Demand-Side-Management Measures Of Power Market Based On User Characteristics

With the development of power market reform,China’s energy structure and power market management mode are undergoing tremendous changes.Under the pressure of low energy consumption index,high coal price,limited net capacity of power grid connection line and other factors,there are some areas in China where the supply and demand situation of power grid is tight and there is a short time supply gap.In this context,demand-side management measures for power users’ behavior become effective measures to relieve the power supply pressure of the power system and ensure safe and reliable use of electricity by users.Among them,demand response and TOU,as important measures of demand side management,play an important role in the power market.However,because different users have different response degrees to TOU price,accurate modeling of demand side response behavior can give full play to the advantages of demand response and TOU price,improve energy utilization,and promote the reform and development of electricity market.Aiming at optimizing load curve and reducing user side electricity cost,this paper studies demand side management measures,and the specific work is described as follows:(1)To reflect the characteristics of power consumption on the demand side,the elastic matrix of power users is constructed.Users are divided into different types according to their power consumption characteristics,power consumption behavior of different types of users is analyzed,the self-elastic coefficient and mutual elastic coefficient of different types of users are calculated,and the demand response analysis model of different users is established.(2)In view of the problem of power consumption period division,the fuzzy membership function is used to deal with it,and the power consumption period is divided into peak-flatvalley three periods by fuzzy clustering method.Then,considering the actual situation of users’ electricity consumption,constraints are established on electricity price,power flow of the power system,revenue of the power selling side,demand side electricity cost,etc.,and objective functions are established on peak-valley difference of load curve and demand side electricity comfort.(3)In order to verify the role of the model in demand side management,the classical daily load data of a certain place is used for example verification.Genetic algorithm was used to optimize the single objective of the example,and the advantages and disadvantages of different time division methods were compared and verified,and a more appropriate division method was selected.The fast Non-dominated sorting genetic algorithm with elite strategy was used to solve the multi-objective function.Pareto optimal solution was found in the conflicting objective function,and the optimization effect of single objective optimization and multiobjective optimization on demand side management was compared.