基于量子粒子群算法的分布式电源优化规划

Distributed Generation Optimization Planning Based on Quantum Particle Swarm Algorithm

针对配电网中分布式电源(distributed generation,DG)的选址和定容问题,在研究标准粒子群优化算法的基础上建立有功网损和DG运行费用最小的目标函数。考虑运行中的约束条件,利用旋转门更新的量子粒子群算法(quantum particle swarm optimization,QPSO)分析DG接入位置、容量不确定的情况下将目标函数和约束条件转换为综合目标函数,并求得最优解。对IEEE14节点配电测试系统进行算例仿真,比较仿真结果与粒子群算法优化结果,验证了QPSO在分布式电源规划上的收敛性和适应性。

Aiming at problems of site selection and constant volume of distributed generation in power distribution network, this paper discusses establishment of minimum objective functions of power loss and DG running cost on the basis of studying standard particle swarm optimization algorithm.Considering constraint condition in operation and using updated quantum particle swarm optimization algorithm of rotation gate,this paper analyzes transformation of objective functions and con-straint condition into generalized objective functions under conditions of uncertain DG connecting position and capacities and acquires optimal solutions.Example simulation on IEEE 1 4 node power distribution testing system was conducted and simula-tion result was compared with result of particle swarm optimization algorithm.Astringency and adaptability of QPSO in DG planning was verified.