30/06/2019 · Accessibility ☒Public ☐ Consortium + EC ☐ Restricted to a specific group + EC...
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D8.7 Raw demonstration results based on the KPI measurements
Version 1.0
Deliverable D8.7
30/06/2019
Ref. Ares(2019)4140682 - 30/06/2019
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 2 of 45
Disclaimer: This report reflects only the author's view and the Agency is not responsible for
any use that may be made of the information it contains.
ID & Title: D8.7 Raw demonstration results based on the KPI measurements
Version: 1.0 Number of pages:
45
Short Description
D8.7 Raw demonstration of results based on the KPI measurements from five different Use Cases. All the Use Cases are based in Sweden and his version is the first demonstration of the results. The purpose of the KPIs within this deliverable are to show the potential contribution that new distributed steerable assets could have within energy systems.
Revision history
Version Date Modifications’ nature Author
V0.1 02-05-2019 Initialisation Annie Bengtsson
V0.2 03-05-2019 Report structure finalization Sebastian Jansson, Pauline Ahlgren, Jörgen Rosvall
V0.3 29-05-2019 First review of results
Helen Carlström, Karolina Ekerlund, Sebastian Jansson, Jörgen Rosvall
V1.0 28-06-2019 Revisited and updated
Helen Carlström, Karolina Ekerlund, Sebastian Jansson, Jörgen Rosvall
Accessibility
☒Public ☐ Consortium + EC ☐ Restricted to a specific group + EC
☐ Confidential + EC
Owner/Main responsible
Name Function Company Visa
Peder Kjellén WPL E.ON
Author(s)/contributor(s): company name(s)
Sebastian Jansson, Helen Carlström, Karolina Ekerlund, Jörgen Rosvall, Annie Bengtsson, Pauline Ahlgren
Reviewer: company name
Company Name
CEZ Distribuce Stanislav Hes
Approver(s): company name(s)
Company Name(s)
Enedis Christian Dumbs
Work Package ID WP 8 Task ID T8.8, T8.9, T8.10, T8.11
D8.7 Raw demonstration results based on the KPI measurements
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TABLE OF CONTENT
1. INTRODUCTION .........................................................................................4
1.1. Scope of the document ...........................................................................4
1.2. Notations, abbreviations and acronyms .......................................................4
2. SUMMARY OF KPI’S .....................................................................................5
2.1. KPI Relationships and Summary List............................................................5
3. KPI DETAILS..............................................................................................6
3.1. WP8_KPI_1: DSR Economic and Operational Impact on Distribution Network (District
Heating/Cooling) ...........................................................................................6
3.1.1. Major Results From Use Cases............................................................8
3.2. WP8_KPI_2: System Peak Load Reduction (District Cooling Grid) .......................9
3.2.1. Major Results From Use Cases.......................................................... 10
3.3. WP8_KPI_3: DSR Dispatch Quality ............................................................ 13
3.3.1. Major Results From Use Cases.......................................................... 14
3.4. WP8_KPI_4: Observability of microgrid performance .................................... 18
3.4.1. Major Results From Use Cases.......................................................... 21
3.5. WP8_KPI_5: Increase of Renewable Penetration .......................................... 22
3.5.1. Major results from the Use Case....................................................... 23
3.6. WP8_KPI_6: DSR technical availability ...................................................... 24
3.6.1. Major Results From Use Cases.......................................................... 26
3.7. WP8_KPI_7: DSR Flexibility Response Time................................................. 28
3.7.1. Major Results From Use Cases.......................................................... 30
3.8. WP8_KPI_8: DSR Potential...................................................................... 32
3.8.1. Major Results From Use Cases.......................................................... 33
3.9. WP8_KPI_9: Customer energy awareness ................................................... 38
3.9.1. Major Results From Use Cases.......................................................... 39
3.10. WP8_KPI_10: Customer Satisfaction Index.................................................. 40
3.10.1. Major Results From Use Cases.......................................................... 41
3.11. WP8_KPI_11: Customer Recruitment ........................................................ 42
3.11.1. Major Results From Use Cases.......................................................... 43
3.12. WP8_KPI_12: P2P platform participation ................................................... 44
3.12.1. Major Results From Use Cases.......................................................... 45
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1. INTRODUCTION
A first version of the raw demonstration results based on the KPI measurements review.
1.1. Scope of the document
The aim of this deliverable report is to present a raw demonstration of the KPIs within Work
Package 8. The different KPIs target separate areas of intreset that is applicable to at leact
one of the Use Cases. The report describes these KPIs and which Use Cases relates to each
KPI. Thereafter, are the results presented one by one for each specific Use Case to show an
overview of the progress so far.
1.2. Notations, abbreviations and acronyms
Table 1.1 provides an overview of the notations, abbreviations and acronyms used in this
report.
Table 1.1 List of notations, abbreviations and accronyms
API Application Programming Interface
BESS Battery Energy Storage Systems
BUG Back-up Generator
CR Customer Recruitment
DER Distributed Energy Recourses
DSR Demand Side Response
ECSI European Customer Satisfaction Index
EM Energy Manager
EMS Energy Management System
HP Heat Pump
HTW Hot Tap Water
IoT Internet of Things
KPI Key Performance Indicator
LES Local Energy System
LCOF Levelized Cost of Energy
LTE Long Term Evolution
NPS Net Promotor Score
P2P Pear to Pear
PV Photovoltaics
RES Renewable Energy Sources
TBD To Be Determined
THD Total Harmonic Distortion
UBV Unit Block Variable
UC Use Case
VIM Virtual Island Mode
WTG Wind Turbine Generator
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2. SUMMARY OF KPI’S
This chapter summarizes the measured KPI’s and to give an overview of which Use Cases
falls under wich demo, demo 4A (Use Case 1 and 2) and demo 4B (Use Case 3, 4 and 5). This
is shown in table 2.1 along the which of the KPI´s each Use Case should handle based on the
two documents Deliverable D8.1 & 8.2 and the 2nd Amendment.
2.1. KPI Relationships and Summary List
Table 2.1 Lists the KPIs and shows the relevant use cases per KPI.
DEMO 4A DEMO 4B
ID KPI Definition UC 1 UC 2 UC 3 UC 4 UC 5
WP8_KPI_1 DSR economic and operational impact on distribution network (district Heating/cooling)
x x
WP8_KPI_2 System Peak load reduction (district cooling grid)
x x
WP8_KPI_3 DSR Dispatch Quality x x x
x
WP8_KPI_4 Observability of microgrid performance
x
WP8_KPI_5 Increase of renewable penetration x x x
x
WP8_KPI_6 DSR technical availability x
x
WP8_KPI_7 DSR flexibility response time x
x
WP8_KPI_8 DSR Potential x x x
WP8_KPI_9 Customer energy awareness
x
WP8_KPI_10 Customer Satisfaction Index x
x
WP8_KPI_11 Customer Recruitment x
x x
WP8_KPI_12 P2P platform participation
x
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3. KPI DETAILS
3.1. WP8_KPI_1: DSR Economic and Operational Impact on Distribution
Network (District Heating/Cooling)
BASIC KPI INFORMATION
KPI Name DSR economic and operational impact on distribution network (district Heating/cooling)
KPI ID WP8_KPI_1
Strategic Objective To analyze the cost impact of deploying DSR in the thermal network
KPI Description
The overall (peak plants and central production) measured impact of DSR on the grid will reveal its value for money. The DSR cost should be less that the ‘Peak plant LCOE’. The time when the RES generation is in excess, the conventional peak plants will be curtailed providing the economic benefits to the operator and would result into increased penetration of renewables. This KPI intends to measure the business Case calculation of benefits achieved for specific grid areas with supply constraints, by introducing the DSR systems with flexible loads. Also with the help of modelling and simulation of the system, different scenarios can be validated for highest economic impact of the RES penetrations on distribution network.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
𝐶𝑓𝑙𝑒𝑥 =𝐶𝐷𝑆𝑅
(𝑃wo⁄_DSR − PwDSR)
𝑛𝑓𝑙𝑒𝑥 =𝐶flex
𝐿𝐶𝑂𝐸𝑝𝑒𝑎𝑘_𝑝𝑙𝑎𝑛𝑡
Variable Description
𝑛𝑓𝑙𝑒𝑥 Factor of economic benefit due to DSR flexibility
𝐶𝑓𝑙𝑒𝑥 Cost of flexibility (€/kW)
𝐶𝐷𝑆𝑅 Cost of DSR System
𝑃𝑤𝑜 ⁄_𝐷𝑆𝑅 Peak plant total production without DSR
𝑃𝑤_𝐷𝑆𝑅 Peak plant total production with DSR
𝐿𝐶𝑂𝐸𝑝𝑒𝑎𝑘_𝑝𝑙𝑎𝑛𝑡 LCOE of the peak production plant
The calculation will intend to find out the cost of flexibility offered by the loads and will be compared against the LCOE of the peak plant, which is generally the most expensive source of energy.
