called "Save at Home" to
learn about consumption and energetic behaviour. There are more employees with knowledge in
smart grids that know this service and have an account (15%).
6.3 CONCLUSIONS AND OPPORTUNITIES
Regarding the technical web tool performance, it can be said that the expected results have been
achieved. The different simulation scenario on data entering has been proved successful. Based on the
test performed some enhancements have been identified. Part of them have been already
implemented, for example making the tool available in three languages (Spanish, Basque and English)
and providing short messages (concepts and recommendations) regarding smart grid and energy
efficiency concepts in a dynamic banner at the top of the web page.
Based on the society research, residential consumers in general might be said to lack a detailed
understanding of what smart grids are and how they can contribute to energy management. Likewise,
there is a significant group of people who are interested in improving their electricity contracting and
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reducing energy consumption in their homes. Given these two factors, it can be concluded that
electricity Consumers will always welcome any reliable information provided to them. In that regard,
Consumers can use web-based tools, such the one being developed in the demonstrator to analyse their
energy consumption and assess how they have changed their habits to reduce electricity costs. This
paves the way to continue developing and disseminating these kinds of tools and being adapted to
Consumers’ needs. At the same time, it is very important to make an effort on disseminating their
existence and make Consumer find them easy to use.
Now that it has been decided on the web-based tool for the residential sector —a very large group but
one that has low energy consumption— it is believed that there is an interesting opportunity for
adapting this application for use in other sectors such as services (small supermarkets, clothes shops,
bars, etc.).
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7. ADDITIONAL LV OPERATION OPPORTUNITIES: INNOVATIVE
SOFTWARE-BASED COMPONENTS
This section presents a summary of conclusions identified so far during tests performed on a set of
innovative software-based components specified basically in WP2 - Innovative Distribution Grid Use
Cases and Functions and, in some cases developed also within this WP2 and adapted to the Spanish
demonstrator. Necessary tests to check functionalities and components objectives have been
performed. However due to the difficulties to integrate them with the DSO system in operation at this
stage, the mentioned tests have been performed with a set of offline data extracted from that system
and a virtual machine29 has been prepared with the purpose of installing as many of these components
as possible. This will allow continue performing new test in the future.
These components are focused on exploiting a series of possibilities, innovative services and
functionalities that are made affordable and boosted as a result of the high amount and diversity of data
the smart grids bring to the application of algorithms and artificial intelligence techniques. These
components are shown in Figure 65. More detailed information can be found in [2].
FIGURE 65: COMPONENTS ADAPTED FOR THE SPANISH DEMONSTRATOR
7.1 IMPROVING OVERLOAD FORECASTING
7.1.1 OBJECTIVE
Overload Forecasting System (OFS) (sometimes referred as SPS30 from its Spanish initials), is an
application that studies the observed evolution of measurements acquired by SCADA compared to some
reference day, forecasts the expected behaviour (i.e. measurements change) and notifies potential
overloads alerting the distribution network Operator in advance. The OFS design has been reviewed
29 A virtual machine has been prepared with the purpose of installing as many of these components as possible. Windows 2012 R2, 30 0GB and 16GB RAM.
30 SPS stands for Sistema de Previsión de Sobrecargar in Spanish.
Overload forecasting system in SS
MV State Estimation SS simultaneity factor
estimation
Demand response simulator
Support for the Maintenance Crews
SS Load and Generation forecasting
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with the aim of achieving a relevant performance (data management and processing) improvement. This
is motivated by new data availability from SSs due to the progress ive installation of AMI that bring
telecommunications to previously uncommunicated SS and enable not only measurements at the MV
side (process started years ago) but also to the LV side of distribution transformers.
7.1.2 EVALUATION
Focused mainly on:
a) Application accuracy improved with a more efficient data interchange schemes (both in terms of
polling timing and the amount of record transferred in each interaction) and the application of
better forecasting algorithm performance.
b) Operator satisfaction degree coming from this mentioned accuracy, additional polling
configuration options and notification means.
The FPS web application is executed from a corporate intranet application server and integrated with
Iberdrola’s authentication and authorization server. Several access roles are considered.
The DMS operator is able to review the set of alarms, check them comparing the real
measurement values with both those from the reference day and the forecasted evolution and act
in consequence. If needed, the DMS operator might vary the reference day or the applied
thresholds for a new forecast process.
An authorised user is able to select measurement points, visualise them along some time period
and retrieve these datasets as .csv files.
7.1.3 CONCLUSIONS AND FUTURE OPPORTUNITIES
The following main conclusions and opportunities have been identified:
It is a tool that is used daily in the control centres. It facilitates the Operator duties generating
messages (i.e. alarms) that inform about the foreseen overloads. For this reason, it is not dismiss
the opportunity of sending this information to the SCADA in the future.
It seemed advisable to modify the AppLinkDMS module [2] in order to achieve faster results and
shorter execution times so the number of treated measuring points would be extended
progressively to include data from SSs within acceptable process time requirements. Otherwise,
DMS operator ordered application executions would become unacceptably slow and useles s for
the intended purpose.
The AppWeb module [2] also required access to the measurement data repository but with a
reduced number of simultaneous points so a gain of a tenth of a second per measuring point
would not deserve any attention nor would provide any value.
Regarding the optimal grouping strategy of electrical measurements to be retrieved a conservative
approach was advised. The review of the sizes of the fields used at Historical Information System (
HIS) database function (up to 200 characters for point list entry) and tables (up to 10 characters
for point number) and obtained times suggested that grouping measures in groups of 15 elements
could be enough. Whatever the real size of each point code was, there would be room for the
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complete string of characters without any further check. The elapsed times for groups of 20 and
25 measuring points could be biased because many of those points have sparse datas ets.
Comparative and cooperative analysis of the results of this tool with the results of other grid
monitoring and managements systems (OMS, network management system, state estimation
tools, etc.) would allow to identify and check grid sections and assets under stress improving grid
supervision, maintenance and planning.
7.2 IMPROVING MV STATE ESTIMATION
7.2.1 OBJECTIVE
The objective of the MV state Estimator is to calculate the most likely state of the network for a certain
instant in time. The state of the network includes for a specific time, the values of: voltages, power and
current injections at buses31, and, power and current flows at network branches 32. State estimation
calculations use, as the main input the measurements collected from the field devices and take into
account their accuracy for calculating the most precise network state. The innovation comes from the
use of a weighted least squares algorithm capable of handling the distribution network characteristic.
Contrary to the current approach where load allocation techniques are used, the deployed state
estimation techniques use information form the full set of measurement devices installed in the
demonstrator. In this way it is able to provide insights about the performance of the monitoring system
while improving the accuracy of the estimations.
7.2.2 EVALUATION
The MV state estimator has been executed as an off-line tool using historical measurement data
provided by Iberdrola of one subnetworks (i.e. Deusto TF-2 132 KV). The analysis includes the following
main points:
Normalised errors: The normalised error indicate how far are the estimated values from the
measured values scaled with the accuracy of the measurement. Values between -1 and 1 can be
considered as normal values while values higher than 1 or lower than -1, indicate that the
accuracy range should be analysed since they may indicate potential errors in the network model
or the measurements themselves.
Absolute errors: The difference between the measured value and the estimated ones gives an
indication about the level of correction made by the estimator. High errors indicate that the
31 A bus corresponds to the set of nodes that, in the considered network state, are connected together through any type of closed switches or equipment with zero impedance. Buses change as the network topology changes (i.e., switches, breakers, etc. change state).
32 A branch is a subset of a network, considered as a two-terminal circuit, consisting of a circuit element or a combination of circuit elements. Each terminal of a branch is connected to a bus. Typical branches are feeder segments and power transformers.
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estimator needs to modify the measured values and provides a measure on the benefit of having
a state estimator respect to only having the measured values.
7.2.3 CONCLUSIONS AND FUTURE OPPORTUNITIES
The MV state estimation component has been successfully developed and tested in the Spanish
demonstrator. The performance of the developed estimator is good enough to be used with large time
series of measured values. The algorithm is able to converge and produce information about the
estimated values and different error indicators.
Using a rich user interface, several off-line analyses have been done over the state estimation results.
The analyses have been focused on the interpretation of the differences between the estimated values
and the measurement values. These analyses yield useful information allowing the identification of
potential inconsistencies in measured values.
The state estimation results indicate also the high potential of applying state estimation techniques in
the distribution networks for correcting measured voltage and power values.
7.3 IMPROVING DSO DECISIONS BASED ON DEMAND SIDE ESTIMATION
7.3.1 OBJECTIVE
The demand response simulator component is intended to be used by the DSO for estimating the effect
of Demand Response (DR) programs in the aggregated consumption profiles of residential Consumer
clusters. This simulator is able to calculate the modification in the aggregated consumption profile of
Consumer groups when different energy prices or other control actions are taken over them.
Specifically, this simulation tool addresses the indirect control strategies based on dynamic pricing. The
Demand Response Simulator component could allow any Energy Market actor (i f permitted by
regulation) to analyse possible Consumers behaviour depending of different control signals, especially of
energy prices.
7.3.2 EVALUATION
From the technical point of view, the installation and testing of the tool run properly and the estimation
algorithms worked quite accurate.
From the Consumer behaviour observed, it can be said that Consumers providing flexibility tend to
reduce heating/cooling energy consumption and delay the starting times of shiftable appliances from
peak price periods to valley periods. The amount of flexibility provided depends on the price sensitivity
of the Consumers, the higher the price-sensitivity, the higher the amount of flexibility provided. In
addition to this, this flexibility can come from shiftable loads, from thermal loads or from both, as a
function of the desired trade-off between price sensitivity to cost, to control of shiftable loads and to
control of thermal loads.
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No relevant performance analysis has been considered due to novelty of the tool and the lack of actual
data available about the Consumers home loads and preferences, which were simulated.
In general and from the conceptual point of view, it can be stated that the results of the analysis of this
tool is satisfactory and promising, even considering that some of relevant data had to be simulated (the
detail of the Consumer home loads and profiles of variable.
7.3.3 CONCLUSIONS AND FUTURE OPPORTUNITIES
The results of the tests show that Consumers providing flexibility tend to reduce heating/cooling energy
consumption and delay the starting times of shiftable appliances from peak price periods to valley
periods. The amount of flexibility provided depends on the price sensitivity of the Consumers, the higher
the price-sensitivity, the higher the amount of flexibility provided. In addition to this, this flexibility can
come from shiftable loads, from thermal loads or from both, as a function of the desired trade-off
between price sensitivity to cost, to control of shiftable loads and to control of thermal loads.
