基于NSGA-Ⅱ改进GSO算法的并网型微电网多目标优化调度研究  

Research on Multi-objective Optimal Dispatch of Grid-connected Microgrid Based on NSGA-ⅡImproved GSO Algorithm

作　　者：黄洪斌 蔡乐才[3] 肖体刚 叶羽泠 高祥 HUANG Hongbin;CAI Lecai;XIAO Tigang;YE Yuling;GAO Xiang(School of Automation and Information Engineering,Sichuan University of Science&Engineering,Zigong 643000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Zigong 643000,China;Yibin University,Yibin 644000,China)

机构地区：[1]四川轻化工大学自动化与信息工程学院,四川自贡643000 [2]人工智能四川省重点实验室,四川自贡643000 [3]宜宾学院,四川宜宾644000

出　　处：《四川轻化工大学学报（自然科学版）》2020年第6期24-31,共8页Journal of Sichuan University of Science & Engineering(Natural Science Edition)

基　　金：四川省科技厅资助项目(2019YFN0104);四川轻化工大学研究生创新基金(Y201915)。

摘　　要：并网微电网的优化调度是微电网在并入大电网的前提下,对微电网优化协调控制,使整个系统的能源利用率达到最大。为解决传统智能算法在优化调度的过程中容易陷入局部最优、收敛速度慢及准确性差等问题,使用NSGA-Ⅱ算法对传统的萤火虫算法(GSO)进行改进,改进后的算法可以较快地跳出局部最优,并提高算法的全局搜索能力。以微电网系统运行成本最低和环境污染最小为优化目标,建立优化调度模型,最后通过MATLAB仿真,检验了算法可以较快地跳出局部最优并有效降低运行成本。改进后的GSO算法与NSGA-Ⅱ算法、GSO算法及PSO算法3种算法进行比较,运行总成本分别减少6.5%,4.4%,3.5%。迭代次数与运行时间的减少表明改进后的GSO算法可以更快地收敛于最优位置,并有效地减少系统运行成本。The optimized dispatching of grid-connected microgrid is to optimize and coordinate the control of the microgrid under the premise that the microgrid is integrated into the large grid,so as to maximize the energy utilization rate of the entire system.In order to solve the problems of traditional intelligent algorithms that are easy to fall into local optimality,slow convergence speed and poor accuracy in the process of optimizing scheduling,the NSGA-Ⅱalgorithm is used to improve the traditional firefly algorithm(GSO).The improved algorithm can be faster Jump out of the local optimum and improve the global search ability of the algorithm.Taking the lowest operating cost of the microgrid system and the smallest environmental pollution as the optimization goals,an optimal dispatch model is established,and finally,through MATLAB simulation,it is verified that the algorithm can quickly jump out of the local optimal and effectively reduce the operating cost.Comparing the improved GSO algorithm with the three algorithms of NSGA-Ⅱ,GSO and PSO,the total operating cost is reduced by 6.5%,4.4%,and 3.5%respectively.The reduction in the number of iterations and running time shows that the improved GSO algorithm can converge to the optimal position faster and effectively reduce the operating cost of the system.

关 键 词：微电网 NSGA-Ⅱ算法 GSO算法 并网 多目标优化调度 

分 类 号：TM714[电气工程—电力系统及自动化]