Multi-Type Energy Day-Ahead Optimal Dispatching Method Considering Electric Vehicle To Grid

With the depletion of fossil energy and the increasingly prominent environmental problems,how to reduce the carbon emissions of energy production and improve the efficiency of energy utilization has been widely concerned by the academic and industrial circles.Especially with the large-scale promotion and application of renewable energy and electric vehicles,how to fully exploit the low-carbon and high-efficiency characteristics from source and load sides has become one of the key links to improve the above problems.However,the large-scale integration of electric vehicles and renewable energy with strong random characteristics will make the current power energy system dispatching face great difficulties and challenges.Therefore,in a multi-type energy supply scenario composed of renewable energy,electric vehicles,energy storage and conventional power,how to build targeted multi-type energy coordinated dispatching methods and decisions to adapt to the large-scale integration of electric vehicles and renewable energy is of great significance to realize the low-carbon and efficient operation of power system.In this regard,based on the dispatching of renewable energy,energy storage and conventional power supply,this paper focuses on exploring the impact of electric vehicle to grid,and studies the da-ahead coordinated dispatching and optimal decision-making method of multi-type energy system including electric vehicles and renewable energy under this background.The main research work of this paper is as follows:Based on the analysis of historical travel data,an electric vehicle charging load calculation model is established to realize the feature mining of electric vehicle charging behavior.Firstly,the factors that affect the electric vehicle power demand are analyzed.Combined with the historical travel data,the electric vehicle time variables and spatial variables are analyzed one by one,and the corresponding probability distribution curve is obtained.Furthermore,for variables that are inconvenient to calculate,such as power consumption per kilometer,the value range is determined by fuzzy control theory.Finally,according to the electric vehicle charging load calculation process,the Monte Carlo method is used to repeatedly simulate the charging behavior characteristics,and the electric vehicle charging load demand is calculated to provide technical support for the research on the energy trading optimization strategy later.By introducing the penalty mechanism for imbalance,a day-ahead coordinated optimal dispatching method combining the charging characteristics of electric vehicles and wind energy storage system is proposed.Firstly,the scenario generation method and scenario reduction technology are used to establish the uncertain wind power output model,and the penalty mechanism for the imbalanced energy is introduced in the day-ahead optimal dispatching to restrict the imbalance hat may be caused by the uncertainty of wind power.At the same time,the electric vehicle charging model is used to optimize the charging behavior and coordinate the dispatching of the wind energy storage system.A two-stage stochastic programming model is established in the case of coordinated and uncoordinated disatching,and CVaR is used to manage the profit and risk brought by coordination under different risk aversion attitudes.Finally,the model is linearized and solved,and a case shows that the proposed method can effectively improve the wind power consumption and enhance the economic benefits.Based on Nash equilibrium theory and Cournot model,this paper proposes a multi-type energy day-ahead optimization decision considering the priority of renewable energy consumption.Firstly,based on Nash equilibrium and Cournot model in game theory,a virtual power plant model combining renewable energy power producers,electric vehicle agents and energy storage system is established,and Shapley value is used to distribute the benefits among each other.Secondly,a game equilibrium model is established for individual competition,virtual power plant cooperation,and considering the priority of renewable energy output in the three cases.The KKT condition is transformed into a single-layer model,and the nonlinear complementary function is introduced to solve the model.Finally,a case is given to illustrate the effectiveness of the proposed dispatching decision in reducing bidding deviation,balancing load fluctuation and restraining market price.