Research On Available Regulation Capability Evaluation And Bidding Strategy In Day-Ahead Market Of Electric Vehicles

Electric vehicles are low-carbon and pollution-free,and can effectively reduce external dependence on energy.Its development prospects are huge.With the rapid increase in the number of electric vehicles,in the future,electric vehicles will be connected to the grid in an orderly manner for charging and discharging,and become a flexible distributed energy storage resource.Converging and participating in grid auxiliary services will be an inevitable trend of development.However,as a special mobile distributed energy storage,the primary task of electric vehicles is to meet the travel needs of users.It is decentralized,random and affected by the factors of "people and society".Converging electric vehicles to participate in grid auxiliary services faces huge technical challenges.In this paper,taking electric vehicles converging and participating in secondary frequency regulation services as an example,from individual electric vehicles to convergent clusters,the research on regulation capacity evaluation and day-ahead bidding strategies is carried out.The main research contents are as follows:(1)From the perspective of individual electric vehicles,a robust evaluation strategy for regulation capacity is proposed that guarantees users’ commuting and takes into account the benefits of providing regulation services.First of all,based on the powerful edge computing capabilities of modern electric vehicles,it can realize real-time collection of key information such as ambient temperature and road conditions.The collected results can be used to estimate the actual energy consumption rate of electric vehicles after data model processing.Consequently,the energy required by a car to complete its daily travel tasks is calculated.In addition,based on the analysis and modeling of automatic generation control signals,the potential impact of participating in frequency regulation services on the state of charge of electric vehicles is quantitatively evaluated.And for the randomness of automatic generation control signals,a robust optimization algorithm is introduced to calculate the reasonable available regulation capacity of individual electric vehicles.In the end,this strategy can fully tap the regulation capacity of individual electric vehicles on the premise of reliably ensuring the travel needs of electric vehicle users.In addition,it will help promote electric vehicles as distributed energy storage systems to participate in providing frequency regulation services for the power system.(2)Introducing the parallel cyber-physical social system(CPSS)theory,and proposing an evaluation strategy for the cluster regulation capacity,which effectively solves the problem of cluster regulation capacity estimation caused by the randomness and self-interest of electric vehicle users.Based on CPSS theory,combined with private car travel statistics,power battery charging and discharging physical models,and electric vehicle users’ response to the economic incentives provided by the aggregator,a software-defined method is used to construct an artificial electric vehicle group that maps electric vehicles in the real world.Based on this parallel system,the Monte Carlo method is used to statistically simulate the total regulation capacity that can be provided by the artificial electric vehicle group in different scenarios.This strategy can help the aggregator to realize the time-sharing regulation capacity it can provide.(3)Regulation capacity is generally traded in the day-ahead market in the form of forward contracts.Aggregators cannot know in advance the time-sharing regulation capacity can be provided by the electric vehicle cluster and the spot price of unit regulation capacity when formulating the day-ahead bidding strategy.From the perspective of aggregators,based on minimizing the conditional value of risk(CVaR)of regret,this paper proposes a risk averse bidding strategy to participate in the day-ahead market.This strategy can avoid the default risk caused by the fluctuation of key factors in the real-time market,and realize the effective management and control of the electricity market transaction risk.