Safe Integration of Renewables at Transmission and Distribution … · 2019-01-18 · IVR GIS...
Transcript of Safe Integration of Renewables at Transmission and Distribution … · 2019-01-18 · IVR GIS...
Safe Integration of Renewables
at Transmission and
Distribution Level
Alexander Krauss - Digital Grid Software and Solutions
siemens.com/digitalgrid Unrestricted © Siemens AG 2017
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May 2017 Alexander Krauss / EM DG SWS S MI
Overview
Table of content
• The Masterplan - One Platform
• Active Nework Management
• Wind Power Managment
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Siemens Digital Grid masterplan architecture for a smooth transition to …
CIM – Common Information Model (IEC 61970)
Enterprise IT
IVR GIS Network planning
Asset management
WMS/mobile Weather Forecasting Web portals CIS/CRM Billing
Enterprise Service Bus
Cloud enabled applications Public cloud
Smart communication
Grid
cyb
er s
ecu
rity
Man
ag
ed
/clo
ud
serv
ices
OT
-IT in
teg
ratio
n, c
on
su
lting
Smart
transmission
Smart
distribution
Smart
consumption
and microgrids
Smart
distributed energy
systems
Smart
markets
$ € ₹
Business applications Grid control applications Grid planning and simulation
CIM CIM
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Cloud enabled applications e.g. analytics
Agility in energy:
open – adaptable – manageable, standards-based ecosystem
Enterprise IT
IVR GIS Network planning
Asset management
WMS/mobile Weather Forecasting Web portals CIS/CRM Billing
Enterprise Service Bus
Public cloud
Smart communication
Grid
cyb
er s
ecu
rity
Man
ag
ed
/clo
ud
serv
ices
OM
NE
TR
IC G
rou
p, S
mart G
rid C
om
pass
Field area networks Substation: SICAM and SIPROTEC within ENEAS solutions
Meter Data
concen- trator
Load controls
Building/ home energy management
system
Grid sensors
Microgrid controllers
Third party …
Substation controllers
Human machine interfaces
Protection devices
Remote terminal units
Bay controllers
Power quality devices
Third party
CIM – Common Information Model (IEC 61970) | PSS – Power System Simulator
Business applications:
EnergyIP
Grid control applications:
Spectrum Power
Grid planning and simulation:
PSS Suite CIM CIM
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Spectrum Power™
Comprehensive portfolio of applications
Generic
applications
Basic
functions
Energy market
& generation
Transmission
Distribution
Multi-utility/
industry
Infrastructure
UI (User Centered Design)
Data Entry & Data Model (IMM)
Archive (HIS)
Communication
SCADA & Base System
Security
IT-Interfaces and SOA
Forecast Applications
Load Shedding
Power Applications
Resource optimization & scheduling
Energy Market Management
Transmission Network Applications
Training Simulator -Transmission
Network Stability Analysis
Distribution Network Applications
Training Simulator -Distribution
Outage Management System
GIS-Interface
Load Management Electricity, Gas
Supply-Management Water
Pipeline-Networks
Disposal Locking
Rota Switching
Renewable
generation Transmission
Energy
production Multi-utility Industry
Distribution Infrastructure
Basic Functions
Specific Applications
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Spectrum Power™ 7
Architecture
Enterprise Service Bus
Spectrum Power High Speed Bus
HIS IMM GIS
I/F OMS CMS TCS DNA TS
Work Force Mgmt
Customer Inform. System
SAP Asset Mgmt
Meter Data Mgmt
Geo-graphical Inform. System
Weather Forecast
Network Planning
SA RO FA PA EMM
Base SCADA CFE
IFS OPC
ICCP
ELC
TNA
Base OPF DSA TS LME LMG LMW MERO
Real-time and day ahead energy market management
Communication with substation
RTU/SAS and other control
centers
Transmission load flow calculation, grid
optimization and what-if studies
Management of infeeds, switchable loads
and storages for minimum cost (power, gas,
water)
Distribution load flow calculation, grid optimization and what-if studies
Systematic outage management for faster and more restoration
Base functionality: data model, UI,
SCADA, Archive
Energy resources and production planning; load forecasting for generation and grid operation planning
Generation control for more economic and reliable operation
... ...
