基于模型预测控制的家庭能量管理优化调度方法研究  

Optimized dispatching strategy of home energy management system based on model predictive control

作　　者：刘旭菲 彭丽莎[1] 黄松岭[1] LIU Xufei;PENG Lisha;HUANG Songling(State Key of Power Systems,Department of Electrical Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]清华大学电机系电力系统国家重点实验室,北京100084

出　　处：《电测与仪表》2024年第10期26-32,共7页Electrical Measurement & Instrumentation

基　　金：国家工信部工业互联网创新发展工程项目(TC200802F)。

摘　　要：在分布式可再生能源大规模接入和分时电价实施的背景下,为降低电费成本、提高用户舒适度并提高可再生能源消纳率,提出了一种基于模型预测控制的家庭能量管理策略。建立由分布式光伏和各类用电负载等组成的家庭能量系统,分析各类设备的工作特性,提出相应的舒适度评价指标,特别针对空调这一典型功率可变负荷,结合建筑的热动态特性,建立室内温度预测模型。在建立家庭能量系统的基础上,使用遗传算法进行优化管理,并在模型预测控制框架下不断执行和更新。最后,实验对比结果表明,文中提出的基于模型预测控制的家庭能量管理策略可以有效实现能量的优化调度,并在预测不确定场景下具有较强鲁棒性。In the context of distributed renewable energy access and implementation of time-of-use tariff,a home energy management strategy based on model predictive controlis proposed to reduce electricity cost,improve user comfort and increase renewable energy consumption.This paper establishes a home energy system consisting of distributed photovoltaic and various types of electric loads,analyzes working characteristics of various devices and puts forward corresponding comfort evolutionindices.For the typical power variable load of air conditioner,an indoor temperature prediction model is established by combining the thermal dynamic characteristics of the building.Based on the architecture of the home energy system,the optimal management is carried out using genetic algorithms,which are continuously implemented and updated under the model prediction control framework.The experimental comparison results show that the home energy management strategy based on model predictive control proposed in this paper can effectively achieve the optimal scheduling of energy and has strong robustness under the prediction uncertainty environment.

关 键 词：家庭能量管理系统 模型预测控制 遗传算法 预测不确定性 舒适度 

分 类 号：TM73[电气工程—电力系统及自动化]