Xu Luo, Zeng Hongtai, Lin Ning, Yang Yue, Guo Qinglai, Poor H Vincent
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544.
Center for Policy Research on Energy and the Environment, Princeton University, Princeton, NJ 08544.
Proc Natl Acad Sci U S A. 2025 May 20;122(20):e2426620122. doi: 10.1073/pnas.2426620122. Epub 2025 May 14.
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 demand loss. Also, the proposed REDUCER model consistently demonstrates its effectiveness in managing the increasing temporal net demand variability introduced by growing large-scale distributed solar integration while maintaining minimal operational costs. This model offers a practical solution for cost-effective and resilient electricity dispatch of modern power systems with large-scale renewable integration facing intensifying climate risks.
配电网络大规模集成了分布式可再生能源，尤其是屋顶太阳能光伏系统，在飓风等极端气候期间，它是现代电力系统中分布最广泛但也最脆弱的组成部分。然而，现有的日前电力调度方法主要侧重于输电网络，缺乏管理与庞大配电网络相关的时空风险的能力，由于计划发电量与实际需求不匹配，这可能会导致严重的电力不平衡。为了在极端气候加剧和分布式可再生能源集成不断增加的情况下解决这一日益关键的差距，我们引入了具有可再生能源集成的极端气候下风险感知电力调度（REDUCER），这是一种风险感知日前电力调度模型，它将具有大规模分布式可再生能源集成的配电网络的高分辨率时空风险分析纳入熵值风险约束混合整数凸优化框架。应用于2022年飓风菲奥娜袭击下的波多黎各电网时，所提出的REDUCER模型被认为能够有效管理这些风险，大幅减少对额外灵活性资源来应对电力不平衡的依赖，与已经考虑平均需求损失的标准机组组合策略相比，在极端情况下将总体运营成本降低约30%。此外，所提出的REDUCER模型在管理大规模分布式太阳能集成增加所带来的日益增加的时间净需求变化方面持续展现出有效性，同时保持最低运营成本。该模型为面临日益加剧气候风险的具有大规模可再生能源集成的现代电力系统的经济高效且有韧性的电力调度提供了一种切实可行的解决方案。
