基于电网分区的负荷恢复智能优化策略

Intelligent optimization strategy for load restoration considering power grid partitioning

针对电力系统大停电后的负荷快速恢复问题,提出了一种基于电网分区的负荷恢复智能优化策略。提出了一种大停电事故后系统恢复的最优分区策略,并在所建立的优化模型中考虑了为恢复发电机而引入的架空线路充电无功以及为恢复负荷所引入的线路合闸操作次数等影响因素。在完成对大规模系统优化分区后,对各分区建立了一个统一的并计及网络重构因素的负荷恢复优化模型,实现各分区负荷的并行恢复。针对上述所提出的优化模型,通过结合传统图论理论和遗传算法,实现基于电网分区的负荷恢复优化问题的求解。解算中,对遗传算法进行了改进,并针对应用遗传算法时所产生的大量不可行解问题,提出了随机甩负荷和最短路径修补策略的处理方法,进一步提高了算法的寻优效率和全局优化能力。以IEEE30节点系统为算例进行仿真分析,验证了所提模型与方法的正确性和有效性。

In this paper,an intelligent optimization strategy for load restoration after power system blackout considering system-partitioning is presented. First of all,an optimal system-partitioning strategy for system recovery after blackout is proposed. Some influence factors involving the charging Mvar of overhead transmission lines for generator restoration,the closing operation times of transmission lines for load restoration are considered during modeling. With respect to the result of optimal system-partitioning,a unified load restoration optimization model taking into account network reconfiguration is built to realize the parallel recovery of each partition. The genetic algorithm （GA） and the traditional graph theory are combined to solve the proposed optimization models described above. Some improvements including stochastic load shedding and the shortest path repair strategies against the problem of a large number of infeasible solutions generated by utilizing the traditional genetic algorithm are made to improve the computational efficiency and the performance of global optimization of GA further during optimization. Finally,the case studies are carried out based on the IEEE-30 test system to demonstrate the effectiveness and validity of the proposed models and methods.