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3 © 2008 Electric Power Research Institute, Inc. All rights reserved. End-uses & DR Distribution System Transmission System Energy Storage Fuel Supply System Fuel Source/Storage Power Plants Renewable Plants Data Communication Wide Area Control Sensors Controllers ZIP M Dynamic Load Models Dynamic Power Plant Models End-to-End Power Delivery Chain Operation & Planning Monitoring, Modeling, Analysis, Coordination & Control
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Smart Grid Vision: Vision for a Holistic Power Supply and Delivery Chain
Stephen LeeSenior Technical Executive Power Delivery & Utilization
November 2008
2© 2008 Electric Power Research Institute, Inc. All rights reserved.
Smart GridTwo Way Communications….Sensors…….Intelligence
Smart Smart GridGrid
Need an Objective Assessment of the Potential for Smart Transmission and the Path to Achieve it
Hype
3© 2008 Electric Power Research Institute, Inc. All rights reserved.
End-uses & DR
Distribution SystemTransmission System
Energy Storage
Fuel Supply System
Fuel Source/Storage
Power Plants
Renewable Plants
Data Communication
Wide Area Control
Sensors
Controllers
ZIP
M
Dynamic Load ModelsDynamic Power Plant Models
End-to-End Power Delivery Chain Operation & Planning
Monitoring, Modeling, Analysis, Coordination & Control
4© 2008 Electric Power Research Institute, Inc. All rights reserved.
Alarm Management
Most Needed Capabilities Requiring Research
• Hierarchical Integration of Entire Supply & Delivery Chain
• Optimal End-to-End Dispatch under Uncertainties
• Dynamic Models of Generators and Loads
• Online Alarm Root-Cause Diagnostics
• Prevention of Cascading Outages, Safety Nets
• Fast System Restoration After Blackouts
ProtectionSCADA
Data ManagementNetwork Management
Security
ChainIntegration
OutageManagement
Dynamic Models
End-to-End Dispatch
Restoration Protection
5© 2008 Electric Power Research Institute, Inc. All rights reserved.
Source: California ISO
Tehachapi, California Wind Generation in April – 2005
0
100
200
300
400
500
600
700
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Hour
MW
Average
Each Day is a different color.
Day 29
Day 5 Day 26
Day 9
Could you predict the energy production for this wind park either day-ahead or 5 hours in advance?
6© 2008 Electric Power Research Institute, Inc. All rights reserved.
Methods of Coping with Wind Uncertainty
• Short-Term– Better wind forecasting– Carry more operating and spinning
reserve to handle up and down ramps of wind output
– Rapid coordination with demand response and energy storage
• Long-Term– Build more energy storage, e.g.,
Compressed Air Energy Storage (CAES)
– Controllable demand response– Holistic planning of transmission,
generation and demand• Virtual Service Aggregator
0
50
100
150
200
250
300
350
Sep06 12:00
Sep06 14:24
Sep06 16:48
Sep06 19:12
Sep06 21:36
Sep07 00:00
Sep07 02:24
1-min MW
fcst 1
fcst 2
fcst 3
Potential wind curtailment
CAES
7© 2008 Electric Power Research Institute, Inc. All rights reserved.
Potential Role of the Virtual Service Aggregator
TraditionalPower PlantsTraditional
Power Plants
RenewableResources
RenewableResources
EnergyStorage
EnergyStorage
TransmissionGrid
TransmissionGrid
Real RegionalControl Center
Real RegionalControl Center
VirtualService
Aggregator
Σ
VirtualService
Aggregator
Σ
End Usesand
DistributedResources
End Usesand
DistributedResources
FinancialSettlement ofNet Difference
Power Flow
FinancialTransaction
8© 2008 Electric Power Research Institute, Inc. All rights reserved.
Alarm Management
Alarm Management• Need to diagnose root-cause of
alarm messages
• Need to link diagnosis to operator procedure
• Current EMS alarm management uses technologies of the 1970s
• Need to integrate all sources of data and messages, through a hierarchical approach
9© 2008 Electric Power Research Institute, Inc. All rights reserved.
Why Accurate Load and Generator Models Are Needed?
• Inadequacy of current model data– Inaccurate voltage recovery
simulation after disturbances– Uncertainty about generator
reactive power capabilities• Implications
– Uncertainty about the stability margin of the power grid
– Unaware of real risk of cascading blackouts or voltage collapse, or
– Under utilization of available stability margin for greater economic benefits
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.2
0.4
0.6
0.8
1
Time (seconds)
Vol
tage
(pu)
Hassayampa 500 kV
MeasuredSimulated H=0.3Simulated H=0.03Simulated H=0.05Simulated H=0.1Simulated H=0.2Simulated H =0.5
Southern Co.’s GenVARRTM
10© 2008 Electric Power Research Institute, Inc. All rights reserved.
Major Power System Disturbances
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60
Duration in Hours
Load
Los
t in
GW
1 - 2003 NE
2 - 1965 NE
3 - 1977 NYC
4 - 1982 WSCC 5 - 1996 WSCC
6 - 1996 WSCC
7 - 1998 MW
Restoration Objectives: ▪ Minimize Duration of Outages ▪ Minimized Unserved Loads ▪ Avoid Equipment Danage
Source: Mike Adibi, NSF/EPRI Workshop on Understanding and Preventing Cascading Failures in Power Systems, Oct 28, 2005.
Effective System Restoration Can Reduce The Societal Impact Of Widespread Blackouts
•Operators need online decision support for restoration strategies
•How can automation be used to improve system restoration?
11© 2008 Electric Power Research Institute, Inc. All rights reserved.
Prevention of Cascading Outages – Safety Nets
• Application of SynchroPhasor Measurements for Controlled Separation, Load Shedding and Generation Rejection– Controlled separation is an
effective last resort to mitigate severe cascading failures
– Voltage Instability Load Shedding
– Online risk monitoring of potential cascading outages
12© 2008 Electric Power Research Institute, Inc. All rights reserved.
Thank you