Research On Electricity Scheduling Strategy For Home Energy Management System

The whole society is becoming increasingly concern about energy crisis and greenhouse gas emissions.The residential sector is expected to play a key role in the balance between energy and environment,since it is currently one of the major contributors to countries' energy consumption.Demand response(DR)is being considered an important measure to solve the energy crisis from the residential sector.In recent years,smart grid provides a strong technical support for the implementation of DR,with the integration of smart metering techniques,intelligent control systems and advanced communication technology.The home energy management system(HEMS),which integrates the functions of data analysis and information management on the advanced metering architecture,can monitor the energy consumption in the users' houses and optimize the operation of the various types of electrical equipments according to DR signal and electricity price,and eventually reduce the cost of energy and improve the efficiency of energy consumption in the premise of meeting the users' power demands.A lot of works have been done with the the hardware platform of HEMS.Still there are some challenges to be tackled on the aspect of the scheduling strategy.In this paper,a comprehensive electricity scheduling strategy for HEMSs was proposed,and the main contents are as follows:Firstly,the overall structure of HEMS was designed.Three common electrical equipments in the residential sector were included in the system: the basic appliances,solar photovoltaic panels and batteries.The system consisted of communication,monitoring,prediction,scheduling and other functional modules.The scheduling module is the core of the whole system and also the center part of this paper.Then,considering that the energy management is closely related to the change of electricity price,and the accurate prediction of electricity price is a necessary condition for the optimization of HEMS,a short-term electricity price forecasting model based on BP neural network and Markov chain was proposed.The short-term electricity price was firstly predicted by BP neural network model,and then the prediction bias was modified based on Markov chain.The accuracy of the proposed method was eventually verified by a numerical simulation.Finally,a electricity scheduling strategy for HEMS was proposed.Based on the mathematical model of the schedulable equipment and energy storage equipment in the system,three scheduling model——cost-effective model,cost-effective & comfort-aware model and cost-effective & privacy-friendly model were proposed considering the diversity of users' needs.A mixed-coding genetic algorithms was then designed to solve the models.In the case study,the effectiveness of the proposed strategy was verified.