光储充电站多目标自适应能量调度策略  

Multi-objective Adaptive Energy Scheduling Strategy for PV-storage Charging Station

作　　者：林兴源 林兵 卢宇 陈星 吴圣[1] LIN Xingyuan;LIN Bing;LU Yu;CHEN Xing;WU Sheng(College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China;School of Electronics Engineering and Computer Science,Peking University,Beijing 100871,China;Key Laboratory of Network Computing and Intelligent Information Processing in Fujian Province,Fuzhou 350116,China;College of Computer and Data Science/College of Software,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福建师范大学物理与能源学院,福州350117 [2]北京大学信息科学技术学院,北京100871 [3]福建省网络计算与智能信息处理重点实验室,福州350116 [4]福州大学计算机与大数据学院/软件学院,福州350108

出　　处：《小型微型计算机系统》2024年第7期1663-1670,共8页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(62072108)资助;福建省高校产学合作项目(2022H6024,2021H6026)资助.

摘　　要：针对光储充电站运营中利润过低、充电桩降功率供电易发和光伏弃光严重的问题,提出了一种基于参考点的非支配排序自适应遗传算法的能量调度策略,目的是提高充电站的运营效益.首先,以最大化充电站利润、充电桩满足率和光伏消纳度为目标建立多目标优化模型;其次,为避免传统NSGA-Ⅲ算法早熟问题,构建了一种自适应的二进制变异算子,以提高其种群多样性;最后,针对多目标优化求解所得的Pareto最优解集难以筛选问题,采用模糊层次分析法从中选出唯一最优解.实验结果表明,该策略在满足100%光伏消纳度的同时,相比其他多目标优化策略提高了0.45%~9.55%的充电站利润和0.42%~5.64%的充电桩满足率.另外,超体积指标表明所改进的算法有更好的收敛性和分布性.An energy scheduling strategy based on a non-dominated ranking adaptive genetic algorithm with reference points is proposed to address the problems of low profit,charging pile down power supply susceptibility and serious PV abandonment in the operation of PV-storage charging station,with the aim of improving the operational efficiency of charging stations.Firstly,a multi-objective optimization model is established with the objectives of maximizing charging station profit,charging pile satisfaction rate and PV consumption degree;secondly,an adaptive binary variation operator is constructed to improve its population diversity in order to avoid the premature maturity problem of the traditional NSGA-Ⅲ algorithm;finally,for the problem that the Pareto optimal solution set obtained from the multi-objective optimization solution is difficult to be filtered,fuzzy hierarchical analysis is used Finally,to address the problem that the Pareto optimal solution set obtained from multi-objective optimization is difficult to be selected,fuzzy hierarchical analysis is used to select the unique optimal solution.The experimental results show that this strategy improves the profit of charging stations by 0.45%~9.55% and the satisfaction rate of charging piles by 0.42%~5.64% compared with other multi-objective optimization strategies while satisfying 100% PV consumption degree.In addition,the supervolume metrics indicate better convergence and distributivity of the improved algorithm.

关 键 词：光储充电站 非支配排序 多目标优化 自适应 能量调度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]