It is important to take into account the payback effect that would be produced when the control actions
finish and the appliances return to their normal operation modes. At an aggregated level, this power
synchronization effect may cause a significant peak in the power consumption and may affect adversely
the operation of the electricity grid. Consequently, the DSO should apply certain strategies to try to
minimise it. In any case, the results of these simulations are only for validation purposes as they are not
based on actual data and therefore the amount of payback generated should be further studied.
As a conclusion, it can be stated that the developed component provides an effective approach to the
challenge of estimating the aggregated consumption profiles of a group of Consumers (cluster) as
response to variable electricity prices. This is very important for the DSO in order to define the strategies
to be followed in terms of market participation and Consumers’ portfolio optimization.
As an opportunity, it has been identified the possibility of adding other simulation criteria, different
from the energy price, especially those options based on Flexibility and grid operation, performance and
quality improvement. In a medium term scenario, and according to the European Commission Clean
Energy (Winter) Package provisions, it is supposed a more relevant participation of the
Consumers/Producers in the electricity market being an active actor in some of the grid O&M processes.
In this new role, it is expected this grid Consumers to declare and make accessible their home loads and
generation sources to be manageable for the optimization of the grid operation. In that scenario, this
kind of Consumer consumption/production estimation tools, being able to access to reference
information from these homes DER, will be of great relevance, not only for the retailing market, but also
for the optimization of the planning and operation of the grid.
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7.4 IMPROVING LOAD DISTRIBUTION BASED ON THE SIMULTANEITY
FACTOR ESTIMATION
7.4.1 OBJECTIVE
This SS simultaneity factor estimation component consists of a statistical algorithm that estimates the
load Simultaneity Factor (Ks) in the scope of a SS. Several issues, such as the network components and
Consumers characteristics (e.g. load curves and contracted power) are considered when computing the
simultaneity factor using this algorithm. This component exploits mainly data gathered currently by
existing smart meters. Ks is defined as a probability measurement of the coincidence of individual
maximums with the maximum of the aggregated load, in other words, it is the ratio of actual kWh used
in a given period, divided by the total possible kWh that can be used in the same period at the peak kW
level.
The formula which defines the simultaneity factor is:
𝐾𝑠 = 𝑃𝑚𝑎𝑥
∑ 𝑃𝑚𝑎𝑥,𝑖𝑁𝑖=1
EQUATION 1: SIMULTANEITY FACTOR
Where Pmax represents the peak load and Pmax,i represents the peak load of the Consumer i. The Ks of a
certain electrical network, a certain distribution transformer or a certain SS relates to the load
characteristic of the Consumers to belong to this electrical network, distribution transformer or SS and
the amount of these Consumers.
7.4.2 EVALUATION
Optimal Phase Swapping in LV network based on smart meter data. Based on DSO requirements to
properly address the Optimal Phase Swapping task [19]. That is, allocate Consumers to certain LV feeder
phases based on their previously calculated Simultaneity Factor.
7.4.3 CONCLUSIONS AND FUTURE OPPORTUNITIES
It is a matter of common experience that the simultaneous operation of all installed loads of a given
residential or industrial installation never occurs in practice, i.e. there is always some degree of diversity
and this fact is taken into account for estimating purposes by the use of a simultaneity factor (K s).
The simultaneity factor is the ratio of the maximum demand of a group of loads, or part under
consideration, to the sum of the individual maximum demands. The factor Ks is applied to each group of
loads (e.g. being supplied from a distribution or sub-distribution board). It is concluded that the
statistical Ks model can be used by DSOs to assess the impact of adding a new set of supplies points (i.e.
Consumers) on a given LV feeder. In this sense, the Spanish demonstrator has perceived the necessity of
having an optimization algorithm capable of distributing these new Consumers to LV line phases with
the objective of minimizing the Ks and thus avoiding deep valleys into the aggregated load curves.
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Therefore, a novel tool for phase swapping in LV distribution networks is adapted to the demonstrator
takin into account these Consumers individual contributions to the aggregated consumption curve using
real hourly measurements. Then, this functionality provides a realistic procedure for optimizing the
topology of LV distribution networks, regarding the connection of Consumer at LV line phases
minimizing, statistically, the Ks factor.
From a technical point of view is concluded that knowing the load pattern, the consumption habits are
known as well. As per this definition, the value is always ≤ 1 and can be expressed as a percentage of
total loads that contributed to peak consumption. Usually it decrease as the number of connected load’s
increases, also if the maximum consumption does not match with the maximum of the aggregate signal
(sum of individual maximum consumptions), the Ks decreased. The real objective when trying to
minimise the Ks is flattening demand redistributing the consumption by shifting load to decrease
demand peaks while filling in troughs. Flattening demand implies reducing the difference between the
peaks and troughs in a LV distribution network usage, thereby creating a fletter usage pattern that
lessens the deviation from the average usage. Demand flattening has the potential to benefit
Consumers as the electric grid becomes smarter and more efficient, since peak demands have a
disproportionate effect on grid capital and operational costs, including transmission, generation, and
fuel costs. For instance, demand flattening significantly reduces transmission and distribution losses,
which account for nearly half (47%) of residential energy consumption [3], since these losses are
proportional to the square of current.
As an opportunity, it has been identified the possibility of extending the main calculations applied for
determining the simultaneity factor to other processes addressed to the optimization of the Consumer
connection to the feeders and phases in SSs. That is: Identification of Consumer unbalanced connectivity
in a due SS.
Definition of the optimal Consumer connection configuration for new or already existing
registrations.
Estimation of the cost required for the adequate reconnection of Consumer in a SS.
Starting from a specific budget, the identification and prioritization of the Consumers that would
be reconnected for to achieve a specific improvement of a SS connection configuration.
7.5 ENHANCING THE OUTAGE MANAGEMENT AND THE SUPPORT FOR
THE MAINTENANCE CREWS COMPONENT OBJECTIVE
7.5.1 OBJECTIVE
This component represents an attempt to test the potential advantage of a combined analysis of the
smart grid data from SSs and AMI for the characterization of the grid assets operation and failure as the
reference for an adaptive maintenance planning and for an optimal outage management and
maintenance crew coordination and support.
The implemented component carries out the following main functional blocks:
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dynamic information acquisition and processing,
static information configuration, risk and reliability assessment
optimal planner for maintenance scheduling
7.5.2 EVALUATION
This component was verified as a standalone application fed with some demo data, both dynamic data
from smart meters and SS sensors and static data for maintenance resource information, network assets
and network and assets model. These data belonged to a SS located in the Spanish demo area.
With these data the tool generated different results about asset replacement scheduling for the
transformer and lines of that SS, network single line diagrams, foreseen failure curves and propositions
of maintenance scheduling for those assets.
This information was shared with representatives of the maintenance function stating the promising
potential of the tool.
Some deviations can be observed in the results of the tool, especially in the line diagram model with
respect the actual one. This is mainly due to the present lack of some information about some grid
assets, especially feeders, that is being completed during the demo completion.
Additionally, proper historical data of assets failures are not completely available for an accurate
preventive asset maintenance scheduling but the proposed plans obtained with simulated historical
failure and the operation data seem to be consisting.
7.5.3 CONCLUSIONS AND FUTURE OPPORTUNITIES
Tool to be considered for the next future when the MV/LV data would be complete and stable, specially
the data related to the network topology model and the historical operation and failure assets data that
will allow to simulate the operation of each asset, to estimate its potential failures and to define
preventive maintenance plans individually for each asset and even fora better grid planning definition.
7.6 LOAD AND GENERATION FORECASTING IN SECONDARY SUBSTATION
7.6.1 OBJECTIVE
The load and generation forecasting component provides the grid manager with the analysis, modelling
and forecasting of the electrical energy consumption of Consumers and/or generators (including the
role of “prosumers”) aggregated at distribution transformer level in SS.
The main functionalities provided by the component are:
a) The analysis of Consumers data that gives insight on its present behaviour and to characterise it
in order to be able to foreseen that behaviour in the future.
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b) With the previous analysis integrated to meteorological information, the forecasting of load and
generation of DERs connected to the grid that is considered a valuable outcome for energy
management at the SS level. This information is important for the DSO, as it can be used to
predict the status of the system and avoid faults or other events.
The objectives of bringing this component to the Spanish Demo were basically:
To check the accuracy and performance of forecasting algorithms in the domain of consumption,
production and “presumption” of the Consumers connected to the grid.
To start considering to incorporate on regular basis external information, meteorological data and
forecast basically, to the grid O&M systems.
7.6.2 EVALUATION
Spanish demo energy consumption and production data was used from a SS with Consumers and
Prosumers with detail of the geographical location of the DERs estimated. This information was
completed with meteorological measurements and forecasting, and the reference working calendar
related to the meteorological information.
With those data, the tool performed properly from the technical point of view providing coherent
results, even considering the provisional nature of the consumption and generation information applied
for comparison.
7.6.3 CONCLUSIONS AND FUTURE OPPORTUNITIES
The tool can clearly help in the estimation of the SSs work conditions in expected situations of gri d
stress. Even, its results can be analysed in combination and coordinated to the results of other tools like
the Demand Response Simulation tool or the Overload Forecasting System. This would be a way of
checking in the future the performance of these tools allowing the updating of the algorithm
configuration and tuning to the changing data conditions of the grid.
It is worthy to highlight also the benefits of integrating meteorological information in the grid estimation
processes, more relevant even in the expected medium term scenario of massive DER connected to the
grid.
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8. BUSINESS PROCESSES IMPACT
8.1 LV OPERATION AND MAINTENANCE
Iberdrola organises the network operations in five regions 33. The Spanish demonstrator is under the
responsibility of the North Region34 which control centre is located in Iberdrola’s premises in Bilbao.
Network operation personnel are responsible for the LV network in a defined region. This includes both
planned and unplanned works, trouble calls and incidents. They have a territorial organization (i.e.