Unit of Measurement The unit of measurement of this KPI will be percentage base.
Expectations
The project has to determine how to measure the total production impact as this is something that has never been done. A baseline will have to be drawn to be able to measure the curtailment impact. Decrease peak energy production should be >10% Decrease at customers site >50% for >1h
Overlap of this KPI with other relevant KPIs and Use Cases
UC1, UC2
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KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
Cost of DSR System
𝐶𝐷𝑆𝑅 Direct Calculation Excel 1/year 1 year
Peak plant total production with DSR
𝑃𝑤𝐷𝑆𝑅 Simulation Simulation Data
Peak plant total production without DSR
𝑃𝑤𝑜⁄_𝐷𝑆𝑅
Simulation
Simulation
Data
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical
Values
☐
Values Measured at Start of Project
☒
Details of Baseline The values calculated at the start of the project will form the baseline.
GENERAL COMMENTS
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Major Results From Use Cases
Results from Use Case 1
The aim with this KPI is to investigate if the DSR can be cheaper than the cost of peak
production. The typical installation cost for peak generation can be estimated to 500
kEUR/MW, while the operational cost estimates to 0.1 kEUR/MWh. This can be compared to
the installation cost for the DSR system and the DSR flexibility, i.e. the avoided peak
production.
During the winter 2018-19 testing of the DSR system, the highest heat power reduction
estimated to 14,5 MW during 1 hour. This was achieved via the DSR system installed in, so
far, 24 buildings. The installation cost for one building is approximately 2 kEUR. This is the
marginal cost for each new installation and does not include R&D costs for the DSR system.
These numbers give a cost of flexibility according to the KPI of:
Cflex = (24 x 2) / 14.5 = 3.3 kEUR/MW.
While a boiler delivers fully dispatchable power, in terms of short term flexibility, the DSR
system offers a significantly lower cost, 3.3 kEUR/MWh compared to 500 kEUR/MWh.
The factor of economic benefit due to DSR flexibility then becomes:
nflex = 3.3/500 = 0.066.
To summarize, the DSR system indeed offers flexibility to a significantly lower cost than
conventional peak generation. Furthermore, when it comes to the operational cost, the DSR
system offers practically zero cost (excluding maintenance etc.) compared to the fuel cost
of a peak boiler of around 0.1 kEUR/MWh.
Results from Use Case 2
Cost flexibility has not been considered for UC2. Focus has been on energy carrier shifting.
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3.2. WP8_KPI_2: System Peak Load Reduction (District Cooling Grid)
BASIC KPI INFORMATION
KPI Name System Peak load reduction (district cooling grid) KPI ID WP8_KPI_2
Strategic Objective To achieve cost savings by activating the flexibilities which will lead to System Peak load reduction (district cooling grid) via simulation
KPI Description
With the help of simulation the baselining Case of the project will be demonstrated. Later, with the integration of thermal model in the simulation (district cooling grid), the additional system peak load reduction can be realised. The project intends to validate the impact of DSR system in reducing the maximum peaks of the load. The times when the RES is available in excess the expensive conventional source of thermal energy will be turned down, to achieve cost savings. Decrease in system maximum peak production should be >30%.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
% ΔPpeakred=
Ppeak,without − Ppeak, with
Ppeak, without
This KPI, the percentage system maximum peak load reduction, will be measured by ratio of the difference between the system peaks with and without the DSR system, to the system maximum peak without the DSR system in the district cooling grid. For better overview of the reduction and to even-out the abnormal peaks during some
special days, the value considered in the 𝑃𝑝𝑒𝑎𝑘 will be the mean value of the daily Peak observed over a period of one month.
Unit of Measurement The unit of measurement of this KPI will be %
Expectation Decrease peak energy production should be >30%
Overlap of this KPI with other relevant KPIs and Use Cases
UC1, UC2
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
System peak load with thermal model
𝑃𝑝𝑒𝑎𝑘,𝑎𝑓𝑡𝑒𝑟 Simulation Field Data Data file 1/year 2 year
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System peak load without thermal model
𝑃𝑝𝑒𝑎𝑘,𝑏𝑒𝑓𝑜𝑟𝑒 Simulation Field Data Data file 1/year 2 year
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☒
Details of Baseline The baselining will be done via simulation with the values from start of the project
GENERAL COMMENTS
Major Results From Use Cases
Results from Use Case 1
The DSR Platform was used extensively to perform peak load reduction tests during the
summer of 2016. A significant amount of the cooling load in the grid was connected to the
DSR Platform through IoT Field Gateway devices. By analyzing historic consumption data,
the tests were conducted to identify the amount of steerable load during different conditions
as well as the on-site impact on cooling systems and indoor climate. The tests were
conducted in the Western Harbour district cooling grid of Malmö with 11 major consumers
on the grid were controlled with DSR.
The peak load without DSR control 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 was defined as the demand at 30 °C outdoor
temperature at the hour of maximum demand for weekdays (weekends have much lower
consumption). This was estimated using historical data and time-binned linear regression to
approximately 8.8 MW 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 for the Western Harbour grid.
During a major DSR test where maximum load reduction was performed, it was estimated
that approximately 20 %, or 1.5 MW, of the load could be curtailed. The total demand
without DSR was estimated to 7.7 MW at the time.
Figure 2:1 Demand forecast from DSR test period around August 25th, 2016. The red line shows the forecasted production power in kW and the blue line shows the outdoor
temperature for the Western Harbour district in Malmö.
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Extrapolating this result to the 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 case yields a DSR-adjusted 𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ of about 7
MW. Thus, the KPI % ΔPpeakred can be estimated to 20 %. These results are summarized below
in table 2.2.
Table 2:2 Shows the peak with and without controling along with the peak reduktion in procentage.
Variable Result
𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ𝑜𝑢𝑡 8.8 MW
𝑃𝑝𝑒𝑎𝑘,𝑤𝑖𝑡ℎ 7.0 MW
KPI: % ΔPpeakred 20.5 %
Results from Use Case 2
Although UC2 is not connected to a district cooling grid KPI 2 was considered for the district
heating grid and its potential for peak load shaving.
