Risk-aware electricity dispatch with large-scale distributed renewable integration under climate extremes

作者：Luo Xu, Hongtai Zeng, Ning Lin, Yue Yang, Qinglai Guo, H. Vincent Poor · 发表于：Proceedings of the National Academy of Sciences · 年份：2025 · DOI：10.1073/pnas.2426620122 · 被引用次数：6 · 研究领域：Electric Power System Optimization、Energy Load and Power Forecasting、Integrated Energy Systems Optimization

Distribution networks, with large-scale integration of distributed renewable resources, particularly rooftop solar photovoltaic systems, represent the most extensive yet vulnerable components of modern electric power systems during climate extremes such as hurricanes. However, existing day-ahead electricity dispatch approaches primarily focus on the transmission network and lack the capability to manage the spatiotemporal risks associated with the vast distribution networks, which can potentially lead to significant power imbalances due to the mismatches between scheduled generation and actual demand. To address this increasingly critical gap under intensifying climate extremes and growing distributed renewable integration, we introduce Risk-aware Electricity Dispatch under Climate Extremes with Renewable integration (REDUCER), a risk-aware day-ahead electricity dispatch model that incorporates high-resolution spatiotemporal risk analysis for distribution networks with large-scale distributed renewable integration into an Entropic Value-at-Risk-constrained mixed-integer convex optimization framework. Applied to the 2022 Puerto Rico power grid under Hurricane Fiona, the proposed REDUCER model is seen to effectively manage these risks with substantially less reliance on additional flexibility resources to cope with power imbalances, reducing overall operational costs by about 30% under extreme cases compared to standard unit commitment strategies already informed by average dem...