计及用户不满意度的家庭能量管理调度优化研究
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摘要
针对家庭能量管理调度优化问题,本文建立了计及用户不满意度的家庭能量管理调度优化模型,综合考虑用户用电成本、用户对用电时间的不满意度和对温度的不满意度3个目标,采用多目标粒子群算法(multi-objective particle swarm optimization,MOPSO)对模型进行求解,并结合信息熵法对最优解进行选取,从而完成对各家用电器的优化调度。同时,对不同情景下的用电模式进行算例仿真。仿真结果表明,采用计及用户不满意度的家庭能量管理调度优化模型对家用电器进行调度,可以在降低用户用电成本的同时,兼顾用户用电的舒适度,验证了模型的有效性。该研究对智能家居的调度和用户的智能用电具有重要的理论和指导意义。
With the continuous penetration of distributed power on the user side and the increasing popularity of smart household appliances,the optimization of household energy management and scheduling has become a hot research topic in electric power system.In this paper,a home energy management scheduling optimization model based on user dissatisfaction is established,which considers three objectives:the cost of electricity consumption,user′s dissatisfaction with electricity consumption time and temperature.The Multi-Objective Particle Swarm Optimization is used to solve the model and the information entropy method is used to select the optimal solution to complete the optimal scheduling of each household appliance.Finally,the power consumption patterns in different scenarios are simulated.The simulation results show that the optimal model of household energy management scheduling taking into account customer dissatisfaction can reduce the cost of electricity consumption and give consideration to the comfort of the users.The validity of the model is verified.This research has important theoretical and guiding significance for intelligent home scheduling and users′intelligent electricity utilization.
With the continuous penetration of distributed power on the user side and the increasing popularity of smart household appliances,the optimization of household energy management and scheduling has become a hot research topic in electric power system.In this paper,a home energy management scheduling optimization model based on user dissatisfaction is established,which considers three objectives:the cost of electricity consumption,user′s dissatisfaction with electricity consumption time and temperature.The Multi-Objective Particle Swarm Optimization is used to solve the model and the information entropy method is used to select the optimal solution to complete the optimal scheduling of each household appliance.Finally,the power consumption patterns in different scenarios are simulated.The simulation results show that the optimal model of household energy management scheduling taking into account customer dissatisfaction can reduce the cost of electricity consumption and give consideration to the comfort of the users.The validity of the model is verified.This research has important theoretical and guiding significance for intelligent home scheduling and users′intelligent electricity utilization.
引文
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[13]Fanger P O.Thermal comfort[M].Copenhagen:Danish Technical Press,1970.
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[15]张子泳,仉梦林,李莎,等.基于多目标粒子群算法的电力系统环境经济调度研究[J].电力系统保护与控制,2017,45(10):1-10.
[16]王金全,黄丽,杨毅,等.基于多目标粒子群算法的微电网优化调度[J].电网与清洁能源,2014,30(1):49-54.
[17]吴颖.基于Pareto最优解的多目标PSO算法在电机优化设计中的应用[D].广州:中山大学,2009.
[18]吴小刚.配网储能系统的规划方法研究[D].北京:华北电力大学,2015.
[19]Erolkantarci M,Mouftah H T.Wireless sensor networks for cost-efficientresidential energy management in the smart grid[J].IEEE Transactions on Smart Grid,2011,2(2):314-325.
[20]纪姝彦.面向智能用电的家庭能量管理研究[D].合肥:合肥工业大学,2017.
[2]刘经浩,贺蓉,李仁发,等.一种基于实时电价的HEMS家电最优调度方法[J].计算机应用研究,2015,32(1):132-137,160.
[3]殷亮亮,李征.基于负荷分类的风光蓄家庭微电网的独立运行能量优化管理[J].电器与能效管理技术,2017(3):84-90.
[4]王守相,孙智卿,刘喆,等.面向智能用电的家庭能量协同调度策略[J].电力系统自动化,2015,39(17):108-113.
[5]官拓颖,贺蓉,李仁发,等.一种改进的HEMS家电最优调度方法[J].计算机应用研究,2016,33(6):1727-1733.
[6]黄焘,马溪原,雷金勇,等.考虑分时电价和需求响应的家庭型用户侧微电网优化运行[J].南方电网技术,2015,9(4):47-53.
[7]杨冰.基于需求响应的家用设备调度策略研究[D].成都:电子科技大学,2016.
[8]纪姝彦,李冬伟.基于用户满意度的智能用电能量调度方法研究[J].电气工程学报,2017,12(4):10-17.
[9]郭佳欢.微网经济运行优化的研究[D].北京:华北电力大学,2010.
[10]Mohsenian Rad A H,Leon Garcia A.Optimal residential load control with price prediction in real-time electricity pricing environments[J].IEEE Transactions on Smart Grid,2010,1(2):120-133.
[11]阮冰洁.计及实时电价的家居混合供电系统能量优化调度方法[D].杭州:浙江大学,2015.
[12]Li N,Chen L I,Low S H.Optimal demand response based on utility maximization in power networks[J].IEEE Transactions on Smart Grid,2011,1(3):67-78.
[13]Fanger P O.Thermal comfort[M].Copenhagen:Danish Technical Press,1970.
[14]苏华东.CFD技术在住宅室内设计节能控制中的应用研究[D].重庆:重庆大学,2010.
[15]张子泳,仉梦林,李莎,等.基于多目标粒子群算法的电力系统环境经济调度研究[J].电力系统保护与控制,2017,45(10):1-10.
[16]王金全,黄丽,杨毅,等.基于多目标粒子群算法的微电网优化调度[J].电网与清洁能源,2014,30(1):49-54.
[17]吴颖.基于Pareto最优解的多目标PSO算法在电机优化设计中的应用[D].广州:中山大学,2009.
[18]吴小刚.配网储能系统的规划方法研究[D].北京:华北电力大学,2015.
[19]Erolkantarci M,Mouftah H T.Wireless sensor networks for cost-efficientresidential energy management in the smart grid[J].IEEE Transactions on Smart Grid,2011,2(2):314-325.
[20]纪姝彦.面向智能用电的家庭能量管理研究[D].合肥:合肥工业大学,2017.