Ectocloud is an optimization tool developed by e.on in collaboration with RWTH Aachen
University which continuously optimises the operation of an ectogrid. As the UC2 asset is a
small ectogrid pilot simulations in the Modelica software has been made. As a base load the
whole DH grid of Malmö City was scaled 1/1000 to simulate a small city district. The results
are shown in figures 2.3 and 2.4 but has not been fully analysed yet. More simulations need
to be done to get detailed results and costs needs to be added to the equation.
Figure 2.3. Shows the scaled load of the DH grid in Malmö without ectogrid balancing.
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Figure 2.4. Shows the DH grid of a small city district when balanced with ectogrid (including PV).
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3.3. WP8_KPI_3: DSR Dispatch Quality
BASIC KPI INFORMATION
KPI Name DSR Dispatch Quality KPI ID WP8_KPI_3
Strategic Objective Improving forecasting capabilities of residential DSR assets. Reduction in the Ferror by >10%
KPI Description Improvement in the reliability of forecasting capabilities by the advanced control and algorithms. The dispatch schedule will be forecasted based on the weather and load forecasts.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
The forecast error will be calculated based on the difference between the forecasted dispatch setpoints and the actual dispatch setpoints. The forecasted
setpoints will be for 2-4 hrs (exact number TBD) ahead of the actual time instants.
Ferror = Sactual − Sforecasted
%Ferror =𝐹𝑒𝑟𝑟𝑜𝑟,𝑠𝑡𝑎𝑟𝑡 − 𝑆𝑒𝑟𝑟𝑜𝑟,𝑒𝑛𝑑
𝐹𝑒𝑟𝑟𝑜𝑟,𝑒𝑛𝑑∗ 100
The minimisation of this forecasted error over the period of the project will be
aimed and recorded. Later the Ferror, at the end of the project will be compared with the Ferror, at the start of the project, for getting the percentage value of
improvement in DSR Dispatch Quality.
Unit of Measurement Unit for this KPI measurement will be % percentage base
Expectation >95% of the forecasted events. Overlap of this KPI with other relevant KPIs and Use Cases
UC1, UC2, UC3, UC5
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
Forecast Error
𝐹𝑒𝑟𝑟𝑜𝑟 DSR Algorithms DSR Server
1 year
Actual dispatch setpoints
𝑆𝑎𝑐𝑡𝑢𝑎𝑙 DSR Algorithms DSR Server
1 year Actual dispatch setpoints
Forecasted dispatch setpoints
𝑆𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑒𝑑 DSR Algorithms DSR Server
1 year
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KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☒
Details of Baseline The values measured at the start of the project will be the baseline
GENERAL COMMENTS
Major Results From Use Cases
Results from Use Case 1
The following observations were made (see also Figures 3:1 – 3:6):
The indoor temperature is difficult to see
The model we use to create forecasts is approx. 500 during actual energy use.
Probably the fault of seasonal variations.
The model forecast coincides well with the actual outcome during the schedule
periods.
Additional analysis is needed.
Figure 3:1 Energy: Actual energy consumption
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Figure 3:2 Steerable forecast: Forecast
Figure 3:3 Current steerable: Scaling up and down forecast according to schedules
Figure 3:4 - Indoor temperature
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Figure 3:5 - Global Amplitude: Schedule signal
Figure 3:6 - All energy plots: Showing the last of the three schedule periods with hourly
resolution. Data from energy, steerable forecast and current steerable.
Results from Use Case 2
The EMS of the building supplied by heat from the UC2 heat pump is not connected to
weather forecasting service and therefore KPI 3 has not been considered for UC2.
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Results from Use Case 3
The forecast models are based on several input and output factors. The underlying
mathematical expressions have been improved upon to achieve a better representation of
the power balance in Simris. From this were the result deducted for Use Case 3. The forecast
improved by 856,43%, %F_error = 856,43%. The big improvement is largely due the first
forecast model for the wind power production which were of by 30%. This can be seen in
table 3:7 and results in table 3:8 below.
Table 3:7 Shows the values that were measured along with the forecasted. This is shown
for WTG, PV and the power consumption in Simris.
Parameter WTG [KWh] PV [KWh] Consumption[KWh]
Actual 658939,50 289580,79 1056197,19
Start forecast 860007,55 286009,24 996219,61
End forecast 672968,01 267509,95 1021234,63
Table 3:8 Shows the parameters, values and explanations needed to answer the KPI
according to the accompanied equation.
Parameter Value Explanation
S_actual -107676,90 [KWh] Sum of actual production minus consumption
S_forcasted_start 149797,18 [KWh]
Sum of forecasted production minus consumption at project start
S_forcasted_end -80756,66 [KWh]
Sum of actual production minus consumption at project end
F_error_start -257474,08 [KWh] S_actual – S_forcasted_start
F_error_end -26920,24 [KWh] S_actual – S_forcasted_end
%F_error 856,43 %
See KPI formula above for %F_error
Results from Use Case 5
Results from Use Case 5 are based on the same data as the results in Use Case 3, therefore
the same results as well.
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3.4. WP8_KPI_4: Observability of microgrid performance
BASIC KPI INFORMATION
KPI Name Observability of microgrid performance KPI ID WP8_KPI_4
Strategic Objective Observe the Microgrid system parameters for reliable operations and non-violation of the technical constraints
KPI Description
The important technical parameters will be continuously monitored by the specific instruments installed in the substations for the reliable operation of the Microgrid when in islanded mode. The active power, reactive power, frequency, voltage, and harmonic THD constraints will be monitored continuously. During the total project period, the time will be divided into units of 60mins. This KPI will be measured as the ratio of, number of units of time (60mins) in which the violations of these important parameters occur to the total number of units of the operational time for the Microgrid.
1. Frequency Constraints
Short term frequency drops and rises. It will be considered as a frequency violation if the frequency falls into Box C. If the frequency gets into box B, EMS will automatically reconnect The frequency drops and spikes should ideally be within the boundaries of A1 The Enercon WTG will trip in 200ms if the frequency reaches 47 Hz or 51 Hz. 2. Voltage Constraints:
a. Short term voltage spikes (valid for voltage levels up to 1000 V)
It would be considered a violation of voltage parameter if voltage spikes falls into Box C. If the voltage gets into box B, EMS will automatically reconnect
The voltage spikes should ideally never exceed the boundaries of A1 b. For voltage levels up to 45 kV, short-term voltage drops:
It would be considered a violation of voltage parameter if voltage spikes falls into Box C. If the voltage gets into box B, EMS will automatically reconnect
The voltage drops should ideally be kept within the boundaries of A1 3. Harmonic Constraints:
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a. Voltage asymmetry During a period of a week the 10 minute value of the voltage asymmetry should be equal to or less than 2%. b. Voltage Harmonics: For voltage levels up to 36 kV: During a period of a week the 10-minute values for each individual harmonic should be equal to or less than the values in the table below. Furthermore, each 10 minute value of the total amount of harmonics should be equal to or less than 8 %.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
Pactual : Active Power measured at PCC Qactual: Reactive Power measured at PCC VMV, actual: Medium voltage in the Microgrid at generation side VLV, actual: Low voltage in the Microgrid close to customers factual: Frequency of the Microgrid HTHD: Total Harmonic Distortion 𝑈𝐵𝑉 : unit block variable (time period like e.g. 15 mins with either a violation or without violation)
αmicrogrid =𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙 − 𝑈𝐵𝑉𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡,𝑣𝑖𝑜𝑙𝑎𝑡𝑖𝑜𝑛
𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙∗ 100
(per measured parameter)
The different monitoring devices in the substations along with the ENCORP Microgrid controller will be measuring these values as against the violations of the standard constraints. If within a unit block variable (specific time period) a violation occurs, this violation will be counted. In the end, all unit block variables without violations will be compared to the total amount of unit block variables.