Distribution Territorial Units (DTUs)) that facilitates a quicker response to faults and incidents and also
reduces travel time for routine work. Each of these units has a main depot, providing accommodation
for office based staff and stores to meet the local operational requirements. Typically there is a team
leader (LV Maintenance Supervisor) under responsibility of all field staff (LV Field Engineers or LV Field
Crews) in each particular DTU. LV Field Crews can be either belongs to Iberdrola Distribución or to
contractors. During working hours the LV Maintenance Supervisor investigates, prioritises incidents and
distributes the tasks to LV Field Crews in an optimised manner. Unplanned faults and incidents are
automatically assigned to the teams in the field according to the automatic choice made by an algorithm
within the Work Orders Management System (WMS). Outside the normal working hours, the Field
Crews work autonomously to cover urgent unplanned outages. In case of UPGRID, the distribution
network is covered by two DTUs : Bilbao and Baracaldo (covering the areas delimited by the red and blue
contours respectively in Figure 9). Thanks to the work developed in the Spanish demonstrator regarding
the LV NMS (desktop and mobility solutions) new opportunities for LV O&M can be evaluated.
Currently, LV incidents in the Iberdrola’s distribution network in general and in the demonstration area
in particular, are managed following a defined flowchart in order to restore the service to the affected
Consumers as quickly as possible. Incidents are registered in the existing Outage Management System
(OMS) triggered from different origins, mainly Consumers who call saying that they are suffering a
supply outage or through manually entries done by Control Centre Operators. Then, Field Crews are
assigned to each incident manually by the control centre staff or automatically thought the existing
mobile solution. Then, Field Crews are in charge of locating, assessing and restoring the service. They
rely on the communication with the Control Centre that use the information they have available from LV
network what, before UPGRID, is not as complete and accurate as it is for MV. This fact introduces some
inefficiency in the process that lead to pass less precise information passed to Consumers. Moreover,
each LV incident restoration report does not provide detailed accounts of the intermediate actions
taken to solve an incident and the number of Consumers that have restored the supply (i.e. lack of
information regarding each individual interruption). Before UPGRID, only two significant times are
registered: the incident starting time (i.e. mainly first Consumer call reporting the lack of electricity
33 Each region has one Distribution Control Centre managing approximately 2 million supply points (apart from the East region that has two for the higher number of supply points).
34 Geographically, the North region covers a total of five provinces: Vizcaya, Guipúzcoa, Araba, Navarra and La Rioja.
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supply) and the incident closing time (i.e. when the service is recovered to the last Consumer affected).
The work developed in the Spanish demonstrator is intended to enhance the latter process mainly by
providing access to update information at any moment to all actors involved. That is, incorporating
smart meter events to trigger incidents automatically, using the offered LV NMS operation ca pabilities
(e.g. tracing features, smart querying and comprehensive event management) and integrating it with
other Iberdrola third parties systems (e.g. GIS, OMS, AMI and SCADA). Moreover, these developments
allow having structured information about the evolution of an incident what facilitates real time
knowledge about its status being of value for O&M and to inform Consumers about it. Then, LV
incidents can be managed more effectively and efficiently as explained, among other benefits in
Chapters 2 and 3.
Iberdrola is evaluating subcontracting all the work related to LV. In this sense a pilot35 was launched
with successful results that would support this new business process. To be successful, new tools are
required to allow Field Crews complete the full cycle that the LV works and incidents involve. It is here
where UPGRID Spanish demonstrator development plays a relevant role. The LV NMS would be the
pivotal tool to do that. This makes the system necessary. Therefore, Iberdrola is planning to have a LV
NMS deployed and in full operation covering the entire LV network under its responsibility in Spain
before 2020 that will be based on the results obtained in the UPGRID demonstrator.
The philosophy would consist in equipping Field Crews with a tablet. These people can be DSO
personnel or subcontracted. The mobile device will allow them performing analysis about the incident
(e.g. requesting meter measurements on demand) before moving to any place along the LV network to
pinpoint the origin as much accurately as possible. This clearly will reduce the displacement for example
in those cases when the incident is originated in an installation which is not under the responsibility of
the DSO. It has been observed, a corroborated with demonstrator data, that around 40% of the calls
that arrive to the DSO are not related to incidents under its responsibility. Apart from the Mobile filter
applicability, the call centre filtering procedure will be reviewed to minimise the number of calls that
should not be arrived to the Field Crews. All this will add even more efficiency on the business process.
Moreover, as mentioned in this document, Field Crews will be able to reflect all field works in the mobile
device being storage in a central system accessible by all the maintenance responsible. Having the
possibility of performing DPF, it will allow them evaluating different alternatives to restore the
electricity supply after an incident.
It is expected a single system (i.e. the LV NMS) with the full Iberdrola LV network being the data
centralised in a unique data base. There will be central workstations from where maintenance
responsible will access to, for example, monitoring the field work. Above all, the main contribution will
be the mobile solution to manage the LV works and incident from the moment an event is recorded
until they are closed. Moreover, based on the Field Crew location, the work order will be automatically
dispatched to the closer one.
35 This is out of the scope of the UPGRID project.
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Based on the UPGRID Spanish demonstrator experience those Field Crews that have used the LV NMS
solution are satisfied. They considered that the tool is useful. During the operation test the Field Crews
have used in parallel the current mobile devices [a mobile phone in which received and close the LV
incidents; and a PC to have access to the graphical interface of the GIS (static view of the LV network)]
and the UPGRID LV NMS Mobile solution. In the near future they will one have one single tablet.
8.2 PRIME MULTISERVICE
The Spanish demonstrator has developed several activities in order to identify the suitability of remote
control over PLC PRIME network, taking into account the standard operation over current PRIME
networks, which is AMI data transport (see section 4.2). The approach is testing current RTUs connected
to a PRIME gateway, in order to test this solution for future RTUs, which will support PRIME technology.
LV remote control extension is assumed within the smart grid functions. MV remote control is well
integrated in the electricity grid operation, although not present in all SS. Its purpose is to get
information of the grid as well as to operate the grid elements (e.g. switches) remotely and safely.
Remote controllable points in the LV grid will allow the same mode of operation in this LV segment of
the grid. Remote control traffic could make use of current PRIME network in order to be transmitted
sharing the channel between AMI and remote control traffic.
If the SS does not have enough GPRS/3G coverage to let the router establish a good connection with the
AMI Head System, DSO would have to deploy an alternative transmission method (often expensive),
such as a proprietary optical fibre link. Within the scope of UPGRID project it has been demonstrated
that using the GTP capabilities to provide an IP connection over PRIME network, DSOs can avoid the
investment in other transmission methods. Remote access would therefore be offered from a meter
room nearby that would have enough GPRS/3G coverage and GTP would offer the last step to the AMI
data concentrator over IP over PLC PRIME.
A multiservice PLC PRIME network as deployed in the scope of the project would enable the LV backup
feeder smart switches as a future business process. Although there were no SS in the demonstrator area
that allowed field deployment of this concept, the idea can be supported with the capabilities
demonstrated within the project.
The approach followed here is to use remote control application (IP over PRIME) in order to manage this
feeder switching and therefore SS switching. This is an alternative future approach that would be
applicable in scenarios where mesh LV networks are available. It requires SS with LV backup feeders
where remote control for smart-switching is applicable. This means that there should be LV points in the
network where a second LV feeder from a backup SS arrives. This kind of LV grid allows switching to a
backup SS in case of supply faults or anomalies in its main SS.
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8.3 PRIME MANAGEABLE: REAL TIME AMI FAULTS DETECTION
Real time information is needed to optimise the operation of the grid providing instantaneous
information of the meters connected to the SSs. SNMP based Network Management System (NMS)
specified, developed and deployed within the project improve AMI fault detection available at the
moment.
This implementation over a massive PLC PRIME deployment would offer detailed PRIME information.
The use of this information allows the detection of faults in the feeders (e.g. broken conductors),
voltage control in the distribution transformer, and tampering detection. Some of these applications are
not feasible at a reduced cost with non-PLC technologies.
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9. CONCLUSIONS
The present document highlights the evaluation and opportunities identified during the performance of
the UPGRID Spanish demonstrator for each of the working lines covered on it (see Figure 7). Detailed
conclusions can be found along the chapters that form this deliverable which are complemented with
those included provided in [1][2][3]. A summary of them is presented next.
First at all, the LV monitoring and controllability enhancements deployed in the demonstrator (e.g.
smart meter data utilisation, LV sound network representation, LV NMS and LV control over PRIME)
contribute to have more accurate knowledge and management capabil ities of the LV network in near-
real time, being the monitoring capability one of the key enabler that allows the rest of new
functionalities.
Results and experience obtained in the UPGRID Spanish demonstration are qualitative and quantitative
steps forward to the LV O&M business process more decentralised oriented where all staff involved has
real time access to update information. In this scenario, that has already started taking shape, the
demonstrator has confirmed the need of managing a better representation and O&M of the LV network.
Firstly, from the control room point of view; secondly, and most important, it has also been confirmed
the benefits of a LV NMS that provides Field Crews with a Mobile solution incorporating a series of
capabilities. This has started introducing value added (to DSOs, Consumers and other electricity sector
stakeholders) and increase the efficiency on LV O&M. The demonstrator has facilitated the definition of
this set of functionalities. Tests results and feedback received from field users show encouraging insights
to back the expectations articulated at the beginning of the demonstrator to continue working in this
direction. The latter feedback indicates that there is still room for further improvement as well. This has
allowed identifying new enhancements and opportunities to be implemented, what stresses the
importance of having a flexible system to add new requirements. All this is already being gathered in a
system specification that will be the base for the LV NMS that will cover the full Iberdrola LV network.
The LV NMS developed in the demonstrator has been able to manage successfully the increase of data
arisen after approximately doubling the original intended LV network covered by the system. Then, it is
concluded that extending the LV NMS to new areas could standardise the system for the whole
Iberdrola, and business processes are not tied up in specific geographical areas, so it should be possible
to extend the current demonstrator experience and know-how.
Main Iberdrola LV NMS users that can now benefit from the new capabilities of the demonstration are
the LV Maintenance Supervisor and LV Field Engineer. The LV Maintenance Supervisor is responsible for
the investigation, prioritisation and distribution of incidents. The Field Engineer works in a crew handling
a mixture of outages and work orders. LV Maintenance supervisors can now: ensure that the asset and
topology information of the LV network is accurate and up to date, introduce temporary changes,
manage unplanned and planned LV outages and investigate LV outages. LV Field Engineers can now: be
assigned outages, introduce temporary changes, manage unplanned LV outages, manage planned LV
outages, investigate LV outages, re-configure feeders.
