基于自适应变异粒子群的风光储微网调度  

Scheduling of the wind-solar-storage microgrid based on the adaptive mutation particle swarm

作　　者：张宁[1,2] 李季[1] ZHANG Ning;LI Ji(Tianjin Key Laboratory for Control Theory&Application in Complicated Systems,Tianjin University of Technology,Tianjin 300384,China;School of Electronic Engineering and Automation,Tianjin University of Technology,Tianjin 300384,China)

机构地区：[1]天津理工大学天津市复杂系统控制理论及应用重点实验室,天津300384 [2]天津理工大学电气工程与自动化学院,天津300384

出　　处：《天津理工大学学报》2024年第2期77-83,共7页Journal of Tianjin University of Technology

基　　金：天津市自然科学项目(17JCTPJC53100)。

摘　　要：为克服传统粒子群算法(particle swarm optimization,PSO)在求解时容易形成局部最优,求解精度低的不足,提出了一种基于自适应变异粒子群优化(adaptive mutation particle swarm optimization,AMPSO)的微电网调度求解方法。AMPSO惯性权重采用自适应正态分布递减,随着迭代次数的增加更新粒子位置的移动策略,并且在算法后期引入变异环节。为验证算法的有效性,该算法与其他改进算法进行收敛性能对比,并对4种典型天气情况下的微网运行成本模型仿真求解,得到最优调度。算例仿真结果表明,AMPSO能够对粒子全局最优搜索优化,在解决微网经济性运行问题上效果优于其他算法,可合理调配各微电源出力时段,具有良好的灵活性和可行性。In order to overcome the shortcomings of the traditional particle swarm optimization(PSO),which is easy to form local optimum and has low solving accuracy,a method of microgrid scheduling based on adaptive mutation particle swarm optimization(AMPSO)was proposed.The inertia weight of AMPSO is decreased by an adaptive normal distribution,and the movement strategy of the particle position is updated with the increase of the number of iterations,and the mutation link is introduced in the late stage of the algorithm.This paper compares the convergence performance with other improved algorithms,and solves the operating cost model of microgrid under four typical weather conditions by simulation,and obtains the optimal scheduling.The results of calculation examples show that AMPSO can search and optimize the global optimal particle,and is better than other algorithms in solving the economic operation problems of microgrid.It can reasonably allocate the output period of distributed power supply,and has a good feasibility and flexibility.

关 键 词：微电网 调度 粒子群算法 自适应 变异 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]