Unit of Measurement The unit of the measurement will be pu for Power measurements, Hz for frequency measurements, and % for total harmonic distortion. The unit of the KPI will be in percentage.
Expectation >75% of the detected events.
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Overlap of this KPI with other relevant KPIs and Use Cases UC3
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection
responsible
Active power
SRS bay 7
SRS.7.P Excel Metrum PQ120 SRS.7 0.1 Hz 12 months
Reactive power
SRS bay 7
SRS.7.Q Excel Metrum PQ120 SRS.7 0.1 Hz 12 months
Voltage N149403
bay 1
N149403.1.V Excel Janitza UMG605 N149403.1 0.1 Hz 12 months
Voltage SRS-131
SRS-131.V Excel Janitza UMG605 SRS-131 0.1 Hz 12 months
Voltage N106160
N106160.V Excel Janitza UMG604 N106160 0.1 Hz 12 months
Frequency N149403
bay 1
N149403.1.f Excel Janitza UMG605 N149403.1 0.1 Hz 12 months
KPI BASELINE
Source of Baseline Condition
Literature values
☒
Company Historical Values
☐
Values Measured at Start of Project
☐
Details of Baseline
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 21 of 45
Major Results From Use Cases
Results from Use Case 3
P_actual= 0.0 pu as Simris operates in island mode.
Q_actual = 0.0 pu as Simris operates in island mode.
KPI: 100%
V_MV,actual_mid =(395-1)/395*100 = 99,7 %
The measurements are performed at medium voltage level at point N149403-1.
V_MV,actual_low = (395-1)/395*100 = 99,7 %
The measurements are performed at low voltage level at point SRS-131 and N106160.
f_actual = 100 %
Frequency is measured at point N149403-1.
H_THD = 100 % The Total Harmonic are not exciding once.
Results from Use Case 5 Results from use case 3 also applies to use case 5 as they are based on the same
powersystem, therefore no addition to be made.
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 22 of 45
3.5. WP8_KPI_5: Increase of Renewable Penetration
BASIC KPI INFORMATION
KPI Name Increase of renewable penetration KPI ID WP8_KPI_5
Strategic Objective Increase the utilization of the renewable energy.
KPI Description
Identify how each of the Use Cases contribute to increased penetration of renewable energy. Evaluate how penetration of renewable energy were influenced as flexibility and controlling strategies were included. Comparisons are made within each Use Case, without the flexibility induced works as baseline.
Changes
This KPI was not included in Deliverable D8.1 & 8.2 and breafly mention in 2nd Amendment thus very little information. Existing KPI information were added by EONs Interflex representatives
KPI Formula
%E𝑐ℎ𝑎𝑛𝑔𝑒 = Ewithout
curtailed − Ewithcurtailed
Ewithoutcurtailed ⋅ 100%
Unit of Measurement
E_curtailed_without: The energy that would be curtailed without the implementation of BESS, DSR-assets along with EMS. P_curtailed_with: The energy that were curtailed despite the implemented BESS, DSR-assets along with EMS.
Overlap of this KPI with other relevant KPIs and Use Cases
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☐
Details of Baseline
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 23 of 45
Major results from the Use Case
Results from Use Case 1
See results from Use Case 2.
Results from Use Case 2
In Sweden, were UC2 is situated, variations in electricity demand is by 98 % regulated by
hydropower which contributes to approximately 45 % of the total electricity production. Only
approximately 1 % of the electricity production in Sweden is related to the burning of fossil
fuels. Due to the nature of the energy mix it was considered that the UC2-project does not
have the potential to increase the penetration of renewable energy in Sweden thus it was
not tested.
In most countries in the EU the energy mix looks somewhat different from Sweden. As a total
43 % (2018) of the electricity production in the EU origins from fossil fuel usage with a small
contribution of hydro power. Due to the limited quantity of flexible and renewable energy
sources UC2 could have an impact on the penetration of renewable energy sources like wind,
solar etc. in the EU.
Results from Use Case 3
The flexibility originates from two sources, the batteries and the DSR assets. Two quantities
were needed to evaluate the influence the project have had so far regarding renewable
penetration in Simris. First of, calculate the amount of energy that would have been
curtailment without the project’s implementation of flexibility within Simris. This were
compared to the actual curtailment using the formula of this KPI shows the curtailed energy
(%Echange) was decreased by 18 %. This is based on data from periods when the project
influenced the electrical situation in Simris. In other words, during test weeks (islanding
weeks) during the period April – November in 2018. Data could be collected more frequently
from that point on due to the implementation of Virtual Island Mode (grid connected with
full DSR and battery utilization) in Simris from mid-November 2018. Due to battery failure
from end of January in 2019, the possibility to run Simris in island mode and VIM stopped.
From week 20 in 2019 the battery has been repaired and is once again functional.
Table 5.1 Shows the curtailment with and without the induced flexibility to Simris
energy system along with the curtailment decrease in percentage.
E_curtailed_with 77247,959 KWh
E_curtailed_without 94357,130 KWh
%E_change 18,132 %
Results from Use Case 5
No additional information that relates to the Use Case 5 compared to the Use Case 3, thus
no further information to be added which gives the same results.
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 24 of 45
3.6. WP8_KPI_6: DSR technical availability
BASIC KPI INFORMATION
KPI Name DSR technical availability KPI ID WP8_KPI_6
Strategic Objective Maximize DSR System availability
KPI Description
An unavailable asset is not only providing zero flexibility but could create a risk where a system has a certain expectancy of flexibility. Identify key drivers for an asset being unavailable and increase overall asset availability Availability of the DSR System should be >99.5%
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
The DSR Platform will send a ‘Check Status’ signal, to every connected asset in every 5 mins. The asset will send an ‘Acknowledgement Signal’ back to DSR Platform. During the total project period, the time will be divided into units of 15 mins. The instants when the ‘Acknowledgement Signal’ is not received from a particular asset, it will be considered as that asset is unavailable for the DSR Support. The Availability of a particular asset will be measured as the ratio of the units where the Acknowledgement signal is not received for consecutive three times to the total number of units of operations time for a particular asset. These individual availabilities will be aggregated and averaged to get the final outcome of this KPI.
𝐴𝑎𝑠𝑠𝑒𝑡.𝑖 =𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙 − 𝑈𝐵𝑉𝑢𝑛𝑎𝑣𝑎𝑙𝑖𝑏𝑙𝑒
𝑈𝐵𝑉𝑡𝑜𝑡𝑎𝑙
𝐷𝑆𝑅𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =∑ 𝐴𝑎𝑠𝑠𝑒𝑡,𝑖
𝑖=𝑛𝑖=0
𝑛∗ 100
𝑈𝐵𝑉 : Unit block variable (15 mins of e.g. 3 response checks)
𝑛 : Total number of the assets connected to the DSR system
𝐴asset_i : Availability of a particular asset in the DSR system as
percentage of the unit blocks with availability to the total amount of unit blocks
𝐷𝑆𝑅𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 : DSR technical availability
Unit of Measurement The KPI unit will be in percentage basis. Overlap of this KPI with other relevant KPIs and Use Cases
UC1, UC3
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 25 of 45
KPI DATA COLLECTION
Data Data
ID
Methodology for data
collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection
responsible
Check Signal CHK Internet DSR Platform Server 5 mins 2 years
Acknowledgement Signal
ACK Internet DSR Platform Server 5 mins 2 years
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☒
Details of Baseline The values measured at the start of the project will be the baseline
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 26 of 45
Major Results From Use Cases
Results from Use Case 1
Calculations made from EM offline alarms on the 30 buildings connected to CESO during a period of 30 days.