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The deployment of multiple interfaces between LV NMS solution and Iberdrola’s existing systems
provides the LV NMS user with relevant information. Some of them are real time interfaces and are
reflected on the LV diagram. The LV NMS user, either using the Desktop or Mobile solution, has a full
understanding of the real status of the LV network at any point in time. The LV diagram conveys visual
information to the user who is better equipped to take decisions regarding the network operation. Plus
the ability to poll the smart meters on site from the Mobile device enhances the information.
The UPGRID Spanish demonstrator contributions improve PRIME subnetwork capabilities. The solutions
validated in the demonstrator have a direct impact into the DSO operation and final Consumer. The aim
has been mainly to take advantage of the telecommunications infrastructure that is deployed for smart
metering purposes offering further information and services that increase its value and usability.
Specific developments have been done in order to validate these functionalities (e.g. LV remote control
can be enabled over existing AMI deployments).
The development of a LV grid remote control over AMI PLC PRIME infrastructure is feasible. A
multiservice PRIME subnetwork can be enabled, as demonstrated during the deployment and testing
phase. The conclusions of this characterization determine that IP over PRIME is a feasible alternative to
transport RTU control traffic using PLC PRIME as a channel. PLC PRIME specification has been analysed
and a ticket proposal has been opened within the PRIME Alliance in order to optimise remote control
data exchange over PLC PRIME. It has been critical to maintain PRIME requirements of a standard and
interoperable network. Within the demonstrator a new LV Remote Control Profile is proposed with its
own capabilities.
This UPGRID multiservice subnetwork requires a higher level of monitoring and knowledge of the PLC
channel. This PRIME monitoring is the first step that enables a PRIME multiservice network. This is a key
enabler then for the control traffic exchange over PLC PRIME developed also in the scope of UPGRID
demonstrator area. A SNMP protocol for managing PRIME devices has been designed, specified,
developed and validated successfully within the demonstrator. The development and integration of
these elements have been validated both in laboratory and in the field (demonstrator area). At this
stage, with the implementation done, PRIME monitoring is ensured.
Regarding smart meter event analysis, it has proven to be promising to network operation. However,
there is still scope to take full advantage of them. On the one hand, the smart meter events offer the
DSO the capability of automatically receiving information about LV network incidents. In the light of the
performed survey, this information could be valuable to enhance the network operation. On the other
hand, using this information involves the technological challenge of dealing with a high quantity of data
which should need the application of big data analytics techniques. The methodology tested for event
analysis will be used for identifying the most useful functionalities and set up parameters to specify,
based on that, new modules in the MSMS.
The technical performance of the developed web tool for managing energy consumption rationally by
Consumers has behaved as expected. Some enhancements have been identified for future versions.
From the social point of view of this line of work, it has been seen that in general, the public has a fairly
limited understanding of smart grids. The same is true of their knowledge of the electricity market and
the way electricity retail prices are set. It therefore seems clear that providing ordinary people with
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tools that help them to understand their current consumption and the cost of electricity in their home
or workplace, could be useful for decision-making in this regard.
It is true that the decisions made using such web-based tools often do not lead to major energy and
economic savings in individual homes. However, when viewed from the perspective of society in
general, these small individual contributions do help to save energy and to reduce the overall energy
consumption as well as flattening the electricity demand curve. It is stressed the importance of
providing Consumers with more and better information on energy in general and electricity in
particular. This will enable them to decide what domestic appliance they want to buy and how they use
it at home. It will also allow them to manage the way they contract their electricity supply. It is also
important to promote the installation of new units that will give additional information on energy use.
Smart meters are already being installed and will be in widespread use in the near future. To sum up,
society needs to be well-informed and have tools that help reduce energy consumption and cut energy
bills. The use of web-based tools, such the one being developed in the demonstrator, can support this.
With respect the technology-based innovation tools, some conclusions can be issued:
The opportunity and potential benefits of the application of advanced data processing
techniques, especially those related to artificial intelligence and data analytics, to the
tremendous amount of data produced practically in all segments of the MV/LV grid.
The promising benefits of the progressive introduction of already presently affordable and
reliable estimation and forecasting processes that allow to foreseen in advance s pecific
situations or behaviour of the grid, the connected DER to it and the different electricity market
actors. This advanced knowledge will clearly support and help in the DSO decision taken for a
proper grid management and planning and an active and effective participation of other related
actors, specially the Consumers and Retailers.
It is considered interesting the possibility of performing analysis of combined data coming from a
different grid segment to that studied. This is especially applicable to the AMI and smart meters
events that, as demonstrated in the Spanish demonstrator, can be applied to several processes
of grid O&M (phases an lines identification and connectivity, outage management, SS
simultaneity factor estimation, overload estimation and detection, etc.) and DER
characterization.
Due to that capability and opportunity of several applications using the same set of data, the
(CIM based) standardization has been observed as crucial in order to maximise the reutilization
of those data and the scalability, replicability and interoperability among those applications.
Last, but not least, it is interesting to summary the impact of the demonstrator results on partners a part
from Iberdrola. They are as follows.
GE, as provider of the LV NMS solution, has gained a better understanding of the Iberdrola IT and OT
environments, and the Iberdrola business processes regarding how to operate the LV network. This
knowledge is key for GE as an input to set up the future strategy of GE GIS and ADMS products suit
roadmap. At the same time, GE has certainly managed to improve current releases of Smallworld (GIS)
of PowerOn (ADMS) product suites, as a real outcome of UPGRID project, mainly the following: The
product capabilities for CIM data format importing of electrical networks into the LV NMS (a real and
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significant extension of a LV network diagram, using CIM format, has been successfully imported in the
demonstrator). The product data model and end user interface to manage smart meters data coming
from AMI, and the new capabilities of the mobile solution to manage temporary elements (cut and
jumpers) and switching operations (insert/eliminate fuse) in the field, improving the interface between
the Desktop and Mobile solutions. This experience helps GE products to be better serve current
requirements of the DSO´s, in the area of O&M of LV grids; from the perspective of both key users:
control room engineers and field engineers.
ZIV, as equipment manufacturer and provider, has the opportunity to evolve existing and standardised
products and launch new ones that include additional applications based on the UPGRID demonstrator
outcomes (e.g. PRIME GTP). The SNMP monitoring tool allows building new competencies inside ZIV.
Turn-key solutions will be demanded in the future and this knowledge will be necessary. This facilitates
ZIV being, in case of UPGRID project scope, on the technology edge of monitoring and controllability LV
solutions.
Tecnalia, as a non-for-profit RTD institution, will take their designs and developments (especially in the
scope of smart meter events analysis automation, load/consumption curves analytics and Consumer
behaviour Active Demand Response simulation) as know-how and reference implementation assets to
be applied in future projects. There will be a special attention in disseminating and transferring these
technological assets to the Small and Medium Enterprises (SMEs) industry in the domain of the smart
grids.
EVE, as an energy agency will be able to use the consumer empowerment tool resulting on the project.
The tool can evolve further with new functionalities and be adapted to extend its use to other type of
users apart from the residential Consumers.
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REFERENCES
UPGRID DOCUMENTS
UPGRID project deliverable D3.1: Tools suit for the Advanced Real Time LV network representation [1]
[confidential]
UPGRID project deliverable D3.2: Tools suite for the smart control and operation of the LV Grid [2]
[confidential]
UPGRID project deliverable D3.3: Customer Capacity building web-based system [confidential] [3]
UPGRID project deliverable D1.1: Report on Technical Specifications [confidential] [4]
UPGRID project deliverable D2.1: Functions and specification of tools for improved supervision and [5]
management of MV/LV grid [confidential]
UPGRID project deliverable D2.2: Report on Services provided by DSOs to the retail market [public] [6]
UPGRID project deliverable D2.5: Conclusions of load and generation forecasting models [public] [7]
UPGRID project deliverable D2.6: Software of Load and Generation Forecasting [confidential] [8]
UPGRID project deliverable D8.1: Report about KPIs analysis and methods of comparison [public] [9]
UPGRID project deliverable D9.1: Targeted Social stakeholders segmentation and analysis [public] [10]
EXTERNAL DOCUMENTS
Real Decree RD 1955/2000. https://www.boe.es/boe/dias/2000/12/27/pdfs/A45988-46040.pdf (in [11]
Spanish)
J. García, 2016, “Beyond Smart Meters: Management of the LV network”, CIGRE Paris 2016, [12]
Preferential Subject 2 - Sub-Topic 3: Impact of developments in energy technology, IT, big data, and
further trends in distribution systems, C6-206.
L. Garpetun, 2014, "The challenge of adapting your AMI-system for LV-grid monitoring", CIRED [13]
conference, Poster session, Theme 3: Grid operation and congestion management, 0244.
PRIME Alliance, http://www.prime-alliance.org/ [14]
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REAL DECREE 1955/2000, dated 1 December, it includes the zone classification [15]
https://www.boe.es/boe/dias/2000/12/27/pdfs/A45988-46040.pdf
ORDER IET/290/2012, dated 16 February, amending ORDER ITC/3860/2007, dated 28 December, [16]
which reviews the electricity rates as of 1 January 2008 in relation to the meter replacement plan.
https://www.boe.es/diario_boe/txt.php?id=BOE-A-2012-2538
“Technical specification of type 5 meters with remote management capabilities and time [17]
discrimination”, 2011.
Tableau Desktop https://www.tableau.com/ [18]
Mendia, I., Gil-López, S., Del Ser, J., Bordagaray, A. G., Prado, J. G., & Vélez, M. (2017, February). [19]
Optimal Phase Swapping in Low Voltage Distribution Networks Based on Smart Meter Data and
Optimization Heuristics. In International Conference on Harmony Search Algorithm (pp. 283-293).
Springer, Singapore.
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LV NMS MONITORING INFORMATION DISPLAYED Annex I.
The following figures (Figure 66 to Figure 73) present some examples when the LV NMS user is provided
with real or near real time data which is reflected on the LV network diagram.
