Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events
Sequential extreme weather events (SEWEs), such as hurricanes and tropical storms occurring in succession, pose significant challenges to local energy (LE) and flexibility (LF) markets. Effective coordination of preventive, emergency, and restorative strategies can mitigate losses during these event...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525000511 |
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| _version_ | 1823859468653297664 |
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| author | Xuemei Dai Jing Zhou Xu Zhang Kaifeng Zhang Wei Feng |
| author_facet | Xuemei Dai Jing Zhou Xu Zhang Kaifeng Zhang Wei Feng |
| author_sort | Xuemei Dai |
| collection | DOAJ |
| description | Sequential extreme weather events (SEWEs), such as hurricanes and tropical storms occurring in succession, pose significant challenges to local energy (LE) and flexibility (LF) markets. Effective coordination of preventive, emergency, and restorative strategies can mitigate losses during these events, but designing optimal trading strategies for joint LE and LF markets remains complex. This paper introduces a novel trading method to address this challenge. First, a two-layer graph neural network (GNN) is employed to predict the probability distribution of system outages caused by SEWEs. Then, a joint LE and LF market transaction model is developed to optimize multi-stage trading and minimize overall losses throughout SEWEs. To address the uncertainty of SEWEs, a probability forecast-driven distributionally robust joint chance constraint (DRJCC) optimization method is proposed, which is efficiently solvable as a convex conic problem. Finally, case studies conducted on modified IEEE 141-bus and 300-bus systems validate the approach, showing reductions in load shedding and trading costs by up to 15.39% and 42.88%, respectively, compared to single-stage or two-stage strategies. Additionally, the two-layer GNN model achieves a root mean square error of 0.01, demonstrating high accuracy in predicting system outage statuses. |
| format | Article |
| id | doaj-art-51c6ba9b637b4864a66be71d2a3e10fa |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-51c6ba9b637b4864a66be71d2a3e10fa2025-02-11T04:33:29ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-04-01165110500Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather eventsXuemei Dai0Jing Zhou1Xu Zhang2Kaifeng Zhang3Wei Feng4College of Automation Engineering, Shanghai University of Electric Power, No. 2588 Changyang Road, Shanghai 200090, ChinaChina Electric Power Research Institute, Nanjing 211103, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Avenue, Shenzhen 518055, ChinaKey Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, No. 2 Sipailou Road, Nanjing 210096, China; Corresponding author.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Avenue, Shenzhen 518055, ChinaSequential extreme weather events (SEWEs), such as hurricanes and tropical storms occurring in succession, pose significant challenges to local energy (LE) and flexibility (LF) markets. Effective coordination of preventive, emergency, and restorative strategies can mitigate losses during these events, but designing optimal trading strategies for joint LE and LF markets remains complex. This paper introduces a novel trading method to address this challenge. First, a two-layer graph neural network (GNN) is employed to predict the probability distribution of system outages caused by SEWEs. Then, a joint LE and LF market transaction model is developed to optimize multi-stage trading and minimize overall losses throughout SEWEs. To address the uncertainty of SEWEs, a probability forecast-driven distributionally robust joint chance constraint (DRJCC) optimization method is proposed, which is efficiently solvable as a convex conic problem. Finally, case studies conducted on modified IEEE 141-bus and 300-bus systems validate the approach, showing reductions in load shedding and trading costs by up to 15.39% and 42.88%, respectively, compared to single-stage or two-stage strategies. Additionally, the two-layer GNN model achieves a root mean square error of 0.01, demonstrating high accuracy in predicting system outage statuses.http://www.sciencedirect.com/science/article/pii/S0142061525000511SEWEsPreventive tradingRestorative tradingGNNDRJCC |
| spellingShingle | Xuemei Dai Jing Zhou Xu Zhang Kaifeng Zhang Wei Feng Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events International Journal of Electrical Power & Energy Systems SEWEs Preventive trading Restorative trading GNN DRJCC |
| title | Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events |
| title_full | Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events |
| title_fullStr | Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events |
| title_full_unstemmed | Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events |
| title_short | Coordination of preventive, emergency and restorative trading strategies under uncertain sequential extreme weather events |
| title_sort | coordination of preventive emergency and restorative trading strategies under uncertain sequential extreme weather events |
| topic | SEWEs Preventive trading Restorative trading GNN DRJCC |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525000511 |
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