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 Coverage Conventional Strategies Weather-Related Cyber System Cybersecurity-Related Monitor and Control (supervisory control and data acquisition, SCADA) System Operators Resilience Aims: Reliability and Safety
3 Weather-related events in power systems Table 1.1 Blackouts between 1984 and 2006 in the United States[1] Events % of events Mean size MW Mean size in customers Earthquake 0.8 1408 375900 4.2 1309 782695 Hurricane/Tropical storm Lightning 11.3 270 70944 14.8 793 185199 Wind/rain Ice storm 5 1152 343448 2.8 367 115439 Tornado Other cold weather 5.5 542 150255 Fire 5.2 431 111244 Intentional attack 1.6 340 24572 Figure 1.1 Increasing trends of weather-related Supply shortage 5.3 341 138957 events from the years 1992 to 2012 [2] 4.8 710 246071 Other external cause [1] P. Hines, J. Apt, and S. Talukdar, "Trends in the history of Equipment failure 29.7 379 57140 large blackouts in the United States," in IEEE PES General Meeting 2008, pp. 1-8. 10.1 489 105322 Operator error [2] Executive Office of the President. Economic Benefits of Voltage reduction 7.7 153 212900 Increasing Electric Grid Resilience to Weather Outages. 44% of the events were weather-related. weather-related outages lead to about $25 billion economic losses annually, based on the analysis of the Executive Office of the President
4 Power System Resilience 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] …… key point: ability to plan for/ride through/recover from potential adverse events Prior to Events Robustness During Events Resourcefulness After Events Rapid recovery Post-Incident Adaptability/Lessons Learned Learning [1] National Academies. Disaster Resilience: A National Imperative. [2] National Infrastructure Advisory Council. A Framework for Establishing Critical Infrastructure Resilience Goals.
5 Markov-based generation redispatch • Critical measure to mitigate damages – perform strategies as an event unfolds • Influences of weather-related events on grids – Stochastic: uncertainty of exact states in the future – Sequential: the future consequence caused by current strategies • A Markov model for sequential proactive generation redispatch t 2 t 3 A t 4 B Markov States S AB S AB S AB Failure Rates t 1 System states P A,1 B S B S B S B P B,3 A P A,1 due to weather t 5 P B,2 S A S A S A S A events S 0 S 0 S 0 S 0 S 0 t 1 t 2 t 3 t 4 t 5 t 1 t 2 t 3 t 4 t 5 Trajectory of Typhoon Time Time (a) (b) Transition Probabilities between Different System States: Depend on component failure rate due to weather events
6 Sequential decision processes – recursive model : current cost and future cost Future Cost Current Cost Function value Cost of loss of load Transition probability – Generator limits – Power balance – Ramping rates of generators – Load limits – Power flow of lines – Voltage limits
7 Simulation results 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., t 6 line faults, to minimize the loss of load, with consideration of operation constraints. G3 G1 G2 G4 8 5 1 2 t 5 7 0.3 Loss of Load (p.u.) 28 6 3 t 4 4 0.2 29 13 10 9 16 G6 27 0.1 12 20 17 t 3 11 30 18 G5 14 19 Possible Failure Scenarios on Trajectory 22 21 26 t 2 (only scenarios with nonzero loss of load) 15 23 24 25 Loss of Load with M1 Loss of Load with M2 t 1 From the perspective of all scenarios, the proposed method is more effective.
8 Simulation results 5 5 Area A 2500 18 5 9 Real Power Outputs (MW) 5 4 Loss of Load (MW) 4 1 3 9 5 3 5 6 6 3 2300 4 14 3 3 0 6 0 3 7 5 7 5 8 6 1 3 5 t 9 4 2 5 2 3 6 4 3 4 4 5 1 5 0 6 4 6 2 4 5 2100 10 3 4 t 8 4 8 4 6 6 6 4 9 6 7 1 1 7 4 7 6 5 Typhoon 1900 6 t 7 2 1 5 3 8 1 4 1 1 6 Trajectory 1 1 1 6 8 1 6 9 1 1 2 1 2 1 9 t 6 1 0 9 1 1 0 1 3 2 1700 1 8 t 5 1 0 8 7 1 7 1 0 3 t 1 3 t 4 t 2 1 0 5 t 3 1 6 6 2 0 7 0 1 2 3 4 5 6 7 8 1 1 1 0 4 8 1 1 1 3 1 0 7 Decision Epochs 3 3 2 2 7 5 9 8 2 1 3 1 7 7 3 0 7 8 1 0 6 7 9 8 0 1 0 5 1 0 0 1 9 9 2 2 7 7 1 1 Real Power Outputs of Area A (Left Axis) 2 9 1 1 4 4 7 4 9 7 2 4 1 0 7 2 1 1 8 Real Power Outputs of Area B (Left Axis) 1 1 5 8 2 9 2 2 3 9 4 1 0 2 7 3 7 6 8 8 9 6 2 8 Loss of Load (Right Axis) 9 2 7 9 3 9 1 2 Area B 8 3 6 9 5 2 5 8 4 Real output in area A decreases and real output 8 5 8 9 9 0 8 8 Generator Bus Normal Bus 8 6 in the area B increases to reduce real power 8 7 through transmission lines on the trajectory of area A : 1,670 MW load and 1,812 MW real output, the typhoon, in case of loss of load due to potential outage of these lines. area B : 2,572 MW load and 2,430 MW real output.
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