FIGURE 66: MV VOLTAGES (VIA ICCP INTERFACE)
FIGURE 67: MV ENERGISATION STATUS (SS ENERGISED/DE-ENERGISED). IN THIS CASE THE UPPER “FAKE” SWITCH IS
OPENED AND ALL THE LV CIRCUITS DOWNWARDS ARE WITHOUT ENERGY SUPPLY (WHITE COLOUR)
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FIGURE 68: DISTRIBUTION TRANSFORMER SUPERVISION METER EVENTS ARE DISPLAYED WITH A FLASHING MARK
(İEVENTO!) NEAR THE TRANSFORMER SYMBOL
FIGURE 69: CONSUMER SMART METER EVENTS ARE DISPLAYED AS A PSEUDO CONSUMER CALL ON NEAR THE FB SYMBOL
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FIGURE 70: DISTRIBUTION TRANSFORMER SUPERVISION METER INSTANTANEOUS VALUES ARE DISPLAYED NEAR THE
TRANSFORMER SYMBOL
FIGURE 71: PENDING MAINTENANCE WORK INDICATION (“AO” TEXT)
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FIGURE 72: CONSUMER SMART METER INSTANTANEOUS VALUES AFTER AN ON DEMAND MEASUREMENTS REQUEST
FIGURE 73: SCHEDULED WORK INDICATION (“SCHEDULE” TEXT)
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FIGURE 74: CONSUMER SUPPLY POINT SYMBOL (FUSE BOX)
FIGURE 75: EXAMPLE OF AN INCIDENT REPORT PREPARED BY THE LV NMS REPORTING TOOLS
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MULTISERVICE PRIME SUBNETWORK: USE CASE 1 Annex II.
FIELD DEPLOYMENT
ANNEX II.1 UPGRID CABINET MODEL 1
Figure 76 shows the schematics and dimensions of the UPGRID portable cabinet that has been used for
the LV remote control over PRIME field test scenarios (use case 1).
FIGURE 76: PORTABLE CABINET TYPE 1 TO BE USED FOR UPGRID TESTING
ANNEX II.2 SIMULTANEOUS AMI AND IP OVER PRIME TRAFFIC
This field deployment and validation was done over a SS within the demonstrator area. This section
describes the tests performed over the selected location. Figure 77 shows the operation web page of
the AMI data concentrator to be used for AMI data sending while UPGRID IP over PRIME traffic is also
transmitted. This AMI data concentrator had nine smart meters installed and accessible, that will be
used for data reading. This was the initial status of the AMI data concentrator with the smart meters
accessible.
FIGURE 77: SCREENSHOT FROM THE AMI DATA CONCENTRATOR BEFORE THE TESTS
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The goal of this initial test plan is to evaluate the channel usage sharing the media between AMI data
and remote control data. These are the main steps executed during the test plan.
- GTPs in the portable cabinet are upgraded to the latest UPGRID functionality version available
at the moment (identified as 3.23.80.36937).
- GTPs are integrated and configured with correct addresses in the same Iberdrola Virtual Local
Area Network (VLAN) as the devices under test. In this case it will be an AMI data exchange
test so the selected VLAN should be the one being used by the AMI data concentrator in that
location.
- Disable the PLC PRIME base node internal to the AMI data concentrator and make the
configuration required so the GTP with master role in UPGRID portable cabinet takes the role
of base node of that AMI network.
- Connect UPGRID cabinet so remote access is ensured.
- Measure_1: Force AMI traffic and check that results are successful. First iteration is done
without IP over PRIME traffic in parallel.
- Please note that AMI traffic for this measure is forced uploading this xml cycle to the AMI data
concentrator.
<cycles>
<cycle name="PRIME_11901122160_TORREABANDOIBARRA2_2" period="1" immediate="true"
stop="2017/08/14 22:03">
<get obis="0-0:1.0.0.255" class="8" element="2"/>
<get obis="1-0:99.1.0.255" class="7" element="2"
selective_access="structure{structure{long_unsigned{8}octet_string{00 00 01
00 00 ff}integer{2}long_unsigned{0}}date_time{2017/03/27
00:00:00}date_time{2017/03/28 10:00:00}array{}}"/>
</cycle>
</cycles>
- Measure_2: Force IP over PRIME traffic and measure the maximum delay of ICMP traffic sent.
First iteration is done without AMI traffic in parallel.
- Measure_3: Force AMI and IP over PRIME traffic in parallel and compare the performance
with the two measurements taken with only a type of traffic each time.
Some screenshot capture during the field tests are shown next:
In Figure 78, once the test scenario is ready, smart meters are registered to UPGRID GTP configured
as base node, as described above. Instead of being registered directly to the AMI data concentrator,
that was the initial status mentioned at the beginning of this section.
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FIGURE 78: METERS REGISTERED TO THE UPGRID GTP ACTING AS BASE NODE (MASTER)
ZIV PRIME Manager monitoring tool is used to measure PRIME PLC topology and traffic of this
UPGRID GTP under test (see Figure 79).
FIGURE 79: ZIV PRIME MANAGER TOOL USED FOR GTP PRIME PLC DATA ANALYSIS
Measure_1: Force AMI traffic and check that results are successful. First iteration is done
without IP over PRIME traffic in parallel.
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The result of AMI readings is successful seeing the following captures and behaviour. Note
that in the capture shown below, ZIV0044410732 smart meter is read. The information
shows transmission and reception traffic from the AMI data concentrator to this s pecific
meter. Analysing Distribution Line Message Specification (DLMS) traffic exchanged the
meters of that SS are read successfully and their AMI measurements are being gathered by
the AMI data concentrator.
A capture example is shown next. It is obtained with ZIV PRIME Manager monitoring tool
connected to the AMI data concentrator. This capture option represents PRIME 4-32
convergence sublayer traffic, this is the PRIME convergence sublayer defined for DLMS AMI
traffic exchange.
[TX] 2017/03/28 10:31:56 3046.150896 - ZIV0044410732
00 | d0 18 30 00 00 00 04 c6 fb 72 6b 06 d4 8e 0c 68
10 | fb 7d b7 10 0b dc 67 df 21 f9
[RX] 2017/03/28 10:31:57 3046.736496 - ZIV0044410732
00 | d4 81 f0 30 00 00 00 04 2a 82 52 95 2e 8f 97 29
10 | 81 b3 44 89 0f 09 55 1a 7c 4d 4a dd 3f e2 31 96
20 | f8 34 58 85 3b 8a e8 13 8d c1 70 3b 50 51 d7 ab
30 | 3c 06 02 6d 82 1d c7 47 be db d2 15 69 48 69 3a
40 | 5b bf 11 19 a1 9f 34 c3 ac 7c 48 48 71 d7 6b a6
50 | 1a 42 f0 47 7d 8f bb 35 24 74 d8 21 be ef 27 e7
60 | 36 d8 48 a8 42 03 84 f5 b9 5f 3f 03 9e ad a7 d4
70 | 77 8d d6 4c 9b 2f 3d c5 a5 70 fe 5b 07 cb bb 66
80 | d0 0e f9 e5 c8 49 bd 04 6b 5e fa 22 52 06 0c da
90 | eb 3c b5 d0 42 f1 6d 32 a1 72 5c 02 89 27 52 b3
a0 | 0d 1d dc 1a 94 6a 78 2a 94 29 2a 94 45 4b 94 1c
b0 | ed f8 e2 7f 26 6f e2 1d 4e 04 38 17 60 e9 cf fe
c0 | 94 c1 a1 f4 42 e4 eb fc bb 7e ac 45 8d d0 40 a2
d0 | 23 e7 14 f3 02 28 33 ff 0f 83 4d 8e 1a a6 10 f4
e0 | b7 c1 69 24 9a 8b 25 5c c4 67 53 76 f8 f5 0b b9
f0 | 1d 9b 7f
[TX] 2017/03/28 10:31:57 3046.747184 - ZIV0044410732
00 | d0 18 30 00 00 00 05 b6 d4 c5 6a 77 88 9a 12 ca
10 | 56 ca 99 e1 33 b8 fa ce cd 33
…
Measure_2: Force IP over PRIME traffic and measure the maximum delay of ICMP traffic
sent. First iteration is done without AMI traffic in parallel. Answer time with IP over PRIME
traffic never exceeds 450 ms. A capture example is shown next.
PING 192.168.1.1 (192.168.1.1): 56 data bytes
64 bytes from 192.168.1.1: seq=1 ttl=64 time=444.450 ms
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64 bytes from 192.168.1.1: seq=2 ttl=64 time=336.175 ms
64 bytes from 192.168.1.1: seq=3 ttl=64 time=345.106 ms
64 bytes from 192.168.1.1: seq=4 ttl=64 time=327.765 ms
64 bytes from 192.168.1.1: seq=5 ttl=64 time=448.359 ms
64 bytes from 192.168.1.1: seq=6 ttl=64 time=495.704 ms
64 bytes from 192.168.1.1: seq=7 ttl=64 time=352.319 ms
64 bytes from 192.168.1.1: seq=8 ttl=64 time=404.869 ms
64 bytes from 192.168.1.1: seq=9 ttl=64 time=431.888 ms
64 bytes from 192.168.1.1: seq=10 ttl=64 time=334.908 ms
64 bytes from 192.168.1.1: seq=11 ttl=64 time=403.241 ms
64 bytes from 192.168.1.1: seq=12 ttl=64 time=473.310 ms
64 bytes from 192.168.1.1: seq=13 ttl=64 time=334.398 ms
…
Measure_3: Force AMI and IP over PRIME traffic in parallel and compare the performance
with the two measurements taken with only a type of traffic each time. The results of AMI
readings during this test are also successful. IP over PRIME traffic with AMI traffic in parallel
maintains the same latency in general 450 ms with punctual packets arriving around 1.500
ms. In any case traffic exchange is successful and the concept is validated, both types of
traffic can coexist. A capture example is shown next:
PING 192.168.1.1 (192.168.1.1): 56 data bytes
64 bytes from 192.168.1.1: seq=0 ttl=64 time=392.997 ms
64 bytes from 192.168.1.1: seq=1 ttl=64 time=428.452 ms
64 bytes from 192.168.1.1: seq=2 ttl=64 time=399.221 ms
64 bytes from 192.168.1.1: seq=3 ttl=64 time=408.426 ms
64 bytes from 192.168.1.1: seq=4 ttl=64 time=470.912 ms
64 bytes from 192.168.1.1: seq=5 ttl=64 time=1698.298 ms
64 bytes from 192.168.1.1: seq=6 ttl=64 time=905.644 ms
64 bytes from 192.168.1.1: seq=7 ttl=64 time=345.307 ms
64 bytes from 192.168.1.1: seq=8 ttl=64 time=336.857 ms
64 bytes from 192.168.1.1: seq=9 ttl=64 time=1803.291 ms
64 bytes from 192.168.1.1: seq=10 ttl=64 time=917.213 ms
64 bytes from 192.168.1.1: seq=11 ttl=64 time=450.958 ms
64 bytes from 192.168.1.1: seq=12 ttl=64 time=739.756 ms
64 bytes from 192.168.1.1: seq=13 ttl=64 time=447.108 ms
64 bytes from 192.168.1.1: seq=14 ttl=64 time=538.438 ms
64 bytes from 192.168.1.1: seq=15 ttl=64 time=742.113 ms
64 bytes from 192.168.1.1: seq=16 ttl=64 time=354.730 ms
64 bytes from 192.168.1.1: seq=17 ttl=64 time=400.141 ms
64 bytes from 192.168.1.1: seq=18 ttl=64 time=360.454 ms
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64 bytes from 192.168.1.1: seq=19 ttl=64 time=1684.143 ms
64 bytes from 192.168.1.1: seq=20 ttl=64 time=678.848 ms
64 bytes from 192.168.1.1: seq=21 ttl=64 time=384.768 ms
64 bytes from 192.168.1.1: seq=22 ttl=64 time=2229.461 ms
64 by
tes from 192.168.1.1: seq=23 ttl=64 time=1233.530 ms
…
ANNEX II.3 REMOTE CONTROL TRAFFIC OVER PLC PRIME
The goal of this advanced test plan is to exchange control data with the RTU over this IP over PRIME
architecture.
