基于遗传-模拟退火算法的配电网电容器优化配置

随着配电网规模的不断扩大,传统的依照经验配置电容器的方法日显其局限性,具体表现在电容器容量配置得不足或过剩,更严重的情况是部分电容器需要投入时由于局部电压的限制而不能投入.将遗传-模拟退火算法用于解决配电网电容器优化配置问题,建立了相应的数学模型,目标函数为配电网有功网损费用、电容器的购置及安装费用总和最小,该算法将电容器的安装位置及安装容量离散化,并在初始方案种群产生和群体进化策略两方面对该算法作了改进,对一个30节点配电网的电容器优化配置结果表明了遗传-模拟退火算法的合理性和可行性.

OPTIMAL CONFIGURATION OF CAPACITORS IN RADIAL DISTRIBUTION NETWORK BASED ON GENETIC-IMPROVED SIMULATED ANNEALING ALGORITHM

Along with the continuous enlargement of distribution network the limitation of traditional experience based capacitor configuration reveals the lack or excess of configured capacitors, more seriously, when part of capacitors should be put into operation, they cannot be switched on because of the restriction of local voltage. The authors applied the genetic-simulated annealing algorithm to the solution of optimal capacitor configuration of distribution network and built up corresponding mathematical model, in which the minimized sum of minimum active power loss charge, the purchasing cost and the installation cost was the objective function. In this algorithm the positions where the capacitors were installed and the installed capacity of capacitors were discrete and the proposed algorithm was improved in both aspects of population generation and strategy of colony evolution strategy. The optimal capacitor configuration result of a 30-bus distribution network by the proposed algorithm showed that this algorithm was reasonable and feasible.