Study On Household Electric Load Scheduling Considering Demand Response

Power shortage,depletion of primary energy and environmental problems threaten the sustainable development of society.Countries around the world are searching methods to reduce emission and save energy.Demand response can effectively reduce equipment investment,fuel loss and pollutant emissions.The home energy management system is an important part of the smart grid.Its goal is usually to reduce energy consumption while providing users with comfortable and convenient services.Users can use the home energy management system to schedule household appliances.The core of the home energy management system is household electricity load dispatch.This article focuses on the study of household electricity load scheduling considering demand response:Firstly,the definition and classification of the demand response and the composition of the home energy management system are introduced in detail.Secondly,the factors affecting the scheduling algorithm of household electricity load are analyzed.Finally,an example of the demand response is used to describe the effect of the optimal scheduling strategy on economic benefit and relieving social electricity pressure.Considering the real-time pricing model used in this paper,most appliances operate at lower electricity prices,which may cause the rebound peak and affect the entire power system.This paper combines the real-time electricity price and inclining block rate mode,taking into account the influence of the delay time rate.Based on this combination pricing model,a household appliance optimization dispatching plan is proposed to solve the rebound peak problem.Social Learning PSO is used to solve the optimization problem.Analyze the impact of the optimal scheduling scheme on power cost and Peak-to-Average Ratio,compare the real-time power price with the inclining block rate pricing scheme and the real-time power price alone.The simulation results show that the proposed optimal scheduling scheme for household electrical equipment can effectively reduce power consumption and peaks.Compared to others,the stability of the entire power system is improved.Finally,day-ahead demand schedule algorithm of household electricity load is put forward in consideration of user's electricity habits,comfort,electric power,peak-to-average ratio and new energy.The purpose of the day-ahead demand schedule algorithm exports the day-ahead demand schedule.Firstly,an improved combinatorial optimization algorithm is used to obtain the equipment information and consumer preference and the representative demand schedule is obtained.Then use the flexibility coefficient and the dependency coefficient to increase the objective function constraint.The model is optimized by using CPLEX optimization software and finally the enhanced demand schedule is obtained.Then we use the data in Dutch Residential Energy Dataset and Reference Energy Disaggregation Dataset to verify day-ahead demand schedule algorithm.Dutch Residential Energy Dataset and Reference Energy Disaggregation Dataset can save up to 25%and 30%of the cost of electricity consumption per month,while lowering the peak-to-average ratio and ensuring user comfort.