Figure 80 shows some images taken during the field validation process at TORRE ABANDOIBARRA 2 SS.
FIGURE 80: IMAGES OF THE REMOTE CONTROL TRAFFIC TEST PERFORMED AT TORRE ABANDOIBARRA 2 SS
First of all, there are some addressing conditions and limitations that required a local SCADA system for
the testing. It was not possible to enable the access to the RTU through the UPGRID GTPs portable
cabinet from the remote operation SCADA system.
Therefore, Figure 81 shows the initial situation of the SS under test.
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FIGURE 81: INITIAL SETUP AT TORRE ABANDOIBARRA 2 SS BEFORE THE TESTING
And Figure 82 shows the architecture set for the test scenario required for UPGRID remote control
validation over PLC PRIME. Note that both GTPs represented in blue are the devices mounted inside the
UPGRID portable cabinet (see section 4.2.2.1).
FIGURE 82: REMOTE CONTROL TRAFFIC TEST SETUP AT TORRE ABANDOIBARRA 2 SS
Over the architecture described, these are the main steps executed during the test plan.
- GTPs in the portable cabinet are upgraded to an improved UPGRID functionality version
developed for this advanced testing (identified as 3.23.80.38260). This is considered the last
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UPGRID GTP version and therefore is given a manufacturing number
4WF01650009_upgrid_RC1, as shown in the screenshot taken during the field testing.
FIGURE 83: FINAL UPGRID FIRMWARE VERSION FOR MULTISERVICE CAPABILITIES OVER GTP
- GTPs are integrated and configured with correct addresses in the same Iberdrola VLAN as the
devices under test. In this case it will be a RTU control data exchange test so the selected
VLAN should be the one being used by the RTU in that location. IP over PRIME functionality
developed for UPGRID project should be enabled.
FIGURE 84: UPGRID MASTER GTP CONFIGURATION, INTEGRATED INTO THE PORTABLE CABINET
- Disable the AMI data concentrator as this test section is oriented to control traffic only and
make the configuration required so the GTP with master role in UPGRID portable cabinet
takes the role of base node.
- Connect UPGRID cabinet so the local SCADA can access the RTU.
- First connect the local SCADA directly to the RTU to ensure the communication. Then connect
it as the architecture shown in the test setup for further measurements.
- Measure_1: Force IP over PRIME traffic with ICMP packets to make sure the channel is
established.
- Measure_2: Connect the local SCADA to the RTU through IP over PRIME. This would validate
UPGRID concept for remote control traffic exchange over PLC PRIME. 104 control traffic is
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starter although due to addressing limitation further exchange is rejected. Anyway,
bidirectional 104 traffic is exchanged and the concept is validated.
- Measure_3: Connect the local SCADA an RTU simulated in a second PC (WinPCPau test tool).
See below some captures examples taken during these field tests.
Measure_1: Force IP over PRIME traffic with ICMP packets to make sure the channel is established.
A capture example is shown next:
C:\Windows\system32>ping -t 10.159.162.250
Haciendo ping a 10.159.162.250 con 32 bytes de datos:
Respuesta desde 10.159.162.250: bytes=32 tiempo=227ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=251ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=362ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=242ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=270ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=245ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=265ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=277ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=278ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=359ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=263ms TTL=63
…
Respuesta desde 10.159.162.250: bytes=32 tiempo=262ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=291ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=274ms TTL=63
Respuesta desde 10.159.162.250: bytes=32 tiempo=254ms TTL=63
Estadísticas de ping para 10.159.162.250:
Paquetes: enviados = 208, recibidos = 208, perdidos = 0
(0% perdidos),
Tiempos aproximados de ida y vuelta en milisegundos:
Mínimo = 211ms, Máximo = 1809ms, Media = 292ms
Control-C
Measure_2 and_3: Connect the local SCADA to the RTU through IP over PRIME. This would validate
UPGRID concept for remote control traffic exchange over PLC PRIME.
These are some screenshots taken during the remote control data exchange over PLC PRIME. These
captures were taken from the Iberdrola’s personnel PC used for the local control SCADA system. This PC
was represented at the top right hand corner in Figure 82.
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FIGURE 85: REMOTE WEB CAPTURE OF THE SS UNDER TEST
FIGURE 86: IBERDROLA SPECTRUM CONFIGURATION CHANGE TO ALLOW LOCAL SCADA ACCESS
FIGURE 87: RTU SIMULATED IN A PC OVER WINPCPAW, INSTALLED ALSO AT THE SS UNDER TEST
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FIGURE 88: WIRESHARK CAPTURE OF 104 CONTROL TRAFFIC OVER IP OVER PLC PRIME
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MULTISERVICE PRIME SUBNETWORK: USE CASE 2 Annex III.
FIELD DEPLOYMENT
ANNEX III.1 UPGRID CABINET MODEL 2
Figure 89 shows the schematics and dimensions of the UPGRID portable cabinet that has been used for
simulating SS locations where the SS does not have enough GPRS/3G coverage to let the router establis h
a good connection with the AMI Head System (use case 2).
FIGURE 89: PORTABLE CABINET TYPE 2 TO BE USED FOR UPGRID TESTING
ANNEX III.2 IP OVER PRIME AS AN ALTERNATIVE TO A SS WITHOUT
REMOTE ACCESS
This field deployment and validation was done over a SS within the demonstrator area. This section
describes the tests performed over the selected location. Figure 90 shows the operation web page of
the AMI data concentrator to be used for simulating a SS without remote access. This AMI data
concentrator had 491 smart meters installed and accessible. This was the initial status of the AMI data
concentrator with the smart meters accessible.
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FIGURE 90: SCREENSHOT FROM THE AMI DATA CONCENTRATOR BEFORE THE TESTS
Figure 91 shows the test scenario to be validated in the field.
FIGURE 91: USE CASE 2 TESTS SETUP AT MIRIBILLA 6 SS REPRESENTING A SS WITHOUT WAN COVERAGE
These are the main steps executed during the test plan.
- GTPs in the portable cabinet are upgraded to an improved UPGRID functionality version
developed for this advanced testing (identified as 3.23.80.38260). This is considered the last
UPGRID GTP version and therefore is given a manufacturing number
4WF01650009_upgrid_RC1, as shown in the screenshot taken during the field testing.
- Install GTP cabinet in the SS next to the AMI data concentrator.
- Adapt the configuration so PRIME node of the AMI data concentrator is disabled and the
internal base node of the GTP in the SS acts as base node of the network instead.
- Confirm that the network converges with this new topology configured.
- Install the second GTP as another meter in the meter room selected for WAN access testing.
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- Confirm that this GTP is registered as service node (slave) to the GTP installed in the SS.
FIGURE 92: SCREENSHOT FROM THE GTP IN THE METER ROOM (SLAVE) REGISTERED TO THE GTP IN THE SS (MASTER)
- Adapt the configuration and routing of all the elements so IP over PRIME channel is ensured.
- Measurement_1: Remote access to the AMI data concentrator from the meter room where
the GTP with WAN access is installed. This is IP over PRIME traffic.
- Measurement_2: Remote access to the AMI data concentrator from the meter room where
the GTP with WAN access is installed while AMI reading data is exchanged. This means that IP
over PRIME traffic and AMI traffic are exchanged simultaneously.
- Measurement_3: Remote access to the GTP with WAN access installed in the meter room
from the AMI operation system at Iberdrola premises. This ensures the last step of WAN
remote accessibility.
- Measurement_4: Remote access to AMI data concentrator from the AMI operation system at
Iberdrola premises, being the first step the WAN connection to the GTP with WAN access
installed in the meter room. This measurement is not possible due to routing limitations in
Iberdrola field operation networks.
These are some pictures taken during the field validation process at MIRIBILLA 6 SS and the meter room
selected for WAN access to the GTP that is Gernikako Lorategia,3.
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FIGURE 93: USE CASE 2 TEST PERFORMED AT MIRIBILLA 6 SS
FIGURE 94: USE CASE 2 TEST ENABLING WAN ACCESS FROM THE METER ROOM OF GERNIKAKO LORATEGIA 3
See below some captures examples taken during these field tests.
Measure_1: Remote access to the AMI data concentrator from the meter room where the GTP with
WAN access is installed. This is IP over PRIME traffic. A capture example is shown next. Note that
the following web server images are uploaded at the meter room being the IP traffic exchanged
over PLC PRIME from the SS.
GTP installed at the meter
room enabling WAN access
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FIGURE 95: CONNECTION PROCESS TO THE AMI DATA CONCENTRATOR FROM THE METER ROOM
Measure_2: Remote access to the AMI data concentrator from the meter room where the GTP with
WAN access is installed while AMI reading data is exchanged. This means that IP over PRIME traffic
and AMI traffic are exchanged simultaneously. A capture example is shown next:
This latency test shows that access is possible although there are some packets that do not arrive to
the destiny. 13% of the packets in this scenario are lost.
C:\Windows\system32>ping -t 10.159.164.169
Haciendo ping a 10.159.164.169 con 32 bytes de datos:
Respuesta desde 10.159.164.169: bytes=32 tiempo=2041ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=490ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=362ms TTL=63
Tiempo de espera agotado para esta solicitud.
Respuesta desde 10.159.164.169: bytes=32 tiempo=932ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=335ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=1666ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=3527ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=3063ms TTL=63
…
Tiempo de espera agotado para esta solicitud.
