| 引用本文: | 黄 煜,李忠行,王卫民,等.基于拓扑重构及PCE代理模型的配电网概率风险评估[J].电力系统保护与控制,2025,53(16):74-85.[点击复制] |
| HUANG Yu,LI Zhongxing,WANG Weimin,et al.Probabilistic risk assessment of distribution networks based on topological reconfiguration and PCE surrogate model[J].Power System Protection and Control,2025,53(16):74-85[点击复制] |
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| 摘要: |
| 极端事件对配电网运行安全构成严重威胁，准确高效地评估配电网的概率风险具有重要意义。为解决传统蒙特卡洛模拟(Monte Carlo simulation, MCS)效率低和现有代理模型在低概率区域精度不足的问题，提出了一种兼顾计算效率与精度的风险评估方法。首先，基于核密度估计(kernel density estimation, KDE)构建了分布式电源出力的非参数概率模型，并结合指数分布和天气因子进行设备状态的动态概率建模。然后，将输入样本分为支路故障和非故障两类。非故障样本使用多项式混沌展开(polynomial chaos expansion, PCE)代理模型快速计算概率潮流；对故障样本，建立拓扑重构模型进行优化潮流计算，以准确捕获拓扑变化下的系统风险。基于此，构建了节点电压越限、支路潮流过载和失负荷3个风险指标，并通过博弈论的主客观组合赋权法确定指标权重，得到综合风险评价值。最后，基于IEEE33与IEEE118节点配电系统的仿真分析表明，该方法能够有效应对极端事件引起的拓扑不确定性，提高风险评估效率与准确性。 |
| 关键词: 极端事件 不确定性 概率风险评估 多项式混沌展开 配电网重构 |
| DOI：10.19783/j.cnki.pspc.246171 |
| 投稿时间：2024-05-29修订日期：2025-03-03 |
| 基金项目:国家自然科学基金项目资助(62293500，62293505，62303243)；江苏省自然科学基金项目资助(BK20232026)；智能电网保护和运行控制国家重点实验室项目资助(SGNR0000KJJS2302149) |
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| Probabilistic risk assessment of distribution networks based on topological reconfiguration and PCE surrogate model |
| HUANG Yu1,LI Zhongxing1,WANG Weimin1,HU Songlin1,WANG Yi2,TAN Chao2 |
| (1. Institute of Advanced Technology for Carbon Neutrality, Nanjing University of Posts and Telecommunications, Nanjing 210023,
China; 2. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China) |
| Abstract: |
| Extreme events pose a significant threat to the operational security of distribution networks, making accurate and efficient probabilistic risk assessment crucial. To address the low efficiency of traditional Monte Carlo simulation (MCS) and the insufficiency accuracy of existing surrogate models in low-probability regions, this paper proposes a risk assessment method that balances computational efficiency and accuracy. First, a non-parametric probabilistic model of distributed generation output is built using kernel density estimation (KDE), while dynamic probability modeling of equipment states is performed using exponential distributions and weather factors. Next, the input samples are divided into branch fault and non-fault categories. For non-fault samples, a polynomial chaos expansion (PCE) surrogate model is used for rapid probabilistic power flow calculation. For fault samples, a topology reconfiguration model is employed to calculate optimal power flow and capture risks under topology changes. Based on this, three risk indicators of node voltage over-limit, branch power flow overload, and load loss are constructed. The weights of these indicators are determined by the subjective and objective combination weighting method of game theory, and the comprehensive risk evaluation value is obtained. Simulation results on the IEEE33 and IEEE118 node distribution systems show that the proposed method effectively handles topology uncertainties caused by extreme events, improving both the efficiency and accuracy in risk assessment. |
| Key words: extreme events uncertainty probabilistic risk assessment polynomial chaos expansion distribution network reconfiguration |
