Research On Pricing Strategy Of Electric Vehicle Load Aggregator

With the domestic power market reform in full swing,China’s power market system is becoming more and more perfect,and in the context of fully opening up the market for operational users to participate in the market,there are several types of emerging market players in the industry,including load aggregators.As a class of load aggregators,EV load aggregators can aggregate scattered EV loads to reach the access threshold of the electricity market and participate in the bidding of the electricity energy market and auxiliary service market.EV load aggregators can purchase electricity in the energy market on behalf of customers and provide charging services for EV customers.By purchasing low-priced electricity in the day-ahead market and selling it to customers at a charging price higher than the cost and lower than the real-time electricity price in the real-time charging process,the EV load aggregator and customers can make profits.The pricing strategy of the EV load aggregator becomes a crucial factor that affects the profitability.Too high a pricing will reduce the stickiness of users and reduce the competitiveness,while too low a pricing will make the profitability of the aggregator suffer.This paper takes EV load aggregators as the object of discussion,uses masterslave game theory to solve the pricing problem of mutual profit of EV load aggregators and users,and also designs an operation model of price arbitrage using V2 G function and proposes a rolling optimization strategy for real-time tariff uncertainty on the basis of agent EV user charging business,as follows.First,the EV load aggregator operation model is discussed in the context of the spot market,and V2 G contracts are added to the conventional contracts of traditional agent customer charging to increase revenue.Second,a master-slave game pricing model for EV load aggregators is constructed,which aims to maximize the interests of both EV load aggregators and users for dayahead pricing,and the model is linearized using the KKT condition and the dualization theorem.The simulation results show that the master-slave game pricing model can fully consider the interests of both parties and reduce the impact of disorderly charging on the grid to a certain extent..Finally,a day-ahead-real-time pricing optimization model is designed considering two types of contract users.In the day-ahead phase,a day-ahead two-stage pricing optimization model is designed to clarify the charging cost of users.The first stage is the master from game pricing stage,where the pricing obtained is the user charging price.The second stage is the V2 G charging and discharging optimization stage,which is designed to make the EV load aggregators and users get the maximum revenue by buying low and selling high.In the real-time phase,the day-ahead strategy may not obtain the maximum benefit due to the uncertainty of real-time electricity prices.Therefore,a real-time rolling optimization strategy is designed to compensate for the losses caused by fluctuations in electricity prices.The simulation results show that the V2 G contract can greatly increase the revenue of EV load aggregators,while users can also get a considerable share,and the orderly charging and discharging behavior indirectly helps to cut peaks and fill valleys on the grid side.The real-time rolling optimization strategy can effectively increase the revenue of EV load aggregators,reduce the revenue loss by nearly 33%,and solve the uncertainty of real-time tariff to a certain extent.