Yin Zhaoyuan, Fang Chao, Yang Haoxiang, Fang Yiping, Xie Min
Department of Advanced Design and Systems Engineering, City University of Hong Kong, Hong Kong SAR, China.
School of Management, Xi'an Jiaotong University, Xi'an, ShaanXi, China.
Risk Anal. 2023 May;43(5):979-993. doi: 10.1111/risa.13995. Epub 2022 Jul 8.
In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.
近年来，自然灾害发生频率的增加导致电网中断更为频繁，有可能造成严重的基础设施损坏和连锁故障。因此，通过实施新技术和运用优化方法来提高电力系统弹性至关重要。本文提出了一种数据驱动的空间分布鲁棒优化（DS-DRO）模型，以提供一个最优方案，针对台风等自然灾害的不确定影响来安装和调度分布式能源资源（DER）。我们采用精确的空间模型，基于风速评估系统组件的故障概率。我们根据历史台风数据构建故障分布的基于矩的模糊集。然后制定一个两阶段的DS-DRO模型，以获得最优的弹性增强策略。我们采用对偶重新表述和列与约束生成算法相结合的方法，并通过在香港地区投影的改进IEEE 13节点可靠性测试系统展示了所提方法的有效性。