Table 6:1 Shows the availability of the DSR assets and an average availability.
Devices = 30
Days = 30
UBVtotal = 2880
Aasset UVBavailable UVBunavailable Percent
1 2875 5 99,83%
2 2865 15 99,48%
3 2875 5 99,83%
4 2867 13 99,55%
5 2868 12 99,58%
6 2862 18 99,38%
7 2879 1 99,97%
8 2869 11 99,62%
9 2865 15 99,48%
10 2870 10 99,65%
11 2865 15 99,48%
12 2877 3 99,90%
13 2878 2 99,93%
14 2877 3 99,90%
15 2864 16 99,44%
16 2873 7 99,76%
17 2864 16 99,44%
18 2868 12 99,58%
19 2863 17 99,41%
20 2864 16 99,44%
21 2878 2 99,93%
22 2878 2 99,93%
23 2877 3 99,90%
24 2875 5 99,83%
25 2875 5 99,83%
26 2871 9 99,69%
27 2860 20 99,31%
28 2872 8 99,72%
29 2876 4 99,86%
30 2871 9 99,69%
DSRavailability 2870,7 9,3 99,70%
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 27 of 45
Results from Use Case 3
Installations which were not performed has been excluded from the dataset for this KPI.
Reasons varied from customer withdraw to technical impossibilities to install the assets. So
all assets that were attempted to connected to the DSR-platform/system has been included.
The availability is shown in table 6:2.
Table 6:2 Shows the accessibility of the different asset types are shown in the table below, in descending order by DSR_availability.
Asset type Number of units Availability [%]
Bobbie (water heater) 7 54,840
Nibe (Heat pump) 2 92,884
Ngenic (Heat pump) 11 95,659
Fronius (battery) 9 98,294
Availability varies between 92 percent and 99 percent for both type of heat pumps and the
battery systems alike. The asset type that stand out is the water heater with an availability
during the project of approximately 55%.
Results from Use Case 4
Results from Use Case 4 are based on the same data as the results in Use Case 3, therefore
the same results as well.
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 28 of 45
3.7. WP8_KPI_7: DSR Flexibility Response Time
BASIC KPI INFORMATION
KPI Name DSR flexibility response time KPI ID WP8_KPI_7
Strategic Objective DSR flexibility response time shall be shorter or equal compared to the targeted response time
KPI Description
As the power fluctuations in a LES sourced by renewables can be significant, it is important to work on improving asset response times. Identify key drivers for response time in the system and reduce the asset’s response times. Achieve a response time per asset appropriate for the cost of the balancing technology and the availability of the flexibility, depending on each type of asset. To be defined by the project based on external benchmarking.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
𝑡𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒,𝐴𝑠𝑠𝑒𝑡 = 𝑡𝑜𝑛 − 𝑡𝑎𝑐𝑡𝑖𝑣𝑒
𝜃 = 𝑡𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒,𝑡𝑎𝑟𝑔𝑒𝑡
(𝑡𝑜𝑛 − 𝑡𝑎𝑐𝑡𝑖𝑣𝑒)
𝑡𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑒 : The instant when the DSR send the activation signal
𝑡𝑜𝑛 : The instant when the device is switched ON 𝑡𝑟_𝐴𝑠𝑠𝑒𝑡 : The response time of the particular asset
The DSR platform will be responsible to send the activation signals and based on the control algorithms the specific devices will be activated.
The figure above describes the generic architecture for the connections from DSR Platform to the assets. The response time for each asset, will be calculated as the time difference between the instant of asset’s actual turning on and the instant of the activation signal being sent by the DSR platform. This KPI will track the improvements in reducing this response time for asset activation and thereby trying to improve the communication speed of the activation signal.
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 29 of 45
Unit of Measurement The unit of the time measurement will be sec or millisec. The unit of the KPI will be in percentage basis.
Overlap of this KPI with other relevant KPIs and Use Cases
UC1, UC3
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection
responsible
The instant of receiving of DSR
activation signal
𝑡𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑒 DSR API Server 2 years
The instant
for actual asset’s turning
on
𝑡𝑜𝑛 Smart-meter/
Customer API
Server 2 years
KPI BASELINE
Source of Baseline
Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☒
Details of Baseline The baseline will be defined by the values measured at the start of the project
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 30 of 45
Major Results From Use Cases
Results from Use Case 1
To get a grasp of the possible delays affecting the DSR flexibility response time, it is
important to highlight the data route from the DSR platform to actual actuation of physical
processes. In principal, such a route is shown in the figure 7:1. The may be transmission
delays in between each entity as well as internal data processing delay.
Figure 7:1 Shows the information flow logic responsible for how the response time is
defined.
In the image, each step is highlighted with a number for which response time is discussed in
the list below.
1. Internal processing delay within the DSR Platform is negligible, even if DSR signals
are to be sent to many customer sites located in different grids.
2. In normal operation, the transmission delay over e.g. LTE network is very small
(less than a few seconds).
3. The internal data processing of the IoT Field Gateway device depends on its
calculation and control sample time. This has been set to 1 minute for initial use
cases but can be reconfigured as needed.
4. Transmission over the on-site network depends on the used technology (Modbus is
used extensively) and set up of the network entities. Depending on which entity is
driving communication, different sample times may be applied. This typically
introduces delays ranging from 5 seconds up to 1 minute.
5. The data processing of the Customer Control System (BMS, PLC or similar) depends
on the performance and operating logic used by the customer. In most cases, it is
assumed this processing does not introduce more than a minute's delay. Depending
on the type of controller and its configuration parameters, additional delay is
introduced - some controllers are set to control slowly whereas others may act
faster.
6. Some response time should be assumed from actuators such as valves, pumps, fans
etc.
7. DSR signal response of the physical process' is determined by their construction
properties, which in turn influences thermodynamic and fluid dynamic responses
on-site and propagated to grid.
In summary, response time may be broken down in to three categories:
Communication and data processing response time
Control and actuation response time
Response time of physical process
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 31 of 45
Results from Use Case 3
There were no reliable measurements of the assets response times that could be obtained.
Consequently, not the way to answer the KPI. This KPI were instead answered with
information given by the asset suppliers and their knowledge of each asset respectively.
Each of the suppliers provided the frequency of which their specific asset retrieves new input
values. These time periods were combined with the frequency of which the DSR-platform in
Simris updates the steering signals. Furthermore, the supplier of water heaters has been
unable to provide any data regarding response time. No results that relates to the water
heaters could therefore be deducted nor presented. The existing results are shown in table
7.2 below were the main result were θ = 0,714 and θ = 0,998 for heat pumps and batteries
respectively.
Table 7:2 Shows data related to calculating the response time for each of the asset types.
