基于改进粒子群算法的微网优化运行

微网作为分布式电源并网的一种有效途径,其优化运行成为研究的重要课题之一。在多方利益的权衡下,考虑了经济成本、环境成本、网损和节点电压偏差等多个运行指标对微网的优化运行进行建模;引入精英反向学习策略和最劣粒子排斥法对粒子群算法(particleswarmoptimization,PSO)进行改进,并将其用来解决多目标多约束的微网优化运行问题,在搜索过程中对当前最优粒子进行混沌扰动,以加强局部探索能力,提高粒子跳出局部最优解的能力。在相同的条件下,分别用改进前后的算法求解所建立的微网优化运行模型,优化结果验证了改进后算法的优越性。

Optimal Operation of Microgrid Based on Improved Particle Swarm Optimization Algorithm

Microgrid is an effective way to integrate the distributed generations, and the optimal operation of microgrid has become one of the important topics in the research of microgrid. The optimal operation of microgrid is modelled with a consideration of multiple operation indicators such as the economic cost, environmental cost, network loss and node voltage fluctuation, and a balance of the interests of various stakeholders. The elite reverse learning strategy and the worst particle exclusion method are introduced into particle swarm optimization algorithm (Particle Swarm Optimization, PSO) to solve the multi-objective and multi-constraint optimal operation problems of microgrid. In the process of searching, chaotic disturbance is made on the existing optimal particle to enhance the local searching ability, and to improve the ability of particle to jump out of local optimal solution. Under same conditions, the optimal operation model of the microgrid is solved using the original algorithm and the improved algorithm respectively, and the superiority of the improved algorithm is verified by comparing the solution results.