{"title":"基于风险的可解释性增强多场景光伏预测调度优化策略","authors":"Haiteng Han, Xiangchen Jiang, Simin Zhang, Chen Wu, Shuyu Cao, Haixiang Zang, Guoqiang Sun, Zhinong Wei","doi":"10.1016/j.epsr.2025.111729","DOIUrl":null,"url":null,"abstract":"<div><div>Photovoltaic (PV) power generation, as a crucial technology for addressing energy transition and climate change, is increasingly becoming essential for power systems. However, its inherent randomness and uncertainty challenge stable operation and economic efficiency. To address these, this paper proposes a risk-based scheduling optimization strategy with explainable multi-scenario photovoltaic forecasting to mitigate these uncertainties. First, by employing an enhanced Copula function and ISODATA clustering, multiple joint output scenarios are generated to capture PV uncertainty. Building on this, a Stacking regression model is utilized to improve forecasting accuracy, while Shapley Additive Explanations (SHAP) explainability analysis is incorporated to enhance the transparency and decision-making of the model. Furthermore, to optimize the dispatch strategy for PV generation, this paper introduces the GlueVaR risk measurement method, which combines the benefits of Value at Risk (VaR) and Conditional Value at Risk (CVaR), thereby refining risk management and increasing the reliability of decision-making. Case studies demonstrate that the proposed strategy enhances PV forecasting reliability, with the R² reaching 0.86, and improves model explainability through SHAP-based analysis. In addition, the GlueVaR-based risk scheduling reduces potential risk by approximately 6 %, while maintaining a balance between power system economy and stability.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111729"},"PeriodicalIF":3.3000,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A risk-based scheduling optimization strategy with explainability enhanced multi-scenario photovoltaic forecasting\",\"authors\":\"Haiteng Han, Xiangchen Jiang, Simin Zhang, Chen Wu, Shuyu Cao, Haixiang Zang, Guoqiang Sun, Zhinong Wei\",\"doi\":\"10.1016/j.epsr.2025.111729\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Photovoltaic (PV) power generation, as a crucial technology for addressing energy transition and climate change, is increasingly becoming essential for power systems. However, its inherent randomness and uncertainty challenge stable operation and economic efficiency. To address these, this paper proposes a risk-based scheduling optimization strategy with explainable multi-scenario photovoltaic forecasting to mitigate these uncertainties. First, by employing an enhanced Copula function and ISODATA clustering, multiple joint output scenarios are generated to capture PV uncertainty. Building on this, a Stacking regression model is utilized to improve forecasting accuracy, while Shapley Additive Explanations (SHAP) explainability analysis is incorporated to enhance the transparency and decision-making of the model. Furthermore, to optimize the dispatch strategy for PV generation, this paper introduces the GlueVaR risk measurement method, which combines the benefits of Value at Risk (VaR) and Conditional Value at Risk (CVaR), thereby refining risk management and increasing the reliability of decision-making. Case studies demonstrate that the proposed strategy enhances PV forecasting reliability, with the R² reaching 0.86, and improves model explainability through SHAP-based analysis. In addition, the GlueVaR-based risk scheduling reduces potential risk by approximately 6 %, while maintaining a balance between power system economy and stability.</div></div>\",\"PeriodicalId\":50547,\"journal\":{\"name\":\"Electric Power Systems Research\",\"volume\":\"246 \",\"pages\":\"Article 111729\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electric Power Systems Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378779625003219\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electric Power Systems Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378779625003219","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A risk-based scheduling optimization strategy with explainability enhanced multi-scenario photovoltaic forecasting
Photovoltaic (PV) power generation, as a crucial technology for addressing energy transition and climate change, is increasingly becoming essential for power systems. However, its inherent randomness and uncertainty challenge stable operation and economic efficiency. To address these, this paper proposes a risk-based scheduling optimization strategy with explainable multi-scenario photovoltaic forecasting to mitigate these uncertainties. First, by employing an enhanced Copula function and ISODATA clustering, multiple joint output scenarios are generated to capture PV uncertainty. Building on this, a Stacking regression model is utilized to improve forecasting accuracy, while Shapley Additive Explanations (SHAP) explainability analysis is incorporated to enhance the transparency and decision-making of the model. Furthermore, to optimize the dispatch strategy for PV generation, this paper introduces the GlueVaR risk measurement method, which combines the benefits of Value at Risk (VaR) and Conditional Value at Risk (CVaR), thereby refining risk management and increasing the reliability of decision-making. Case studies demonstrate that the proposed strategy enhances PV forecasting reliability, with the R² reaching 0.86, and improves model explainability through SHAP-based analysis. In addition, the GlueVaR-based risk scheduling reduces potential risk by approximately 6 %, while maintaining a balance between power system economy and stability.
期刊介绍:
Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview.
• Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation.
• Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design.
• Substation work: equipment design, protection and control systems.
• Distribution techniques, equipment development, and smart grids.
• The utilization area from energy efficiency to distributed load levelling techniques.
• Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.