| Led by the dual carbon strategy goal,China’s electric vehicle ownership is growing rapidly and the charging load penetration rate is rising rapidly.Guiding electric vehicle loads to respond to grid regulation needs can greatly alleviate the impact of disorderly charging and discharging of electric vehicles on the power system.At the same time,by effectively stimulating the value of EV mobile energy storage,it can provide abundant stock flexibility resources for the new power system.However,the willingness of EV users to respond is seriously insufficient.At present,the EV load participating in grid demand response in China was less than 1%of the total charging load.The reason is that existing research equates vehicle-grid interaction to the problem of power battery regulation and operation.But the intrinsic mechanism of vehicle owner group behavior is ignored.Therefore,a systematic study of EV load aggregation trading and operation management decision problem under the influence of owner group behavior is urgently needed.This paper focuses on the key scientific issues of EV load aggregation trading and pricing decision considering the owner group behavior.The paper investigates the modeling approach of EV owner group behavior.Further,the techno-economic and management bottlenecks of resource allocation,transaction decision,and pricing optimization faced by EV load aggregation operation are explored.The research in this paper has the theoretical innovation of the methodological system and the practical application value of the research content.Firstly,the paper compared the research background and significance of EV load aggregation trading and pricing model.The current situation of domestic and international development of EV load aggregation participation in electricity market trading is analyzed.A review of the current status of EV load aggregation research was conducted,including aggregation trading strategies,aggregation regulation optimization methods,aggregation pricing strategies and group behavior modeling.The research content and technical path of the electric vehicle load aggregation trading and pricing model considering group behavior are proposed.Then,the paper studied the electric vehicle load aggregation trading process and cost-benefit analysis method.The research analyzes the current situation and trend of EV load aggregation trading development in China.Based on the analysis of electric vehicle load characteristics,the aggregation trading structure is designed.Combined with China’s electricity market mechanism,the electric vehicle load aggregation trading model and trading process are constructed.The cost-benefit analysis method of electric vehicle load aggregation is proposed.Then the key technical issues affecting the operation of load aggregators are analyzed.The logical ideas of the subsequent research in this paper are described.The foundation is laid for conducting research on EV load aggregation trading and pricing strategies.Secondly,the paper investigated the method of modeling the group behavior of electric vehicle load aggregation owners.The factors influencing the individual behavior of owners of different types of electric vehicles have been analyzed.The mechanism of owner behavior interaction influence in different response stages was explored.The interactive influence of owner behavior in the whole link of load aggregation response is established.A group behavior characterization model of EV owners considering the influence factors and trend extrapolation was constructed.A deep reinforcement learning-based extrapolation method of EV owner group behavior is proposed.The dynamic evolution of EV owner group behavior under aggregation operation mode was revealed.It lays the foundation for the formulation of EV load aggregation transaction and pricing strategy.Again,the paper investigated the optimization problem of hierarchical aggregation of EV load adjust-ability taking into account uncertainty.The EV load was classified into two categories:incentive-guided and direct-controlled.The uncertainties faced by EV load aggregation were analyzed by considering the uncertainty of vehicle owner’s behavior and market revenue uncertainty.Further,based on the load aggregation response deviation assessment constraint under different markets,the quantitative assessment index of EV load adjustable capability has been proposed.An optimization model of electric vehicle load adjustable capacity aggregation with graded response to market demand has been established.The optimal matching of electric vehicle heterogeneous flexibility resources with market demand has been achieved.This is the foundation for the development of EV charge/discharge load aggregation trading strategy.Again,the paper studied the optimization problem of EV load aggregation trading declaration strategy under multiple markets.The scenario of the study on the transaction declaration strategy is the EV load aggregators’ participation in medium and long term electricity energy trading and peaking auxiliary service trading scenario.The study investigated the EV load aggregators’ participation in medium and long term electricity energy and peaking auxiliary services declaration methods and trading strategies.By exploring the coordinated trading mechanism of multiple types of markets,an optimization model of EV load aggregation trading strategy under the coordination of multiple markets has been established.The trading volume and price declaration strategies for EV participation in multi-markets were proposed.It supported the strategy formulation for EV participation in multi-market trading.Finally,the paper investigated the optimization model of electric vehicle load aggregation incentive pricing strategy based on value transfer.The types of EV load aggregation incentive strategies were sorted and analyzed from the perspective of load aggregators.Response price elasticity models were developed for EV operators and non-operators,respectively.Then,an incentive model for individual and group behavior of EV users has been constructed.A pricing strategy optimization model for EV load aggregators was established.This supported the value transmission and operational pricing strategy formulation for EV load aggregation transactions.Through this paper,theoretical approaches such as behavioral modeling,decision optimization,reinforcement learning,and machine learning are used to solve the management decision problems faced by electric vehicle load aggregation.The research results can provide decision support for the optimal allocation of adjustable capacity,optimization of aggregation transaction decision,and optimization of incentive pricing faced by EV load aggregation operation.At the same time,the research results can provide reference for the participation of multiple types of demand-side resources such as energy storage and flexible load aggregation in market transactions. |
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