考虑不确定性因素影响的配电网防灾资源规划

Disaster Prevention Resource Planning for Distribution Networks Considering the Impact of Uncertainty Factors

摘要: 极端灾害对配电网安全运行构成严峻威胁，其不确定性(灾害强度、负荷波动、电源出力)显著增加了防灾资源规划的难度。本文提出一种考虑多重不确定性的配电网防灾资源规划方法。首先，建立设备故障概率模型(线路绝缘子闪络、杆塔倒塌及变压器水淹)、负荷动态修正模型以及分布式电源(光伏、风电)时变出力模型，量化灾害全过程的源–荷不确定性。其次，构建以最小化负荷损失成本为目标的两阶段优化模型：灾前阶段优化风储与光储联合系统的部署位置及抢修路径，灾中/灾后阶段协调储能放电与分布式电源出力，满足负荷分级恢复、功率平衡及资源数量等约束。采用混合整数线性规划求解。在39节点系统上的多场景仿真表明，所提方法可根据灾害强度动态调整资源策略，部署联合系统后负荷损失成本降幅达51.8%~83.2%，验证了模型在提升配电网抗灾经济性与适应性方面的有效性和鲁棒性。

Abstract: Extreme disasters pose severe threats to the secure operation of distribution networks, and their uncertainties—including disaster intensity, load fluctuations, and distributed generation output—significantly increase the difficulty of disaster prevention resource planning. This paper proposes a planning method that accounts for multiple uncertainties. First, equipment failure probability models (covering insulator flashover, pole/tower collapse, and transformer flooding), a dynamic load correction model, and time-varying output models for distributed generators (photovoltaic and wind power) are established to quantify the source-load uncertainties throughout the disaster process. Second, a two-stage optimization model is constructed to minimize the load loss cost: the pre-disaster stage optimizes the siting of wind-storage and PV-storage combined systems as well as repair routing, while the in-disaster/post-disaster stage coordinates storage discharge and distributed generation output, subject to constraints on load-class restoration, power balance, and resource availability. The model is solved via mixed-integer linear programming. Multi-scenario simulations on a 39-node system demonstrate that the proposed method can dynamically adjust resource strategies according to disaster intensity, achieving a reduction in load loss cost of 51.8%~83.2% after deploying the combined systems, thereby verifying its effectiveness and robustness in enhancing the economic resilience and adaptability of distribution networks against disasters.