Control And Day-Ahead Bidding Strategies Of Electric Vehicle Aggregator For Multiple Application Scenarios

With the rapid development of electric vehicles(EVs)in recent years,the influence from the integrations of large scale EVs in the power grid have aroused considerable attention.The influence from an individual EV is neglectable as its battery capacity is really small to the power grid.We should pay attention to the aggregator effect from the integrations of EVs in the power grid.The operation states of the integrated EVs are affected by the factors of the traveling behaviors of EV users,the usage babits of EV users,the charging and discharing characteristics of EV batteries,the electricity market,and so on.These factors add the modeling complexity of EV aggregator with large scale EVs.Due to different communication levels and various market policies under different application scenarios,the developed modeling and control methods for EV aggregator needs to consider the factors of the fundamental infrastructure,the technical feasibility,and the application purpose.Considering above factors,this manuscript focuses on modeling,control and bidding strateges of the EV aggregator under different application scenarios.The main work is summarized as follows:1)In one specific application scenario,the EV aggregator control center can acquire the complete information of each EV.Firstly,based on the energy consumption characteristics and traveling behaviors of EVs,the individual EV model is developed considering three connecting states and four responding modes.Then,the parameters of each EV are obtained based on the Monte Carlo method,and the regulation capacities of each EV under different responding modes are obtained.Then,the EV aggregator model for evaluating the response capacity are developed to obtain the power ouput and regulation capacity.With this EV aggregator model,the power smoothing control strategy for the tie-line is developed based on the SOC adaptive method for EVs,and the coordinated control strategy for EV aggregators and conventional generators is developed based on the power flow tracing method.Simulation results validate that the regulation capacity of the EV aggregator is able to smooth the power fluctuations of the renewable energy.2)In one specific application scenario,the EV aggregator control center can only acquire the limited information of each EV and cannot conduct the high-quality individual communication with each EV.Firstly,considering connecting states and SOC states of EVs,the state space method and the Markov chain method are used to describe the state distribution and transition of the EV aggregator.Then,considering responding modes of EVs,the state space model of EV aggregator is developed to predict the power output and regulation capacity.With this EV aggregator model,the frequency control strategy of the EV aggregator is developed considering the state distribution and the adaptive control,and the global control signal is derived based on the state distribution to decrease the requirement for the communication infrastructure.Meanwhile,the frequency control strategy of the EV aggregator is developed considering the responding functions and the state recovery,and the global control signal is derived based on the connecting states.The control signal is further simplified and the requirement for the communication infrastructure is further lowered.The charging requirements of EV users are ensured with the user-side response functions and the global state recovery signal.Simulation results validate that the state space model of the EV aggregator achieve the high prediction and control accuracy.3)In one specific application scenario,the EV aggregator participates in the dayahead market biddng.First,from the perspective of the EV aggregator operator,the influence of compensation price on the participation rate of EV users are analyzed under different responding modes in the electricity maket.Then,considering the participation rate of EV users,the price reponse model of the EV aggregator is developed to estimate the regulation capacity and the cost curve of power output.With the analysis for the participation rate of EVs under this EV aggregator model,the incentive mechanisms for EV charging and discharging are proposed to encourage the participations of EVs in the day-ahead market condiering the uncentain factors from the market and EVs.Meanwhile,considering the temporal differences of the constraints of EVs and the constraints of market bidding,the day-ahead optimal bidding strategy is developed for the retailer to help obtain the optimal day-ahead bidding curve.Simulation results validate that the retailer can achieve higher bidding profits with the flexible participations of EVs in the day-ahead market.