基于微粒群优化算法的电力系统动态无功优化

根据系统负荷曲线变化趋势按单调性将一天的预测负荷曲线划分为若干(小于24)个时段，并将动作次数约束还原为经济成本，将它与网损费用等之和作为目标函数，并考虑了各种运行约束条件，采用微粒群优化算法进行计算，该算法收敛速度较快。与静态无功优化模型相比表明，该无功优化模型更适合给定的地区电网，优化后全天的网损略有增大，但变压器抽头调节次数以及电容器组投切次数明显减少。

Dynamic Reactive Power Optimization Based on Particle Swarm Optimization Algorithm

In the light of the variation trend of load curve, the forecasted daily load curve is divided into several time intervals according to its monotonicity and the amount of the intervals is less than 24, meanwhile the restraint of compensation devices’ action times is resolved into economic cost and the sum of the economic cost and other charges such as network loss and so on during the current time interval is taken as objective function in which various operational restraints are taken into account, thus a reactive power optimization model is constructed. The proposed mathematical model is solved by particle swarm optimization (PSO) algorithm that can rapidly converge. Compared with static reactive power optimization model, the proposed reactive power optimization model is more suitable to given regional power network. Although the network loss might be slightly increased after the optimization, the switching times of transformer taps and the switching on/off times of capacitor banks are evidently reduced.