摘要
解决微网中新能源出力的随机性与波动性是微电网优化运行的前提和关键,为此,提出一种快速非支配排序遗传算法NSGA-Ⅱ(non-dominated sorting genetic algorithm)和基本粒子群优化算法PSO(particle swarm optimization)相结合的NSGA-Ⅱ-PSO算法,考虑将经济运行成本和环境污染成本作为优化的目标函数,建立常见发电单元以及蓄电池储能的多目标优化运行模型。通过Matlab仿真对比PSO、NSGA-Ⅱ和NSGA-Ⅱ-PSO算法的适应度收敛曲线,验证所提算法具有收敛速度快、全局和局部搜索能力强的优点,较单一的PSO算法和NSGA-Ⅱ算法具有更优的特点;结合经典微网系统进行算例仿真,通过对单目标与多目标的分析,结果表明该算法能有效降低经济成本和使环境效益达到最优;并且进一步验证所提算法的优越性。
Since solving the randomness and fluctuation of new energy output in a microgrid is the prerequisite and key for its optimal operation,a non-dominated sorting genetic algorithm-particle swarm optimization(NSGA-Ⅱ-PSO)algorithm combining the fast NSGA-Ⅱalgorithm and the basic PSO algorithm is proposed.The economic operation cost and environmental pollution cost are considered as the optimization objective functions,and a multi-objective optimal operation model for the common power generation units and battery energy storage is established.Through MATLAB simulations,the fitness convergence curves of PSO,NSGA-Ⅱand NSGA-Ⅱ-PSO algorithms are compared,and it is verified that the proposed algorithm has advantages of fast convergence speed and strong global and local search capabilities.Compared with the single PSO and NSGA-Ⅱalgorithms,the novel algorithm has better characteristics.The simulations of an example are performed by combining a classic microgrid system.Through the analysis of single and multiple objectives,results show that this algorithm can effectively reduce the economic cost and optimize the environmental benefits.Moreover,its superiority is also verified.
作者
赵珍珍
王维庆
樊小朝
王海云
ZHAO Zhenzhen;WANG Weiqing;FAN Xiaochao;WANG Haiyun(School of Electrical Engineering,Xinjiang University,Urumqi 830047,China)
出处
《电源学报》
CSCD
北大核心
2023年第1期118-125,共8页
Journal of Power Supply
基金
国家自然科学基金资助项目(51667020,51666017)
新疆维吾尔自治区天山雪松计划资助项目(2017XS02)。
关键词
并网型微网
多目标优化运行
快速非支配排序遗传-粒子群优化算法
grid-connected microgrid
multi-objective optimal operation
fast non-dominated sorting genetic algorithm-particle swarm optimization(NSGAⅡ-PSO)algorithm
