Proactive Operation Strategies Chong Wang, Yunhe Hou, Feng Qiu, - - PowerPoint PPT Presentation
1 Paper No: 18PESGM0497 Resilience Enhancement With Sequentially Proactive Operation Strategies Chong Wang, Yunhe Hou, Feng Qiu, Shunbo Lei , Kai Liu The University of Hong Kong leishunbo@eee.hku.hk 2 Electric Power System Wide Geographical
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Paper No: 18PESGM0497
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Aims: Reliability and Safety Cyber System
Monitor and Control (supervisory control and data acquisition, SCADA)
System Operators
Wide Geographical Coverage
Cybersecurity-Related Weather-Related
Conventional Strategies
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Events % of events Mean size MW Mean size in customers Earthquake 0.8 1408 375900 Hurricane/Tropical storm 4.2 1309 782695 Lightning 11.3 270 70944 Wind/rain 14.8 793 185199 Ice storm 5 1152 343448 Tornado 2.8 367 115439 Other cold weather 5.5 542 150255 Fire 5.2 431 111244 Intentional attack 1.6 340 24572 Supply shortage 5.3 341 138957 Other external cause 4.8 710 246071 Equipment failure 29.7 379 57140 Operator error 10.1 489 105322 Voltage reduction 7.7 153 212900
Table 1.1 Blackouts between 1984 and 2006 in the United States[1]
Office of the President
[1] P. Hines, J. Apt, and S. Talukdar, "Trends in the history of large blackouts in the United States," in IEEE PES General Meeting 2008, pp. 1-8.
Figure 1.1 Increasing trends of weather-related events from the years 1992 to 2012 [2]
[2] Executive Office of the President. Economic Benefits of Increasing Electric Grid Resilience to Weather Outages.
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National Academies: the ability to prepare and plan for, absorb, recover from and more successfully adapt to adverse events. [1] National Infrastructure Advisory Council: the ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially disruptive event. [2] ……
[1] National Academies. Disaster Resilience: A National Imperative. [2] National Infrastructure Advisory Council. A Framework for Establishing Critical Infrastructure Resilience Goals.
key point: ability to plan for/ride through/recover from potential adverse events
Robustness Resourcefulness Rapid recovery Adaptability/Lessons Learned Prior to Events During Events After Events Post-Incident Learning
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– perform strategies as an event unfolds
– Stochastic: uncertainty of exact states in the future – Sequential: the future consequence caused by current strategies
A B
t1 t2 t4 t3 t5
Trajectory of Typhoon
t1 t2 t4 t3 t5 Failure Rates Time A B PA,1 PB,2 PA,1 PB,3 (a) t1 t2 t4 t3 t5 Markov States Time (b) S0 S0 SA S0 SA SB SAB S0 SA SB SAB S0 SA SB SAB
System states due to weather events Transition Probabilities between Different System States: Depend on component failure rate due to weather events
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– recursive model : current cost and future cost
Function value Cost of loss of load Transition probability
Future Cost Current Cost
– Power balance – Ramping rates of generators – Power flow of lines – Generator limits – Load limits – Voltage limits
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13 12 15 14
G6
16 17 10 9 27 29 30 11 21 22 26 25 24 23 19 20
G5
18 1 2 3 4 G1 G2 6 28 7 5 G3
t2 t3 t1 t4 t5 t6
8 G4
M1: The proposed method; M2: Non-proactive strategies. In this case, the system operators will not proactively redispatch the system beforehand. They only take actions after some events, i.e., line faults, to minimize the loss of load, with consideration of operation constraints.
Possible Failure Scenarios on Trajectory (only scenarios with nonzero loss of load) Loss of Load with M1 Loss of Load with M2 0.1 0.2 0.3 Loss of Load (p.u.)
From the perspective of all scenarios, the proposed method is more effective.
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3 3 7 7 7 8 8 2 8 7 9 8 1 9 7 9 6 8 3 8 4 8 5 9 5 9 4 1 9 8 8 7 8 6 8 8 8 9 9 9 2 9 3 9 1 1 2 1 1 1 3 1 4 1 6 1 7 1 5 1 1 2 1 1 1 1 1 1 9 1 8 9 9 2 4 7 2 7 3 7 7 1 1 7 4 7 7 5 1 1 8 7 6 2 1 1 9 1 2 1 1 7 6 5 1 1 7 8 8 9 1 3 1 6 1 7 1 4 1 5 1 3 1 1 3 1 8 2 2 2 2 3 2 5 2 6 3 3 2 2 3 1 2 9 2 8 1 1 5 1 1 4 2 7 2 3 4 1 3 6 3 5 3 4 3 8 3 7 3 9 4 4 1 4 3 4 2 4 4 4 5 4 6 4 8 4 7 4 9 6 9 6 5 6 6 6 7 6 4 6 2 5 5 1 5 2 5 3 5 4 5 5 5 6 5 7 5 8 5 9 6 3 6 1 6 6 8 1 1 6
Typhoon Trajectory Normal Bus Generator Bus
t1 t2 t3 t4 t5 t6 t7 t8 t9
Area A Area B
1 2 3 4 5 6 7 8 1700 1900 2100 2300 2500 2 6 10 14 18 Decision Epochs Real Power Outputs (MW)
Real Power Outputs of Area A (Left Axis) Real Power Outputs of Area B (Left Axis) Loss of Load (Right Axis)
Loss of Load (MW)
area A : 1,670 MW load and 1,812 MW real output, area B : 2,572 MW load and 2,430 MW real output.
Real output in area A decreases and real output in the area B increases to reduce real power through transmission lines on the trajectory of the typhoon, in case of loss of load due to potential outage of these lines.