Pricing Strategy Design And Optimization For Demand Response In Smart Grid

With the advancement of electric power marketing progress,the demand response program has been widely researched by academics for its fast response,low cost,flexible scheduling.Demand response as a kind of auxiliary service plays an important role for the stable operation of power grid,the optimization of electric power market and sensible allocation of resources in the electric power market.The pricing strategy as the basic ways of realization of demand response,which can effectively affect the distribution of the interests between the consumers and all parties involved in the electric power market.In this paper,the pricing strategy design and optimization are studied between the consumers’ demand response and utility company based on the game theory.It aims to improve the efficiency of the electricity market.The main research results of this paper can be summarized as follows:Firstly,a pricing strategy for purchasing the consumers’ regulation capacity is put forward.The cost function of one generator set participating in the regulation service is formulated according to the practical operation of one generator set.Then the cost function of multiple generator sets participating in ancillary service is established.The cost function of the consumers involved in regulation service is formulated according to the consumers’ response function to the load adjustment.Furthermore,the total cost of the utility company involved in regulation service is established according to the cost function of the generation-side and demand-side.The optimal value is searched by interval division and one dimensional search method.Secondly,a supermodular game is established among multiple utility companies.The utility companies set the appropriate retail price to maximize their own profits respectively.When the game of utility company has multiple Nash equilibrium price,there must be a maximum retail price and a minimum retail price.The maximum retail price is an accumulation point equilibrium,the utility company tends to choose this price to maximize its profit.Then a distributed algorithm is proposed to search for the optimal retail price.Meanwhile,a cost model for the consumers with heating,ventilation,and air conditions participating in the demand response is established to keep the electricity real-time balance.The simulation results verify the validity of the model.Thirdly,with the consideration of the existence of the load forecast errors in the electricity supply chain,the revenue model of the utility company is established.there is an electricity supply chain composed of one generation company,multiple consumers and competing utility companies.A non-cooperative game among multiple utility companies is formulated and derive the sufficient condition on the uniqueness of the Nash equilibrium.Hence,an iterative algorithm is provided to determine the Nash equilibrium.The multiple utility companies select the preferred contractual terms offered by the generation company to maximize their profits and coordinate the electricity supply chain.Then it can achieve optimum social welfare.Finally,an economic operation strategy with demand response is presented in the electricity market.First of all,based on the consumer’s utility function,the revenue function of the utility company is established.Furthermore,a wholesale price negotiation problem between the multiple utility companies and the generation company is established.Then,the wholesale price negotiation is proved to be a bargaining problem and RBS solution is utilized to achieve the optimal solution.In the electricity retail market,a price regulation model is established based on the consumers’ utility maximization and bargaining outcome.The iterative algorithm is used to search for the optimal retail price and the power consumption.Moreover,the power management system is input-to-state stability under additive electricity measurement and price disturbance and the stability condition is given.Simulation results demonstrate that the model has good robustness under different disturbances.