Parameter Heat pumps Batteries Water
heaters
Number of units 13 9 7
t_activate [s] (starting value) 0 0 0
t_on [s] 210 150,267 N/A
t_response, Asset [s] 210 150,267 N/A
t_response, target [s] 150 150 150
θ 0,714 0,998 N/A
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 32 of 45
3.8. WP8_KPI_8: DSR Potential
BASIC KPI INFORMATION
KPI Name DSR Potential KPI ID WP8_KPI_8
Strategic Objective DSR Potential of different flexible technology and impact on the system
KPI Description
This KPI will intend to measure how much flexibility each household is able to deliver which has a direct impact on the system design. This KPI will be an evaluation of maximum theoretical exploitable potential of each house and will be compared against the actual DSR flexibility offered by that particular house. This comparison of ideal scenario versus the real scenario will assess the additional potential of the DSR System. Evaluating each asset technology to realize the highest potential assets. External benchmarking of each technology will be done.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
%𝑃𝐷𝑆𝑅,𝑎𝑠𝑠𝑒𝑡 =𝑃𝑎𝑣𝑎𝑖 − 𝑃𝑢𝑠𝑒𝑑
𝑃𝑎𝑣𝑎𝑖∗ 100
% 𝑃𝐷𝑆𝑅_𝑎𝑠𝑠𝑒𝑡 : Percentage DSR potential of the asset 𝑃_𝑎𝑣𝑎𝑖 : DSR Potential of an asset, available in total for flexibility contribution 𝑃_𝑢𝑠𝑒𝑑 : DSR Potential of an asset, actually contributed The DSR potential of an asset is the asset’s flexibility contribution for one year (kW/year). This is compared to the actual flexibility contributed by that asset, which quanties it’s potential for the DSR flexibility contribution capability. The DSR assets will be either the Buildings in UC1, commercial heat pump in UC2, and HTW boiler, heat pumps, PV + Batteries in UC3. This KPI will evaluate and rank the assets based on its DSR potential, which will be Useful for business Case calculations. During the start of the project, some assumptions were made about each of these assets, about their DSR Potential.
Unit of Measurement This KPI will be measured in percentage base
Expectation One of the outcomes of the project is a cost impact analysis of deploying a local energy market.
Overlap of this KPI with other relevant KPIs and Use Cases
UC1, UC2, UC3
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☒
Details of Baseline
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 33 of 45
Major Results From Use Cases
Results from Use Case 1
Modeling for using hot tap water boilers to reduce excess RES generation for a case study in
Simris.
The modeling used 10-minute and 1-minute values for simulations, note that the 1-minute
data were interpolated based on the 10-minute measurements. This probably flattens out
some spike that otherwise would exist.
In total, three variables were varied:
- Installed tap boilers [ranging from 9, which are currently installed to 30, 50, 100 and 140, which is the number of households in Simris]
- Limiting excess generation (75%, 80%, 85%, 90%, 95% of the original profile of RES generation minus Simris load)
- Lag between sending a DSR signal and implementing it (0, 1 time step in the 10-minute simulations and 5 minutes in the minute-by-minute simulation)
When neglecting any activation lag, major improvements occurred when using tap boilers
(note that there is hardly any difference between the 10-minute and 1-minute simulations
when neglecting activation lags).
When having only 9 boilers and limiting excess generation to 75% of the original profile, we
can already avoid 20% of the curtailment. This number quickly increases with more
households (50 households almost 80% curtailment avoided) and eventually all curtailment
can be avoided when providing all buildings with tap boilers and setting an excess limit of
75% of the original profile. [When going for lower excess limits, e.g. 50%, would more
flexibility likely be needed, this is one finding that have not been further studied]. This also
translates into the increased hosting capacity. The increase is almost linear (9 buildings
1,6% increase; 30 buildings 5,3% increase; 50 buildings 8,9% increase).
When considering a lag between sending the DSR signal and actually implementing there is
less benefits. Here, the avoided curtailment is slightly reduced but we cannot avoid all
curtailment due to the time delay. Up to 95% reduced curtailment when 100 of 140 buildings
are equipped with tap boilers.
The positive effects in hosting capacity is reduced when only look at power peaks with
activation lag. Due to the delayed reaction, the hosting capacity only increases by up to 8%
in comparison to up to 20% without activation lag.
Excess limit hereby describes when the DSR should start. In the original data, one peak was
730592 W of RES excess generation. With excess limit being 90%, we would start activating
the boilers if we notice a RES surplus of at least 90%*730592 W. Excess limit hereby describes
when the DSR should start. In our original data, we had a peak of 730592 W of RES excess
generation.
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 34 of 45
Table 8:1 - Inst. Devices is the number of households with a tap boiler installed (assumed to have 1300 W power). Lag describes the time delay between sending an activation signal and actually turning on the tap boiler. P_avaliable and P_used is used to calculate the KPI
using above-mentioned definition.
Minutely simulations
Inst. Devices Excess limit Lag Peak DSR
Peak_original P_available P_used KPI
9 75 0 718892 730592 11700 11700 100,00%
9 80 0 718892 730592 11700 11700 100,00%
9 85 0 718892 730592 11700 11700 100,00%
9 90 0 718892 730592 11700 11700 100,00%
9 95 0 718892 730592 11700 11700 100,00%
30 75 0 691592 730592 39000 39000 100,00%
30 80 0 691592 730592 39000 39000 100,00%
30 85 0 691592 730592 39000 39000 100,00%
30 90 0 691592 730592 39000 39000 100,00%
30 95 0 691592 730592 39000 39000 100,00%
50 75 0 665592 730592 65000 65000 100,00%
50 80 0 665592 730592 65000 65000 100,00%
50 85 0 665592 730592 65000 65000 100,00%
50 90 0 665592 730592 65000 65000 100,00%
50 95 0 684970 730592 65000 45622 70,20%
100 75 0 601884 730592 130000 128708 99,00%
100 80 0 600592 730592 130000 130000 100,00%
100 85 0 619973 730592 130000 110619 85,10%
100 90 0 656351 730592 130000 74241 57,10%
100 95 0 683851 730592 130000 46741 36,00%
140 75 0 601884 730592 182000 128708 70,70%
140 80 0 584320 730592 182000 146272 80,40%
140 85 0 619973 730592 182000 110619 60,80%
140 90 0 656351 730592 182000 74241 40,80%
140 95 0 683308 730592 182000 47284 26,00%
9 75 5 718892 730592 11700 11700 100,00%
9 80 5 718892 730592 11700 11700 100,00%
9 85 5 718892 730592 11700 11700 100,00%
9 90 5 718892 730592 11700 11700 100,00%
9 95 5 727051 730592 11700 3541 30,30%
30 75 5 691592 730592 39000 39000 100,00%
30 80 5 691592 730592 39000 39000 100,00%
30 85 5 691592 730592 39000 39000 100,00%
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 35 of 45
Minutely simulations
Inst. Devices Excess limit Lag Peak DSR
Peak_original P_available P_used KPI
30 90 5 707164 730592 39000 23428 60,10%
30 95 5 719172 730592 39000 11420 29,30%
50 75 5 671019 730592 65000 59573 91,70%
50 80 5 672452 730592 65000 58140 89,40%
50 85 5 682241 730592 65000 48351 74,40%
50 90 5 700249 730592 65000 30343 46,70%
50 95 5 711669 730592 65000 18923 29,10%
100 75 5 671019 730592 130000 59573 45,80%
100 80 5 671019 730592 130000 59573 45,80%
100 85 5 682241 730592 130000 48351 37,20%
100 90 5 695269 730592 130000 35323 27,20%
100 95 5 701919 730592 130000 28673 22,10%
140 75 5 671019 730592 182000 59573 32,70%
140 80 5 671019 730592 182000 59573 32,70%
140 85 5 682241 730592 182000 48351 26,60%
140 90 5 695269 730592 182000 35323 19,40%
140 95 5 695135 730592 182000 35457 19,50%
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 36 of 45
Results from Use Case 2
To find out the DSR potential for UC2 simulations were made using a script turning the heat
pump on and off according to a predetermined schedule. The results were used to find out
the potential of load reduction for the electricity grid ( and DH grid) during times of high
peak loads, RES availability etc.
Figure 8.1. UC2 electricy and heat load and during test run.