Respuesta desde 10.159.164.169: bytes=32 tiempo=2512ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=3775ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=545ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=399ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=467ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=1742ms TTL=63
Respuesta desde 10.159.164.169: bytes=32 tiempo=385ms TTL=63
Estadísticas de ping para 10.159.164.169:
Paquetes: enviados = 67, recibidos = 58, perdidos = 9
(13% perdidos),
Tiempos aproximados de ida y vuelta en milisegundos:
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Mínimo = 325ms, Máximo = 3775ms, Media = 1287ms
Measure_3: Remote access to the GTP with WAN access installed in the meter room from the AMI
operation system at Iberdrola premises. This ensures the last step of WAN remote accessibility. A
capture example is shown next:
FIGURE 96: SCREENSHOT FROM THE AMI OPERATION SYSTEM DURING THE GTP WAN ACCESS
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MANAGEABLE PRIME SUBNETWORK (SNMP Annex IV.
MONITORING) FIELD DEPLOYMENT RESULTS
The following figures present some examples of the PRIME SNMP web based tool. At first, the idea was
to perform the tests in 40 real data concentrator but, due to connectivity issues in 4 of them, finally only
data from 36 data concentrators had been collected. These 40 AMI data concentrators are installed in
40 SSs that feed, in total, some thousands of residential Consumers. The list of 40 data concentrators in
the demonstrator area is shown in Table 22:
TABLE 22: LIST OF SS INVOLVED IN THE FIELD TEST OF THE MANAGEABLE PRIME SUBNETWORK
SS Code Name Number of
Consumers
200007470 GOIKOETXEA-VILLABASO 128
200000750 COOPERA.IPARRAGIR-BI 220
200005490 FINAL PRIM 151
901121590 PRIM- DOLARETXE 158
200002180 PLAZA INDAUTXU-BI 226
200001220 TRAVESIA VERDEL 710
200000200 SINDICAL 232
200000391 FINAL SAN FRANCISCO 277
200001940 HERMANAS CARMELITAS 537
200006990 ETXEANDIA-BILBAO 280
200004520 BIZKARGI 503
200004790 LEDESMA LEKERIKA 194
200007070 CASERIO ARBOLAGANE 280
200000210 ALHONDIGA-URKI/IPARR 304
200000610 SANTIAGO-BILBAO 322
200004400 B. ETXANIZ M. OREJA 332
200007501 VIVIENDAS FONTAN 342
901120210 INDALECIO PRIETO 396
901122620 BENIDORM 143
901101370 VENECIANA 401
200007580 LANDABASO-BILBAO 408
200007400 IBARREKO 1-BI 411
200001110 CAMPA EL MUERTO 419
200000970 NUEVA AURORA 432
200001080 ADORATRICES 357
200002470 VIVIENDAS AGARRE 432
200001750 ARTABE VDA. 433
200007720 ETXEA 442
200001810 MEDIA LUNA 443
200007610 TORREMADARI.J.CRUZ 448
200002720 CAMINO TUTULU 550
200003480 E.C.POZA 55 157
200003520 COCHERITO DE BILBAO 516
200003820 SANTA MARTA 2 324
200004570 ARABELLA II 345
200004880 TR.VERDEL-TR.CARMELO 537
200004910 F. DEL CAMPO-BI 363
200007080 BADAJOZ-L.GOIKOETXEA 243
901120060 MIRIBILLA 6-BILBAO 487
901121130 CONVENTO CONCEPCION 119
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Showing up next, the results from the tool with the graphics of the number of terminals (green lines)
and switches (blue lines) in each data concentrator installed in the SS listed in Table 22.
FIGURE 97: REAL DATA CONCENTRATOR A: TERMINALS AND SWITCHES
In the data concentrator shown in Figure 98 it is possible to notice that this network has an issue about
4:00 in the morning because, at that time, all the smart meters are disconnected. This issue can be
induced by the existence of either a particular noise or the data concentrator is rebooting periodically
due to some software issue. Usually, this periodic behaviour is caused by signal to noise in the network.
With this tool, these noises are easily identified and monitored, to implement solutions.
FIGURE 98: REAL DATA CONCENTRATOR B: TERMINALS AND SWITCHES
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FIGURE 99: REAL DATA CONCENTRATOR C: TERMINALS AND SWITCHES
In Figure 100, a smart meters drop is shown; this drop is due to the periodical reboot configured in all
the data concentrators. This reboot is configured to make it happen once a week in order to avoid any
kind of software issues on the device.
FIGURE 100: REAL DATA CONCENTRATOR D: TERMINALS AND SWITCHES
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FIGURE 101: REAL DATA CONCENTRATOR E: TERMINALS AND SWITCHES
FIGURE 102: REAL DATA CONCENTRATOR F: TERMINALS AND SWITCHES
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FIGURE 103: REAL DATA CONCENTRATOR G: TERMINALS AND SWITCHES
FIGURE 104: REAL DATA CONCENTRATOR H: TERMINALS AND SWITCHES
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FIGURE 70: REAL DATA CONCENTRATOR I: TERMINALS AND SWITCHES
FIGURE 105: REAL DATA CONCENTRATOR J: TERMINALS AND SWITCHES
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FIGURE 106: REAL DATA CONCENTRATOR K: TERMINALS AND SWITCHES
FIGURE 107: REAL DATA CONCENTRATOR L: TERMINALS AND SWITCHES
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FIGURE 108: REAL DATA CONCENTRATOR M: TERMINALS AND SWITCHES
FIGURE 109: REAL DATA CONCENTRATOR N: TERMINALS AND SWITCHES
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In Figure 110, a periodical drop of about 200 smart meters is shown. This drop is caused by noises in the
network but as it only affects to some smart meters, this noise has to be located in a concrete loc ation,
it can be a specific LV feeder or in a specific FB.
FIGURE 110: REAL DATA CONCENTRATOR O: TERMINALS AND SWITCHES
FIGURE 111: REAL DATA CONCENTRATOR P: TERMINALS AND SWITCHES
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FIGURE 112: REAL DATA CONCENTRATOR Q: TERMINALS AND SWITCHES
FIGURE 113: REAL DATA CONCENTRATOR R: TERMINALS AND SWITCHES
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FIGURE 114: REAL DATA CONCENTRATOR S: TERMINALS AND SWITCHES
FIGURE 115: REAL DATA CONCENTRATOR T: TERMINALS AND SWITCHES
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FIGURE 116: REAL DATA CONCENTRATOR U: TERMINALS AND SWITCHES
FIGURE 117: REAL DATA CONCENTRATOR V: TERMINALS AND SWITCHES
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FIGURE 118: REAL DATA CONCENTRATOR W: TERMINALS AND SWITCHES
FIGURE 119: REAL DATA CONCENTRATOR X: TERMINALS AND SWITCHES
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FIGURE 120: REAL DATA CONCENTRATOR Y: TERMINALS AND SWITCHES
FIGURE 121: REAL DATA CONCENTRATOR Z: TERMINALS AND SWITCHES
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FIGURE 122: REAL DATA CONCENTRATOR AA: TERMINALS AND SWITCHES
FIGURE 123: REAL DATA CONCENTRATOR AB: TERMINALS AND SWITCHES
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FIGURE 124: REAL DATA CONCENTRATOR AC: TERMINALS AND SWITCHES
FIGURE 125: REAL DATA CONCENTRATOR AD: TERMINALS AND SWITCHES
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FIGURE 126: REAL DATA CONCENTRATOR AE: TERMINALS AND SWITCHES
FIGURE 127: REAL DATA CONCENTRATOR AF: TERMINALS AND SWITCHES
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FIGURE 128: REAL DATA CONCENTRATOR AG: TERMINALS AND SWITCHES
FIGURE 129: REAL DATA CONCENTRATOR AH: TERMINALS AND SWITCHES
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REAL DATA CONCENTRATOR AI: TERMINALS AND SWITCHES
FIGURE 97: REAL DATA CONCENTRATOR AJ: TERMINALS AND SWITCHES
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MANAGEABLE PRIME SUBNETWORK (SNMP Annex V.
MONITORING) DETAILED EXAMPLE – SS 200000750
For this example a SS with two distribution transformers has been chosen. The location of the SS is
shown in Figure 130 below.
FIGURE 130: LOCATION FOR THE SS AND THE DATA CONCENTRATOR
This SS has one distribution transformer and gives service to 220 different Consumers. It has 7 LV
feeders and 2 LV switchboards. Figure 131 shows the LV switchboards.
FIGURE 131: THE TWO LV SWITCHBOARD OF THE SELECTED SS
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Figure 132 shows the data concentrator and the cabinet where it is installed in the selected SS.
FIGURE 132: DATA CONCENTRATOR
The steps to start a new data collection are as follow:
- First, the data concentrator had been upgraded and configured in order to enable its PRIME
advanced SNMP monitoring capabilities. The SNMP part of the data concentrator was
configured as follows:
FIGURE 133: SNMP CONFIGURATION IN DATA CONCENTRATOR
- Then, it was necessary to provision the data concentrator in the PRIME Management tool.
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FIGURE 134: PROVISIONING OF THE NODE IN THE WEB TOOL
FIGURE 135: PROVISIONED NODE
- Once the node was provisioned, two scheduling tasks were configured as explained in section
4.3. These two tasks were the recollection of the number of Terminals and the number of
Switches in the network.
- After 4 days, the collected data show the quality of the PRIME network ensuring the
communication during the test with all the meters. As it is shown, the number of smart meters
is around 200 (in green in the graphic) and the number of switches varies between 40 and 50
(in blue). It is important to remember that the switches are the service nodes acting as
repeaters for other service nodes in the network.
FIGURE 136: QUALITY OF PRIME NETWORK DATA STORED IN WEB TOOL AFTER 4 DAYS
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SMART METER ANALYSIS AND PROCESSING: Annex VI.
MACROS TOOLS
The following figures (Figure 137 to Figure 138) present some examples of results representation
obtained after executing the macro tools developed by Tecnalia for smart meter event analysis.
FIGURE 137: MAIN SHEET OF ONE OF THE DEVELOPED MACROS. IT CONTAINS THE EXECUTION CONFIGURATION
PARAMETERS (LEFT) AND THE SUMMARY OF RESULTS (RIGHT)
FIGURE 138: EXTRACT OF THE EXCEL TABLE RESULTED FROM EXECUTING THE MACRO THAT ANALYSES THE TIME OUT OF
VOLTAGE LIMITS AT FB LEVEL
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SMART METER ANALYSIS AND PROCESSING: Annex VII.
