Design Of Demand Side Pricing Mechanism For Smart Grid Based On Game Theory

With the continuous advancement of power system reform and the continuous development of power marketization,demand-side response,as a way to optimize the allocation of market resources,has attracted more and more attention from academia.Demand-side response strategy can stimulate users to control electricity consumption flexibly by adjusting electricity price,such as raising electricity price in peak period and lowering electricity price in trough period,so as to ensure stable operation of power system and improve economic benefit of power market.Demand-side response strategy can optimize the efficiency of utility companies and power users,and it is one of the main ways to optimize electricity market.Based on the non-cooperative game theory,hierarchical game theory and cooperative game theory in the economics field,this paper models and analyzes the utility companies and power users in the power market,and finds the optimal pricing strategy to provide guidance for utility companies and power users in power dispatching and power decision making.The main research contents of this paper are as follows:Firstly,the retail price decision of several competing utility companies is studied.Based on the income from selling electricity,the cost of purchasing electricity and the cost of electricity regulation,the mathematical model of the income of utility company is established.In the construction of the power adjustment cost model,the auxiliary regulating effect of the user is taken into account,and the optimal incentive price is found by the iterative algorithm.In addition,the author also considers the competitiveness between utility companies and the privacy of information,so the relationship between multiple utility companies is modeled as a non-cooperative Bayesian game model.Subsequently,the author study the Nash equilibrium solution of the Bayesian game and find the Nash equilibrium solution through the FP algorithm.Through simulation,it is verified that the Bayesian game method proposed in this paper can effectively solve the problem of incomplete information game between power companies,and can optimize the earnings of power companies through pricing.Secondly,the competition between utility companies and power users is studied.Based on the Bayesian game theory,a horizontal game model between power users is established to maximize the personal benefits of power users on the premise of information privacy.Based on the hierarchical game theory,a vertical game model between utility companies and users is established to maximize the benefits of utility companies and adjust the balance of power market.The price mechanism is designed according to the unmatching degree of power supply and demand.The regulation price is converged to the equilibrium point by an iterative algorithm.The simulation results verify that the proposed method can improve the benefits of utility companies and users and promote the matching of supply and demand in the power market by changing the pricing.Finally,the power consumption decision problem of power users is studied.Based on the Cartel mechanism,the users in the power market is modeled as cooperative game.The optimization problem of power users' income is set as a social welfare optimization problem,which improves the Pareto efficiency.The Powell algorithm is used to solve the multi-dimensional variable optimization problem,and the approximate optimal solution is found.Considering the possible selfish behaviors of users in the process of cooperation,a punishment mechanism is designed to avoid uncooperative behaviors and ensure the stable operation of cooperation.The simulation results show that the proposed cooperative strategy can optimize the power user's revenues and reduce the power cost compared with the Nash equilibrium strategy.In addition,the results show that under the Cartel mechanism,the behaviors of users deviating from cooperation will reduce their profits.Therefore,the mechanism designed in this paper can restrain the non-cooperative behavior of users.