As shown in figure 8.1 the heat pump was turned on and off several times during a period of
10 hours (several similar test were made). For the particular asset the power demand during
full heat pump load is approximately 18-20 kW producing 60 kW of heat for the connected
building. As a back-up the energy central is connected to the local DH grid and when turning
the heat pump off the DH load is automatically increased to meet the heat demand. As
shown in figure 8.2, when turned off, the power quickly goes down to about 0,5 kW (stand-
by mode) and the DH load increases accordingly.
Using this flexible system in combination with DH as a peak and back-up heat source it is
possible to shift energy carrier from electricity to DH making it possible to relieve the DH
grid (or electricity grid) during peak hours, increase the RES share when available etc. When
implemented in larger numbers the potential to relive the grid, increase the RES share should
be considered as significant.
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
Tim
e0
8:5
9:4
51
0:0
0:0
01
1:0
0:1
51
2:0
0:3
01
3:0
0:4
51
4:0
1:0
01
5:0
1:1
51
6:0
1:3
01
7:0
1:4
51
8:0
2:0
01
9:0
2:1
52
0:0
2:3
02
1:0
2:4
52
2:0
3:0
02
3:0
3:1
50
0:0
3:3
00
1:0
3:4
50
2:0
4:0
00
3:0
4:1
50
4:0
4:3
00
5:0
4:4
50
6:0
5:0
00
7:0
5:1
50
8:0
5:3
00
9:0
5:4
51
0:0
6:0
01
1:0
6:1
51
2:0
6:3
01
3:0
6:4
51
4:0
7:0
01
5:0
7:1
51
6:0
7:3
01
7:0
7:4
51
8:0
8:0
0
kW
Nobelvägen, Malmö
Active power Heat production
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 37 of 45
Results from Use Case 3
Out of the total amount of energy that the DSR assets could provide were not all use. The
utilization for each asset is presented in table 8.2. The heat pumps and residential batteries
provides flexibility both in the positive and negative direction. In other words, they can be
turned off to decrease consumption and turned on to increase consumption. in contrast to
the other asset types can the water heaters only provide flexibility in one direction; used to
increase consumption. The reason for this is to not risk the health of Simris residences by
turning off the water heater. Decreasing water temperature within the water boiler system
could otherwise result in an increased bacterial growth.
Table 8:2 Shows the utilization of the the potential DSR for each of these assts in the folowing order, battary system, heat pump and water boiler.
Parameter Utilization [%]
%P_DSR_asset_BESS 25,569
%P_DSR_asset_HP 32,966
%P_DSR_asset_Boiler 37,250
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 38 of 45
3.9. WP8_KPI_9: Customer energy awareness
BASIC KPI INFORMATION
KPI Name Customer Energy Awareness KPI ID WP8_KPI_9
Strategic Objective To increase the Customer Energy Awareness due to the project
KPI Description
% of increase in the active participation in energy related activities (measured at the start and close to the end of the trial). A measurement index will be created to establish the level of awareness of customers at the beginning of the project. This will help to track its evolution and its influence on their decision making.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
The values of 𝑄𝑖 will be from {1, 4, 7, 10}, which will decide the weight of the question. The values for 𝐴𝑖 will be from {0, 2, 4} which will indicate customers’ unawareness, partial awareness or complete awareness about the respective question.
Customer Awareness Index at the start
𝐶𝐴𝐼𝑠 =∑ ∑ (𝑄𝑖 ∗ 𝐴𝑖)𝑛
𝑖=1𝑚𝑗=1
𝑚𝑡𝑜𝑡𝑎𝑙,𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑠
Customer Awareness Index at the end
𝐶𝐴𝐼𝑒 =∑ ∑ (𝑄𝑖 ∗ 𝐴𝑖)𝑛
𝑖=1𝑚𝑗=1
𝑚𝑡𝑜𝑡𝑎𝑙,𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑠
Rise in Customer Awareness Index
%𝐶𝐴𝐼 =𝐶𝐴𝐼𝑒 − 𝐶𝐴𝐼𝑠
𝐶𝐴𝐼𝑠∗ 100
𝑛 : Total number of questions
𝑚 : Total number of the customers The increase in the customer awareness index will be the percentage increase of the weighted average measured by the customer survey which will be carried out at the start of the project and then at the end of the project.
Unit of Measurement The unit will be measured in % percentage base
Expectation >50%
Overlap of this KPI with other relevant KPIs and Use Cases
UC4
KPI DATA COLLECTION
Data Data ID
Methodology for data
collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data collection
Minimum monitoring
period
Data collection responsible
The customer survey will be done and the
answers will be recorded
Ai Customer Survey
Customer Interviews Excel 2 times during the project
duration
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☒
Details of Baseline The baseline depends on the values acquired at the start of the project
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 39 of 45
Major Results From Use Cases
Results from Use Case1
No energy awareness index was set in the beginning of the project. Interviews have not been
performed according to the KPI - WP8_KPI_9.
Customer Energy Awareness of Simris vilagers in early 2018 (CAI_s) and 2019 (CAI_e)
respectivly
Results from Use Case 4
Four questions were used to monitor the customer’s energy awareness over an approximately
1-year period. The questions were rated on how well they could represent the customers
energy awareness were good capability gave a greater influence on the result. From the
replies of the customers could a slight increase of energy awareness can be observed in the
results. Worth mentioning is the decrease in replies that the survey received, out of 140
households 51 and 45 replies in 2018 and 2019 respectively. For the residents in Simris had
energy awareness increased (%CAI) by 1,96 %.
Table 9.1 Shows Customer Energy Awareness of Simris villagers in early 2018 (CAI_s), 2019 (CAI_e) respectively and the change in procentage.
Parameter Value
CAI_s 84,039
CAI_e 85,689
%CAI 1,963
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 40 of 45
3.10. WP8_KPI_10: Customer Satisfaction Index
BASIC KPI INFORMATION
KPI Name Customer Satisfaction Index KPI ID WP8_KPI_10
Strategic Objective NPS improvement: 10 points better after one year
KPI Description ECSI (European Customer Satisfaction Index) value measured at each year of the trial, or NPS-system (Net Promoter Score)
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on Deliverable D8.1-8.2 and 2nd Amendment), these shall be stated here.
KPI Formula
𝑁𝑃𝑆𝑝𝑜𝑖𝑛𝑡𝑠 = %𝑃𝑝𝑟𝑜𝑚𝑜𝑡𝑒𝑟𝑠 − %𝐷𝑑𝑒𝑡𝑟𝑎𝑐𝑡𝑜𝑟𝑠
Net Promoter Score, measures customer experience and is difference between the percentage of People who are Promoters and percentage of people who are Detractors.
Unit of Measurement This KPI will be measured in terms of the points on the NPS system Overlap of this KPI with other relevant KPIs and Use Cases UC1, UC4
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
Promoters (people who rate
their satisfaction from 8-10)
𝑃𝑝𝑟𝑜𝑚𝑜𝑡𝑒𝑟𝑠 Customer surveys & interviews
Customer interviews Excel 1/year 12 Months
Passives (people who rate
their satisfaction from 5-7)
𝑃𝑝𝑎𝑠𝑠𝑖𝑣𝑒𝑠 Customer surveys & interviews
Customer interviews Excel 1/year 12 Months
Detractors (people who rate
their satisfaction from 1-4)
𝐷𝑑𝑒𝑡𝑟𝑎𝑐𝑡𝑜𝑟𝑠 Customer surveys & interviews
Customer interviews Excel 1/year 12 Months
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical
Values
☐
Values Measured at Start of Project
☒
Details of Baseline The values will be measured in the start of the project and that will form the baseline for this KPI
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 41 of 45
Major Results From Use Cases
Results from Use Case 1
The results from the first trial period have been analyzed on a grid and building level. The
first analysis focused on the indoor temperature. The purpose of the analysis was to ensure
that the CESO steering did not negatively affect the buildings citizens. Some fluctuations
were detective, but the temperature difference was lower than 0,5℃.