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Enterprise IT
Integrated platform strategy to
ensure minimized risk and high cost efficiency
Digitalization
& Automation
Enterprise Service Bus
Smart Communication
Smart transmission
Smart distribution
Smart consumption and microgrids
Smart distributed energy systems
Smart markets
$ € ₹
CIM – Common Information Model (IEC 61970)
IVR
GIS
Network planning
Asset management
WMS/mobile
Weather
Forecasting
Web portals
CIS/CRM
Billing
EnergyIP
Smart grid & smart market applications
• Meter Data Management
• Decentralized Energy Resource Management
• Revenue protection/Non-Technical Losses
• Prepaid Energy System
• Market Transaction Management
• Energy Engage customer web portal
• Energy Analytics
Spectrum Power
Grid control application
• Transmission & Distribution Network Analysis
• System stability and system balancing applications
• Outage and trouble call management
• Active network management
• Fleet optimization & scheduling
• Forecasting and planning applications
• Energy Market Management
CIM
Center
E-car
Operation
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Overview
Table of content
• The Masterplan - One Platform
• Active Nework Management
• Wind Power Managment
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New challenges for grid management due to growing need
for integration of renewable generation
No clear direction of power flow
Violation of voltage limits
Overload situations
Increasing installation of renewable
energies
Observability improvement
Volt-/VAR management
Capacity management
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Spectrum Power Active Network Management
Releasing hidden capacity by Active Network Management
Real time thermal rating Energy storage Voltage control device Controllable generation Controllable loads
Network state
1
Problem detection
2
Decision making
3
Set-point command
4
Active Network Management System
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Spectrum Power TM ANM
Active Network Management, Use case
Use case description
Decentralized power generation and distributed infeed causes
• voltage band violations on high, medium and low voltage level
• overload of grid components (transformers, lines, etc.)
• power quality problems due to inverters
Active network management monitors the network state to detect
network volatilities, suggests counter measures and implements
the counter measures in closed loop
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Spectrum Power Active Network Management
Active Network Management based on real time state estimation
Estimate
Voltage
Violations
Overloads
Significant
measurement
change
Configured
Cycle
Topology
change
Control
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Control
RDC Primary
RDC Secondary
Optimization
Spectrum Power Active Network Management
System components of an hierarchical solution
Data
Con
ce
ntr
ato
r Battery Capacity
Management
OLTC AVC
Management
Capacitor Control Lo
ca
l C
oo
rdin
ation
& P
rio
ritiza
tio
n Thermal Modelling
Local Thermal
Management
Local Voltage
Management
Fro
nt-
En
d
Capacity
Management
Voltage
Management
SC
AD
A
Topological
Coloring
State Estimation
Volt / Var
Optimization
Central Control
(Siemens Spectrum Power Active Network Management)
Control Center
& Assets
Local Control
(Siemens Autonomous Substation Controller)
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Spectrum Power TM ANM
References
Northern Power - Grid Grand Unified Scheme
The Grand Unified Scheme (GUS) brings together battery storage, enhanced voltage
control, demand response and real-time thermal rating in closed loop for optimal
grid operation.
Energy Northwest - Smart Street project
Voltage management at the HV level to reduce network losses and conservation
voltage reduction (CVR) at LV level to reduce energy demand, and run LV meshed
networks to release network capacity.
IREN2
Microgid with renewable generation as substitute of conventional generating units.
Migrogid providing flexibilities for the distribution grid operation
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Electricity North West, Smart Street
Loss Reduction in 11/6.6kV by increasing the
voltage level to an optimal level.
Energy Efficiency by reducing the voltage close
to the lower limit
33 kV 11 / 6.6 kV LV
+10%
-6%
+6%
-6%
Limits on DG at HV
Limits on LCTs such as
heat pumps
Low voltage leads to
unnecessary losses in
DNO network
High Voltage leads to poor
Energy Efficiency for
Customers
+10%
-6%
+6%
-6%
Increases Energy Efficiency
for CustomersIncreased scope for DG
Increased scope for
LCTs such as heat
pumps
Optimised voltage for
overall loss minimization
33 kV 11 / 6.6 kV LV
Current network situation
Optimized network situation
The coordination of the capacitor banks will optimize the voltage levels on HV network for overall loss minimization. The loss reduction will be achieved by increasing the voltage level to an optimal level.
Energy efficiency will be achieved by reducing the voltage close to the lower limit. A meshed network will be utilized using LV circuit breakers along with capacitor banks to flatten the voltage profile thus permitting ENW to drop the voltage across both feeders (using OLTC transformers) to facilitate reduction in losses and energy consumption.
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Active Network Management – Experiences from ENW Smart Street
Goal to Target
Three key steps ...
