丁志龙, 张亚超. 考虑灾害不确定性和调度过程非预期性的配电网多阶段韧性提升策略[J]. 电网技术, 2025, 49(1): 146-156. DOI: 10.13335/j.1000-3673.pst.2024.0031
引用本文: 丁志龙, 张亚超. 考虑灾害不确定性和调度过程非预期性的配电网多阶段韧性提升策略[J]. 电网技术, 2025, 49(1): 146-156. DOI: 10.13335/j.1000-3673.pst.2024.0031
DING Zhilong, ZHANG Yachao. A Multi-stage Resilience Enhancement Strategy for Distribution Networks Considering Disaster Uncertainties and Scheduling Non-anticipativity[J]. Power System Technology, 2025, 49(1): 146-156. DOI: 10.13335/j.1000-3673.pst.2024.0031
Citation: DING Zhilong, ZHANG Yachao. A Multi-stage Resilience Enhancement Strategy for Distribution Networks Considering Disaster Uncertainties and Scheduling Non-anticipativity[J]. Power System Technology, 2025, 49(1): 146-156. DOI: 10.13335/j.1000-3673.pst.2024.0031

考虑灾害不确定性和调度过程非预期性的配电网多阶段韧性提升策略

A Multi-stage Resilience Enhancement Strategy for Distribution Networks Considering Disaster Uncertainties and Scheduling Non-anticipativity

  • 摘要: 近年来,台风暴雨成为影响沿海城市配电网安全稳定运行的典型自然灾害。为有效应对该极端灾害的影响,提出一种考虑台风暴雨灾害时空不确定性的配电网多阶段韧性提升策略。首先,根据台风历史数据模拟其灾害场景,计算相应场景下的配电线路故障概率,从而构建考虑台风暴雨灾害攻击的不确定集。在此基础上,结合电力系统调度过程的非预期性特点,建立基于上述不确定集的多阶段鲁棒优化模型。其次,提出一种预拓展鲁棒对偶动态规划算法对其进行求解,获取灾前防御策略和训练好的灾中最恶劣情况未来成本函数,据此求解极端灾害发生过程中各时段的紧急响应策略,从而在离线训练和在线应用有机结合的框架下制定最优的配网韧性提升策略。最后,以不同规模的配网为测试系统,验证了所提模型和方法的有效性。

     

    Abstract: In recent years, typhoons and rainstorms have increasingly become the typical natural disasters that affect the safe and stable operation of distribution networks in coastal cities. To effectively cope with the influence of extreme disasters, a multi-stage resilience enhancement strategy is proposed for distribution networks, considering the spatiotemporal uncertainty of typhoon rainstorm disasters. Firstly, the disaster scenarios are simulated according to the historical data of typhoons, and the failure probability of distribution lines is calculated to construct an uncertainty set for the typhoon rainstorm disaster attack. On this basis, combined with the non-anticipativity characteristics of the power system scheduling process, a multi-stage robust optimization model based on the uncertainty set above is established. Secondly, a pre-extended robust dual dynamic programming algorithm is developed for solving. Then, the pre-disaster defense strategies and well-trained worst-case cost-to-go functions during disasters are obtained, which can solve the emergency response strategies for each period of extreme disasters. As a result, the optimal resilience enhancement strategies for distribution networks have been made in an organic combination of offline training and online application. Finally, the test systems for distribution networks with different scales are used to verify the effectiveness of the proposed model and method.

     

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