发表机构
Institute of Computing, University of Campinas（坎皮纳斯大学计算研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对负荷不确定性，采用两阶段随机规划构建MISOCP模型，在12个不同规模网络上验证了配电网重构可降低网损与电压越限，且空间异质场景对不确定性表征及重构决策有重要影响。
AI 中文摘要
本文采用两阶段随机规划模型研究需求不确定性下的配电网重构问题。网络拓扑在第一阶段确定，而电气变量则针对每个场景分别确定。其确定性等价模型被构建为混合整数二阶锥规划（MISOCP）问题，该模型考虑了功率平衡、网损、电压约束和辐射状结构约束。在12个节点数从17到10561的网络上进行的实验中，考虑了三种需求场景。重构在所有实例中均降低了预期网损，降幅范围为3.23%至65.23%，平均降幅为31.76%，同时在事后评估中电压越限数量减少了约55%。12个问题中有11个在规定时间内求解到最优解。然而，由于所考虑场景的同形结构保留了负荷的空间分布，随机建模的额外收益可忽略不计。结果表明，空间异质场景对于表征不确定性以对重构决策产生有意义的影响至关重要。
英文摘要
This paper investigates distribution network reconfiguration under demand uncertainty using a two-stage stochastic formulation. The network topology is selected in the first stage, whereas the electrical variables are determined separately for each scenario. The deterministic equivalent is formulated as a MISOCP problem that accounts for power balances, losses, voltage constraints, and radiality. Experiments on 12 networks, ranging from 17 to 10,561 nodes, consider three demand scenarios. Reconfiguration reduced expected losses in all instances, with reductions ranging from 3.23% to 65.23% and averaging 31.76%, while also reducing the number of voltage violations in the ex post evaluation by approximately 55%. Eleven of the 12 problems were solved to optimality within the prescribed time limit. However, the additional benefit of stochastic modeling was negligible because of the homothetic structure of the scenarios considered, which preserves the spatial distribution of loads. The results show that spatially heterogeneous scenarios are important for the representation of uncertainty to meaningfully affect reconfiguration decisions.
