基于多种群分层粒子群优化算法的电力系统低压减载优化方法研究

低压减载是维护电力系统电压稳定的一道重要防线。最优减载问题是离散变量(地点和轮次)和连续变量(各轮的控制量)的混合优化问题。提出了一种利用多种群分层粒子群优化算法(HSPPSO)求解最优减载问题的新思路,采用受动态电压约束的系统最优潮流所建立的优化模型,以所切负荷总量最小为目标函数。最后以IEEE-39节点作为算例,结果与标准粒子群算法(PSO)和遗传算法(GA)对比,得出在同样的稳定裕度和迭代次数要求下,HSPP-SO算法总能够得到最小的减载量,表现出了在求解UVLS问题时的良好适应性。

Research on Optimal Undervoltage Load Shedding of Power System Based on Hierarchical Structure Poly-particle Swarm Optimization

Undervoltage load shedding(UVLS) is an important measure for maintaining the voltage stability of power system.Optimal load shedding is a problem mixed with discrete variables and continuous variables.A new idea using hierarchical structure poly-particle swarm optimization(HSPPSO) to solve the optimal UVLS is presented.Simultaneously,the concept of dynamic security-constrained optimal power flow(OPF) is employed to develop a model for optimal UVLS,which takes the minimum quantity of load shedding as the ojective function.Finally,taking IEEE 39-bus system for example,the results of HSPPSO are compared with the results obtained by other two evolutionary approaches like particle swarm optimization(PSO) and genetic algorithm(GA).It can obtained that HSPPSO can get the minimum quantity of load shedding under the same stability margin and interation numbers,which has shown the good flexibility in solving UVLS problems.