Study Of Optimal Control Of Intelligent Electricity Consumption For Residential Customers Based On Time-of-use Price

With the rapid socio-economic development,the power demand of the whole society is continuously increasing.Extreme weather conditions always lead to widening peak-valley load differences.The proportion of renewable energy generation such as wind and solar is increasing,and its strong randomness,poor controllability,unpredictability characteristics make the power system need more operating reserve.The development of information communication technology and the expansion of intelligent electrical equipment make it possible for users to assist power system balance by the power side and participate in demand response(DR),which is one of the important ways to improve the reliability of power system and the level of renewable energy consumption.The price mechanism of DR uses pricing information to guide users to adjust their electrical activity.Considering the opening degree of China's power market,the real-time pricing(RTP)is difficult to implement.Therefore,it has more practical value to carry out DR project based on the time of use pricing(TOU)at the present.Based on the background,this paper is devoted to the study of the TOU decision-making mechanism basing on the load response characteristics and the optimal control of intelligent electricity consumption for residential customers under TOU mechanism,to reduce the peak-valley differences for power system and reduce electricity cost for residential customers.Firstly,this paper introduces the concept,classification,domestic and foreign practical experience and theoretical research status of DR,and the research status of home energy management control strategy.By analyzing the user's load characteristics,build the models of different types of smart home appliances and explore the user's load regulation mode.Then,based on the theory of demand-price elasticity,the elasticity effect weight is put forward to propose an improved electricity-price elasticity matrix model.Then the load response degree model is established.In view of the existing shortcomings of the current mid-and long-term peak-valley TOU in China,this paper proposes a multi-time day-ahead peak/valley TOU mechanism and establishes an optimal decision model of TOU.The effect of the TOU model on filling peak and lowering peak-valley differences is verified by the simulation analysis.Through quantitative analysis of the influence of load response characteristics on the TOU decision model,some recommendations for TOU decision can be submitted to the power grid corporation or the demand response actuators.Finally,based on the load models of smart home appliances,the home energy management model of residential customers is established.Under the day-ahead peak/valley TOU mechanism and the real-time incentive signals,considering the dual purpose of reducing user's electricity cost and improving the comfort of electricity consumption,this paper develops the optimal control decision of intelligent electricity consumption for residential customers to participate in DR projects.The simulation shows that the optimal control strategy can effectively save electricity cost and reduce the peak load while ensuring the satisfaction of electricity consumption.It also verifies that the residents can realize the rapid load reduction under the incentive signals to provide emergency operating reserve for power system.Through quantitative analysis of the influence of user's satisfaction requirements on intelligent electricity consumption optimization strategy,some recommendations for the establishment of response subsidy standard can be submitted to the power grid corporation or the demand response actuators.