• Coordinated voltage control, using
transformers with on load tap changers and
capacitors, across HV and LV networks
• Interconnecting traditionally radial HV and
LV circuits
• Real time coordinated configuration and
separate voltage optimization targets of
HV and LV networks
… and expected targets
• Release capacity up to four times faster and
40% cheaper than traditional reinforcement
techniques for low carbon technology clusters.
• Deliver conservation voltage reduction to improve
the energy efficiency of customers’ electrical
appliances reducing energy up to 3.5% - 4% per
annum, and lowering network losses by up to
2.5 – 3.5% per annum across HV and LV
networks.
This will deliver recurring financial savings for
customers, without degradation to the quality of
customers’ supplies.
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Active Network Management - ANM
Solution Overview
No clear direction of
power flow
Violation of voltage
limits
Overload situations
Increasing installation of
renewable energies
Observability
improvement
Volt-/VAR management
Capacity management
Active Network Management
System
Real time
thermal rating Energy storage
Voltage control
device
Controllable
generation
Controllable
loads
Network state
1
Problem detection
2
Decision making
3
Set-point
command
4 Improves situational
awareness
Reduces voltage band
violations and equipment
overloads
Minimizes distribution
losses
Supports various network
components
Optimizes in full closed-
loop operation
Scalable and flexible
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Spectrum Power ANM
Key benefits
Efficient peak load management
provides comprehensive information
for taking decisions to avoid
problems such as voltage violations
and overloads
Reliable voltage regulation
Generic on-line optimization that can
be easily extended to cover additional
network areas and controllable
resources
High grid stability
supports operator in detecting
network volatility early enough to
react in time
1
2
3
4 Reduced costs
Helps to avoid expensive network
extensions while operating the
existing network in an optimized way
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Overview
Table of content
• The Masterplan - One Platform
• Active Nework Management
• Wind Power Managment
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Wind Power Management
Introduction
Wind power generation mainly influenced by
Weather effects, Wind Farm Structure, Region
Experience from countries in the sector shows that
advanced tools are needed to forecast wind power
generation
Principal actors: power producers, transmission
system operators and independent power generators
Uncertainty of wind hard to predict
Accurate and reliable forecasting systems of wind
power production increases wind penetration
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4 types of input data:
WPF and WPE files are given by external forecasters and are optional
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Input Data
Real time measurements
(SCADA) are received every
15/20/30 or 60 min and give
the power production of
each telemetered wind farm
RTM
Wind Power Estimations are
received each hour:
estimation of last 24 hours of
total wind power produced
(telemetered and non
telemetered)
WPE Wind
Forecasts
Wind forecasts (speed and
direction) in a distributed set
of geographical coordinates
and close to the location of
the wind farms
Total Wind Power Forecasts
are received each hour:
forecast of total wind power
produced by the system
WPF
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Calculates predictions based on real time measurements and meteorological forecasts (wind speed and
wind direction) up to 48 hours
Ensemble forecast: 8 forecasts generated and combined internally
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Prediction Algorithm
8 prediction
models
Nonparametric
models
More historical data
are needed to be
more accurate
Parametric
models
Less historical data is
needed to obtain
initial estimates
M1: Persistence Model
M2: Autoregressive Model
M3: Autoregressive and Linear Speed Model
M4: Autoregressive and Quadratic Speed Model
M5: Autoregressive, Linear Speed, and Wind Direction Model
M6: Autoregressive, quadratic speed and wind direction model
M7: Autoregressive and Non-parametric Prediction Based on
Speed and Direction
M8: Non-parametric Prediction Based on Speed and Direction
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Prediction Algorithm
The final forecast is obtained by combining the predictions generated by
8 different models
RTM Wind Forecasts
Model 1 (accuracy 1)
Model 2 (accuracy 2)
Model 8 (accuracy 8)
Combinations
Final Prediction
…
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Benfits and Key features
Key
Features
1
4
6
5
3
Statistical and
combined
forecasting method
Total / regional wind power
forecast 1h up to 10 days
Disaggregation of
the result up to 48
hours
After the Fact Error
Analysis
15/20/30/60 minutes
time grid
2
Voltage
Pre- and Post
contingencies 7
Locational Marginal
Price for Intraday
and Day ahead
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STWPF sample display: wind farms capacities per location
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How does it look like?
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How does it look like?
STWPF sample display: wind direction
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Questions ?
Alexander Krauss
Sr Business Developer
EM DG SWS S MI
Humboldtstr. 59
90459 Nürnberg
Germany
Phone: +49 151 54444345
E-mail:
siemens.com/digitalgrid