地震往往会对配电网产生破坏, 影响供电质量. 配电网震后 抢修资源保障、抢修人员路径规划等易受失电区域地理特征影响. 文章以累积抢修绩效最大为目标建立考虑地理特征的配电网震后恢复优化模型, 并根据模型特点设计一种混合改进细菌群趋药性算法进行求解. 为验证上述模型的有效性和算法的性能, 基于IEEE34, IEEE123 和IEEE240节点系统随机产生不同情景, 即不同地理特征、不同受损程度与不同抢修规模. 结果表明: 1) 受地理特征影响, 失电区域交通路网稀疏程度对配电网震后恢复影响较大, 随着交通路网稀疏程度的增加, 配电网震后累积抢修绩效逐渐降低; 2) IEEE34节点系统中, 混合改进细菌群趋药性优化算法的求解质量与求解时间均优于遗传算法和枚举法, 顺序为混合改进细菌群趋药性优化算法、遗传算法、枚举算法; 3) IEEE123和IEEE240节点系统中, 混合改进细菌群趋药性优化算法求解质量与求解时间均优于遗传算法.

Earthquake causes great damage to the distribution system, decreasing the quality of power service. Because of geographical characteristics of the outage areas have impacts on the routing of repair crews and logistics support. Therefore, we propose an optimization model for post-earthquake distribution system restoration considering geographical characteristics to maximize the cumulative utility of repairing. A hybrid improved bacterial colony chemotaxis optimization algorithm (HIBCCOA) is designed to solve this model. We take a serial of scenarios derived from IEEE34, IEEE123, and IEEE240-bus system to validate the effectiveness of the model and the performance of the approach by considering different geographical characteristics, different damaged levels, different scales of repair crews. The results show that: 1) The geographical characteristics of the outage areas have impacts on the density of their transportation road network, moreover, the cumulative utility of repairing decreases with the density of their transportation road network decreases; 2) In IEEE34-bus system, HIBCCOA is out performance of genetic algorithm (GA) and the enumerate method (EM) for objective values and CPU times, the rank of them is HIBCCOA, GA, and EM; 3) In IEEE123 and IEEE240-bus system, HIBCCOA is better than GA in the objective values and the CPU times.