Abstract
The fixed service charge pricing model adopted by traditional electric vehicle aggregators (EVAs) is difficult to effectively guide the demand side resources to respond to the power market price signal. At the same time, real-time pricing strategy can flexibly reflect the situation of market supply and demand, shift the charging load of electric vehicles (EVs), reduce the negative impact of disorderly charging on the stable operation of power systems, and fully tap the economic potential of EVA participating in the power market. Based on the historical behavior data of EVs, this paper considers various market factors such as peak-valley time-of-use tariff, demand-side response mode and deviation balance of spot market to formulate the objective function of EVA comprehensive revenue maximization and establishes a quarter-hourly vehicle-to-grid (V2G) dynamic time-sharing pricing model based on deep deterministic policy gradient (DDPG) reinforcement learning algorithm. The EVA yield difference between peak-valley time-of-use tariff and hourly pricing strategy under the same algorithm is compared through the case studies. The results show that the scheme with higher pricing frequency can guide the charging behavior of users more effectively, tap the economic potential of power market to a greater extent, and calm the load fluctuation of power grid.
Highlights
With the development of electric vehicles (EVs), electric vehicles have gradually become new transaction subject in power market with the continuous expansion of its volume
Aiming at the discrete problem of traditional time-sharing pricing model for EV, a quarter-hourly dynamic pricing strategy based on deep deterministic policy gradient (DDPG) reinforcement learning algorithm is proposed to fully develop EV scheduling potential
Taking the annual actual travel data of EV in a certain region of North China and the price data of the electric power trading market as an example, the three scenarios of electric vehicle aggregators (EVAs) revenue and load changes under quarter-hourly pricing, hourly electricity price and dynamic peak-valley time-of-use tariff are compared, which verifies the superiority of this method
Summary
With the development of electric vehicles (EVs), electric vehicles have gradually become new transaction subject in power market with the continuous expansion of its volume. Dynamic programming methods cannot cope with the impact of unstable environments brought by users with highly flexible behaviors In this case, EVA needs to design an appropriate mechanism to design the pricing scheme to maximize its overall profit while considering the user response mode. Generation cost analysis is the basis of EVA pricing This method is simple and feasible, but it does not take into account the price information of the external environment, and it is difficult to maximize its own profit [7]; after solving the constraints, user behavior analysis usually gets the theoretically fixed user elasticity coefficient or probability density curve, which cannot accurately fit the daily dynamic travel demands of users. Through the comparative analysis of different pricing frequencies, the effectiveness of the algorithm and the necessity of fine pricing are verified
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