基于改进MOEA/D算法的含可再生能源系统协同优化调度  

Collaborative optimal scheduling of renewable energy systems based on improved MOEA/D algorithm

作　　者：赵新刚[1,2] 王桢 ZHAO Xin-gang;WANG Zhen(School of Economics and Management,North China Electric Power University,Beijing 102206,China;Beijing Key Laboratory of New Energy and Low-Carbon Development,Beijing 102206,China)

机构地区：[1]华北电力大学经济与管理学院,北京102206 [2]新能源电力与低碳发展研究北京市重点实验室,北京102206

出　　处：《吉林大学学报（工学版）》2024年第4期1129-1135,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(71273088);中央高校基本科研业务费专项资金项目(2020YJ008)。

摘　　要：针对含可再生能源系统协同优化调度方法存在SOC值控制约束能力差、调度能耗高的缺点,提出基于改进MOEA/D算法的含可再生能源系统协同优化调度方法。首先构建含可再生能源系统中关键发电模型,分别为光伏阵列模型、风力发电模型以及储能蓄电池模型,然后以能耗最低为目标,构建出系统的协同优化调度函数,得出电功率及热功率平衡的函数约束条件,最后利用改进MOEA/D算法求解出函数最优解,实现含可再生能源系统协同优化调度。实验结果表明,本文方法的SOC值控制约束能力好,SOC值控制结果最接近理想状态,在环保性最优准则下系统调度能耗低,24 h能耗功率为0.6 kW。Microgrid with renewable energy system has high economy and environmental protection,and requires system load to be flexible and controllable,that is,resources can be coordinated for scheduling operation.Therefore,a collaborative optimal scheduling method for renewable energy system based on improved MOEA/D algorithm is proposed.Build key power generation models in renewable energy systems,including photovoltaic array model,wind power generation model and energy storage battery model.With the lowest energy consumption as the goal,build the system′s collaborative optimal scheduling function,obtain the functional constraints of electric power and thermal power balance,and use the improved MOEA/D algorithm to solve the optimal solution of the function to achieve the collaborative optimal scheduling of renewable energy systems.The experimental results show that the proposed method has good SOC value control constraint ability,and the SOC value control result is the closest to the ideal state.Under the environmental optimization criterion,the system scheduling energy consumption is low,and the 24-hour energy consumption power is 0.6 kW.

关 键 词：MOEA/D算法 光伏发电 含可再生能源系统 协同优化调度 电力能耗 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]