中文摘要：

提出了一种基于两层遗传算法的多时段无功优化方法，将复杂的无功优化问题转化为多个时段静态无功优化的并行处理问题。第一层优化是针对调度周期内的每个时段．建立传统静态无功优化模型，对全调度周期内各个时段进行并行计算，并统计出多组较好的优化状态，构成全调度周期内控制设备动作次数的寻优状态空间；第二层优化是针对整个调度周期，建立以动作次数最少为目标的无功优化模型，从第一层形成的状态空间中寻出控制设备动作次数较少所对应的潮流分布．从而得到有功网损、电压质量及控制设备动作次数的综合优化效果。此外，该方法易于实现并行处理。算例表明．所提出的方法优化效果好．有在线应用的前景．

英文摘要：

A multi- period reactive power optimization method based on two- layer genetic algorithm is presented,which recasts the complex reactive power optimization problem into a parallel processing problem of multi- period static reactive power optimization. In the first layer,the traditional static reactive power optimization model is built and the calculations are carried out in parallel for each time interval within the dispatch period to find out multiple groups with better optimization status for composing the state space of control device action amount within dispatch period. In the second layer,the reactive power optimization model with the minimum action amount as its objective is built and some power flow distributions with less action amount are sought out from the state space. The integrated optimization of system power loss,voltage quality and control device action amount is further realized. Because of its parallel processing,the proposed method is more effective and more suitable for online applications.