基于粒子群算法的24小时综合无功协调优化

The 24 hours reactive power optimization and coordination based on particle swarm algorithm

对于电力系统24小时无功协调优化来说,优化方法是使用粒子群优化算法及罚函数法,将所有的不等式约束方程式引入原目标函数作为惩罚项;优化目标是以全天经济费用最小作为目标函数;优化过程为静态优化和综合优化两个阶段。并根据在线负荷预测来确定24个时刻的并联电容器组的投切状态和变压器分接头的位置。将粒子群算法用于求解多目标无功优化问题中能够有效降低有功网损,减少无功补偿成本,而且其收敛性能好、收敛速度快、稳定性好。

For 24 hours reactive power optimization and coordination in the power system,the optimization method is used in particle swarm optimization algorithm and penalty function method to bring all the inequality constraint equations into the original objective function,which is optimized as a penalty term. The optimization goal is the minimum economic cost as the objective function throughout the day,and the optimization procedure is composed of two stages of static and comprehensive optimization. Based on the on-online forecasted load powers,the shunt capacitors switching states and transform tap stalls for 24 hours are determined. The particle swarm algorithm is used to solve the multi-objective reactive power optimization problem,which can not only reduce the active power loss effectively and the cost of reactive power compensation,but also improve the convergence performance,the convergence speed and the stability.