VIRTUAL REGISTER RESULTS
These annex provides with more examples of voltage magnitude issues at supply points analysed with
the Virtual Register (see Table 18).
ANNEX VII.1 MEASUREMENTS FROM SS_1 (UNDERVOLTAGE)
The information of the first SS (SS_1) at Table 23 is included next. There are 2 FBs in the SS_1, which is
within the worst undervoltage cases:
FB_1, with 11 smart meters.
FB_2, with 3 smart meters.
The code of smart meters belonging to each FB is shown in Table 18, while the graphical representation
obtained with the Virtual Register (Figure 139 - Figure 141) show that all these smart meters have
several measurements under the regulatory voltage (i.e. measurements are below the red line indicated
in the plots that indicates the regulatory voltage limit).
TABLE 23: SS_1 - METERS FROM WORST FB UNDERVOLTAGE
SS_NAME FB_CODE METER CODE
SS_1 FB_1 ZIV********44
SS_1 FB_1 ZIV********45
SS_1 FB_1 ZIV********49
SS_1 FB_1 ZIV********50
SS_1 FB_1 ZIV********51
SS_1 FB_1 ZIV********52
SS_1 FB_1 ZIV********53
SS_1 FB_1 ZIV********25
SS_1 FB_1 ZIV********27
SS_1 FB_1 ZIV********51
SS_1 FB_1 ZIV********22
SS_1 FB_2 ZIV********34
SS_1 FB_2 ZIV********36
SS_1 FB_2 ZIV********37
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FIGURE 139: MEASUREMENTS FROM FB_1 (PART 1)
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FIGURE 140: MEASUREMENTS FROM FB_1 (PART 2)
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FIGURE 141: MEASUREMENTS FROM FB_2
ANNEX VII.2 MEASUREMENTS FROM SS_2 (UNDERVOLTAGE)
There is only one FB in this SS classified as one of the worst undervoltage cases: FB_3. It has 11 smart
meters and their codes are shown in Table 24, while the following figures show that all these meters
have several measurements under the regulatory voltage (indicated in the plots with a red line). Some
meters such as ZIV0041963449, ZIV0041963634, ZIV0041963636, ZIV0041963638 and ZIV0043100833
have recorded fewer measurements than the average gathered by the Virtual Register. As they do not
provide useful information, their plots have not been included in this deliverable.
TABLE 24: SS_2 SMART METERS FROM WORST FB (UNDERVOLTAGE)
SS_NAME FB_CODE METER CODE SS_2 FB_3 ZIV********12
SS_2 FB_3 ZIV********44
SS_2 FB_3 ZIV********47
SS_2 FB_3 ZIV********49
SS_2 FB_3 ZIV********50
SS_2 FB_3 ZIV********52
SS_2 FB_3 ZIV********34
SS_2 FB_3 ZIV********36
SS_2 FB_3 ZIV********38
SS_2 FB_3 ZIV********96
SS_2 FB_3 ZIV********33
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FIGURE 142: MEASUREMENTS FROM FB_3
ANNEX VII.3 MEASUREMENTS FROM SS_3 (UNDERVOLTAGE)
The FB_4, belonging to SS_3 is classified as one of the worst undervoltage cases. It has 10 smart meters,
which are shown in Table 25 while the following figures show that all these meters have several
measurements under the regulatory voltage (indicated in the plots with a red line).
TABLE 25: SS_3 - METERS FROM WORST FB (UNDERVOLTAGE)
SS_NAME FB_CODE METER CODE SS_3 FB_4 ZIV********31 SS_3 FB_4 ZIV********35
SS_3 FB_4 ZIV********80
SS_3 FB_4 ZIV********87
SS_3 FB_4 ZIV********32
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SS_NAME FB_CODE METER CODE SS_3 FB_4 ZIV********34
SS_3 FB_4 ZIV********36
SS_3 FB_4 ZIV********37
SS_3 FB_4 ZIV********90
SS_3 FB_4 ZIV********95 SS_3 FB_4 ZIV********31
FIGURE 143: MEASUREMENTS FROM FB _4 (PART 1)
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FIGURE 144: MEASUREMENTS FROM FB_4 (PART 2)
ANNEX VII.4 MEASUREMENTS FROM SS_4 (UNDERVOLTAGE)
There are 2 FBs in this SS within the worst undervoltage cases:
FB_5, with 5 smart meters
FB_6, with 8 smart meters
The code of meters belonging to each FB is shown in the table, while the following figures show that all
these meters have several measurements under the regulatory voltage (indicated in the plots with a red
line). These measurements have been gathered through the Virtual Register tool .
TABLE 26: SS_4 METERS FROM WORST FB (UNDERVOLTAGE)
SS_NAME FB_CODE METER CODE
SS_4 FB_5 ZIV********00
SS_4 FB_5 ZIV********25
SS_4 FB_5 ZIV********91 SS_4 FB_5 ZIV********55
SS_4 FB_5 ZIV********60
SS_4 FB_6 GE*********42
SS_4 FB_6 GE*********89 SS_4 FB_6 GE*********74
SS_4 FB_6 GE*********55 SS_4 FB_6 GE*********70
SS_4 FB_6 GE*********20
SS_4 FB_6 LG*********57
SS_4 FB_6 ZIV********24
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FIGURE 145: MEASUREMENTS FROM FB_5
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FIGURE 146: MEASUREMENTS FROM FB_6 (PART 1)
FIGURE 147: MEASUREMENTS FROM FB_6 (PART 2)
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ANNEX VII.5 MEASUREMENTS FROM SS_5 (UNDERVOLTAGE)
There are 4 FBs in this SS within the worst undervoltage cases:
FB_7, with 2 smart meters
FB_9, with 2 smart meters
FB_8, with 1 smart meter
FB_10, with 1 smart meter
The code of meters belonging to each FB is shown in the table, while the following figures show that all
these smart meters have several measurements, gathered through the Virtual Register tool, under the
regulatory voltage (indicated in the plots with a red line).
TABLE 27: SS_5 - METERS FROM WORST FB (UNDERVOLTAGE)
SS_NAME FB_CODE METER CODE SS_5 FB_7 ZIV********23
SS_5 FB_7 ZIV********28
SS_5 FB_9 ZIV********24
SS_5 FB_9 ZIV********29
SS_5 FB_8 ZIV********17 SS_5 FB_10 ZIV********63
FIGURE 148: MEASUREMENTS FROM FB_7
FIGURE 149: MEASUREMENTS FROM FB_9
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FIGURE 150: MEASUREMENTS FROM FB_8
FIGURE 151: MEASUREMENTS FROM FB_10
ANNEX VII.6 MEASUREMENTS FROM SS_6 (OVERVOLTAGE)
There are 2 FBs in this SS within the worst overvoltage cases:
FB_11, with 8 smart meters
FB_12, with 3 smart meters
The code of meters belonging to each FB is shown in the table, while the following figures show that all
these meters have some measurements over the regulatory voltage (indicated in the plots with a red
line).
TABLE 28: SS_6 SS - METERS FROM WORST FB (OVERVOLTAGE)
SS_NAME FB_CODE METER CODE SS_6 FB_11 ZIV********33
SS_6 FB_11 ZIV********43
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SS_NAME FB_CODE METER CODE SS_6 FB_11 ZIV********75
SS_6 FB_11 ZIV********81
SS_6 FB_11 ZIV********02
SS_6 FB_11 ZIV********52
SS_6 FB_11 ZIV********56
SS_6 FB_11 ZIV********48
SS_6 FB_12 ZIV********92
SS_6 FB_12 ZIV********09
SS_6 FB_12 ZIV********35
FIGURE 152: MEASUREMENTS FROM FB_11 (PART 1)
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FIGURE 153: MEASUREMENTS FROM FB_11 (PART 2)
FIGURE 154: MEASUREMENTS FROM FB_11
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ANNEX VII.7 MEASUREMENTS FROM SS_7 (OVERVOLTAGE)
There is one FB in this SS belonging to worst overvoltage cases: FB_13. The code of its ten smart meters
is shown in the table. Besides, the following figures show that none of these meters have any
measurements over the regulatory voltage (indicated in the plots with a red line).It is also remarkable
that a number of measurements is missed along the same periods in these meters. It may be attributed
to the billing process during the nights.
TABLE 29 SS_7 SS - METERS FROM WORST FB (OVERVOLTAGE)
SS_NAME FB_CODE METER CODE SS_7 FB_13 ZIV********07
SS_7 FB_13 ZIV********97
SS_7 FB_13 ZIV********59
SS_7 FB_13 ZIV********73
SS_7 FB_13 ZIV********58
SS_7 FB_13 ZIV********63
SS_7 FB_13 ZIV********12
SS_7 FB_13 ZIV********97
SS_7 FB_13 ZIV********48
SS_7 FB_13 ZIV********30
FIGURE 155: MEASUREMENTS FROM FB_13 (PART 1)
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FIGURE 156: MEASUREMENTS FROM FB_13 (PART 2)
ANNEX VII.8 MEASUREMENTS FROM SS_8 (OVERVOLTAGE)
There is one FB in this SS belonging to worst overvoltage cases: FB_14. It has three meters, whose code
is shown in the following table.
TABLE 30 SS_8 - METERS FROM WORST FB (OVERVOLTAGE)
SS_NAME FB_CODE METER CODE SS_8 FB_14 SAG********50
SS_8 FB_14 ZIV********58
SS_8 FB_14 ZIV********59
Besides, the following figures show these meters have only a few measurements over the regulatory
voltage (indicated in the plots with a red line).
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FIGURE 157: MEASUREMENTS FROM FB_14
ANNEX VII.9 MEASUREMENTS FROM SS_9 (OVERVOLTAGE)
There is one FB in this SS belonging to worst overvoltage cases: FB_15. The code of its five meters is
shown in the table below.
TABLE 31 SS_9- METERS FROM WORST FB (OVERVOLTAGE)
SS_NAME FB_CODE METER CODE SS_9 FB_15 ZIV********02
SS_9 FB_15 ZIV********03
SS_9 FB_15 ZIV********24
SS_9 FB_15 ZIV********94
SS_9 FB_15 ZIV********95
Besides, the following figures show that none of these five smart meters have any measurements over
the regulatory voltage (indicated in the plots with a red line).
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FIGURE 158: MEASUREMENTS FROM FB_15