Study On Smart Home Energy Scheduling By Considering Incentive Mechanism

Based on the background of the rapid development of city's energy internet,the smart home energy management system,which responds to the demand side,provides an optimal smart power consumption strategy for users.Through the real-time monitoring of this system for home appliances,it can optimally arrange the operation and energy consumption of the home appliance and achieve the demand response to the power grid;moreover,it also can minimize the power cost,maximize the self-interest and reduce the peak-to-average ratio(PAR).Hence,it can conduct to the stable operation of the power grid.In this paper,the dynamic fuzzy neural network(DFNN)theory is used to predict the electricity price.Then,a mathematical model of photovoltaic power(PV)generation is established by considering the photovoltaic power generation accessing smart home,the household electrical appliances are classified and modeled according to the working characteristics and energy consumption to study the relationship between the subsidy and the time-of-use price in detail.The function of optimal load controls aiming at maximizing household income are constructed on the basis of the different operation modes of the photovoltaic power generation system.The household income and load balance are analyzed based on decision variable of controllable load and price prediction.Finally,taking into account the effect of demand response on electricity consumption behavior of users,an incentive mechanism combining price incentive with demand side response is proposed based on maximum optimizing the foremention function.Customers should be further encouraged to save energy,increasing photovoltaic power consumption by change the energy consumption patterns.Compared with traditional smart home energy scheduling further reduce the peak-to-average ratio(PAR)in load demand.By introducing several auxiliary variables and linear variation techmiques,the fore-mentioned function is transformed into a lixed integer linear programming(MILP)model to solve the research problem of this paper.Simulation results are used to verify the effectiveness and practicability of the proposed energy optimization scheduling method.Study results can serve as a guidance which can effectively help participate in power grid interaction and provide guidance to use intelligent electricity.