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家庭微网储能分组能量管理优化策略
引用本文:胡陈壮.家庭微网储能分组能量管理优化策略[J].电子测试,2021(7):46-49,19.
作者姓名:胡陈壮
作者单位:上海电力大学电子信息与工程学院,上海,200090
基金项目:国家自然科学基金资助项目(61401269,61572311);上海市科技创新行动计划地方院校能力建设项目(17020500900)。
摘    要:在家庭能量管理系统中,可再生能源的发电功率具有不确定性和间断性,成为影响家庭能量优化调度的因素。储能系统在优化过程中过多充放电次数也会增加储能折旧费用。针对上述问题,文中提出一种储能分组能量管理优化策略。根据可再生能源出力不确定部分和确定部分为储能系统配置充电部分和调度部分。首先建立风力发电系统、光伏发电系统和储能系统模型,然后在此基础上搭建以每日用电费用最小为目标的家庭能量管理优化调度模型。最后以上海市一住宅用电为例,通过改进遗传算法对模型求解。仿真算例分析表明所提策略降低用电费用的同时可以减小可再生能源发电不确定性对能量优化调度的影响,具有一定的有效性和参考价值。

关 键 词:家庭能量管理  优化调度  储能分组  遗传算法

Energy management optimization strategy of grouped energy storage system in household microgrid
Hu Chenzhuang.Energy management optimization strategy of grouped energy storage system in household microgrid[J].Electronic Test,2021(7):46-49,19.
Authors:Hu Chenzhuang
Affiliation:(School of Electrical Information and Engineering,Shanghai University of Electrie Power,Shanghai,200090)
Abstract:In the home energy management system,the generation power of renewable energy has uncertainty and discontinuity,which has become a factor affecting the optimal scheduling of household energy.Excessive charging and discharging times in the optimization process of energy storage system will also increase the depreciation cost of energy storage.To solve the above problems,this paper proposes an energy management optimization strategy of energy storage group.According to the uncertain part and determined part of renewable energy output,the charging part and dispatching part are configured for the energy storage system.Firstly,the models of wind power generation system,photovoltaic power generation system and energy storage system are established,and then the optimal scheduling model of household energy management with the objective of minimizing daily electricity cost is built.Finally,taking a residential power consumption in Shanghai as an example,the improved genetic algorithm is used to solve the model.Simulation results show that the proposed strategy can reduce the impact of renewable energy generation uncertainty on energy optimal scheduling while reducing the power consumption cost,and has certain effectiveness and reference value.
Keywords:Home energy management  Optimal scheduling  Energy storage grouping  Genetic algorithm
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