The data from the first trial period was controlled by the Ectocloud team. It was detected
that the CESO system gave a temperature flow higher than in normal operation. This could
be explained by the fact that the CESO system plans to be used during periods with high
heating consumption.
No effect on the indoor climate has been detected during the first trial so no changes in NPS
can be identified. The contact person for the property company can confirm that no increase
or decrease of complains has been identified after installing CESO.
Results from Use Case 4
The customer satisfaction indexes for KPI 10 were deducted from two customer surveys, one
preformed in the beginning of the 2018 and one in the beginning of 2019. The number of
completed interviews 2018 and 2019 were 45 and 51 respectively. From these datasets can
it be seen that NPS 2018 = 35.294 compared to NPS 2019 = 44.444. In the period of one
year had the NPS increased by 9.15 points. Unfortunately, just short of the stated goal of
increasing the satisfaction with 10 points.
Table 10:1 Shows the summary from two surveys and how satisfied the villagers in Simris are with the project.
Score 2018 Score 2019
Detractors 3,921 % 8,889 %
Neutral 56,863 % 37,778 %
Promoters 39,216 % 53,333 %
NPS 35,294 44,444
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 42 of 45
3.11. WP8_KPI_11: Customer Recruitment
BASIC KPI INFORMATION
KPI Name Customer recruitment rate KPI ID WP8_KPI_11 (WP2.2_KPI_4)
Strategic Objective Measure whether demos are managing to recruit enough customer base in order to attain demo objectives
KPI Description
Customer engagement is a heuristic for the new energy system. This KPI measures if customers are prone to be more active in the new system and this will have an impact on how new solutions will be designed in a commercialization phase. A prerequisite for this is that they are willing to take part, in the first place.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on D8.1-8.2), these shall be stated here.
KPI Formula
𝐶𝑅% =𝐶𝑅𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙
𝐶𝑅𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑∗ 100
CR% : percentage of required customer base that Use Case was able to recruit CRsuccessful : number of customers (installed capacity, energy volume) needed to obtain enough flexibility in demo in order to verify the use cases CRrequired : number of customers (installed capacity, energy volume) recruited
Unit of Measurement Unit of the CR depends on Use Case description, but should be either customer numbers (#), installed capacity (MW) or Energy (MWh). The unit of the KPI is in percentage basis.
Expectation Steering calls executed per month
Overlap of this KPI with other relevant KPIs and Use Cases
UC1, UC3, UC4. This KPI also forms a part of Common KPIs defined for all the Demos in INTERFLEX
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
Numbers of customer/installed capacity/energy
volume needed to obtain enough
flexibility in demo in order to verify
Use Cases
CRrequired Analysis during Use Case design
phase
N/A N/A N/A N/A E.ON
Numbers of customer/installed capacity/energy volume recruited
CRsuccessful Records from
recruitment activities (customer
agreements if
applicable)
N/A N/A N/A N/A E.ON
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 43 of 45
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical
Values
☐
Values Measured at Start of Project
☐
Details of Baseline
GENERAL COMMENTS
Major Results From Use Cases
Results from Use Case 1
UC1’s Trial was done at 30 buildings own by MKB. The steering of the heating also called
power control is done on building level. E.ON needs to connect 1000 of your largest energy
customers to be able to reduce 20% of customers' heating needs without the indoor
temperature falling below 17 degrees.
Of these 1000 buildings, approx. 100 EIA properties and currently 30 are connected to the
CESO system.
CR required: 1000 buildings
CR successful: 30 buildings
30 / 1000 * 100 = 3%
The goal is to have the 1000 buildings connected by 2020.
Results from Use Case 3
In the 140 households that make up Simris were assets installed which later been used for
DSR, the number of assets is 29. Compared to the required number of assets, which were
20, were the recruitment successful.
CR_successful = 29
CR_required = 20
Therefor are the customer recruitment 45 % above required or CR% = 145 %. Note, the
project did have an internal goal of 40 assets that have not been reached.
Results from Use Case 4
Results from Use Case 4 are based on the same data as the results in Use Case 3, therefore
the same results as well.
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 44 of 45
3.12. WP8_KPI_12: P2P platform participation
BASIC KPI INFORMATION
KPI Name P2P platform participation KPI ID WP8_KPI_12
Strategic Objective To interact with customers through P2P platform and promote DSR activities
KPI Description
This KPI aims to increase the P2P platform participation and involve the customer in the DSR activities. The customers’ log-in into their personal profiles provided by the P2P API will be recorded and will contribute towards measuring the increase in the platform engagement of the customers.
Changes
If any changes have been made to any part of the BASIC KPI INFORMATION for this KPI (based on D8.1-8.2), these shall be stated here.
KPI Formula
𝑁𝑓𝑟𝑒𝑞,𝑣𝑖𝑠𝑖𝑡𝑠 =∑ 𝐴𝑤𝑒𝑒𝑘,𝑖
𝑛𝑖=0
𝑛
%𝑁 =(𝑁𝑓𝑟𝑒𝑞,𝑣𝑖𝑠𝑖𝑡𝑠 − 𝑁𝑠𝑡𝑎𝑟𝑡,𝑣𝑖𝑠𝑖𝑡𝑠)
𝑁𝑠𝑡𝑎𝑟𝑡,𝑣𝑖𝑠𝑖𝑡𝑠∗ 100
𝑛 : Total number of customers 𝐴𝑤𝑒𝑒𝑘 : Total number of individual customers that logged in at least once during a week The KPI intends to measure the increase in customer engagement through P2P platform by calculating percentage increase in the number of individual customers that logged in at least once during a week measured at the start of the project and at the end of the project. The number of logins (= 𝑁𝑓𝑟𝑒𝑣𝑖𝑠𝑖𝑡𝑠) is calculated as average of total number of actions per visit by all the customers during the whole day. It is not possible to estimate in beforehand.
Unit of Measurement Unit will be percentage Overlap of this KPI with other relevant KPIs and Use Cases UC4
KPI DATA COLLECTION
Data Data ID Methodology
for data collection
Source/Tools/Instruments for Data collection
Location of Data
collection
Frequency of data
collection
Minimum monitoring
period
Data collection responsible
Number of actions per visit by the customer
𝐴𝑑𝑎𝑦 Internet P2P API / Customer API
Server 1 Hz 2 years
KPI BASELINE
Source of Baseline Condition
Literature values
☐
Company Historical Values
☐
Values Measured at Start of Project
☒
Details of Baseline
GENERAL COMMENTS
D8.7 Raw demonstration results based on the KPI measurements
InterFlex – GA n°731289 Page 45 of 45
Major Results From Use Cases
Results from Use Case 4
The frequent which the customers have visited the P2P visualization platform have increased
over the measured period. Using the earlier stated equation for this KPI along with the
gathered data shows that the number of visits has increased by 5%.
Two things worth to keep in mind when seeing at the result.
First of, out of the total number of active participants (20) have several users not used the
service at all. Therefore, activity of the customers that use the service are far greater that
what is shown in these results.
Secondly, the data set these calculations are based on contains rather few datapoints and
stretch over a period of roughly two months, beginning of May 2019 – late June 2019. A fuller
dataset would represent the reality in a better way, but this is the data that have been
obtained.