An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II  

作　　者：Lei Xing Yakui Liu 

机构地区：[1]Yinchuan University of Energy,Wangtaibao,Yinchuan,750100,China [2]Qingdao University of Technology,Qingdao,266520,China [3]State Key Laboratory of Electrical Insulation and Power Equipment,Xi’an Jiaotong University,Xi’an,710049,China

出　　处：《Energy Engineering》2023年第4期1023-1043,共21页能源工程（英文）

基　　金：supported in part by the Natural Science Foundation of Shandong Province(ZR2021QE289);in part by State Key Laboratory of Electrical Insulation and Power Equipment(EIPE22201).

摘　　要：The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.

关 键 词：Multi-objective optimization wind/photovoltaic/hydrogen power system particle swarm algorithm non-dominated sorting genetic algorithms-II 

分 类 号：TM73[电气工程—电力系统及自动化]