Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Abstract

With the share of electric vehicles in the transportation sector rising, solutions for their power system and energy market integration are of increasing importance. In this context, aggregation entities are helpful for addressing the integration process of electric vehicle (EV) fleets. In this paper, a novel methodology for day-ahead charging control of EV fleets is proposed. This includes the prediction of required energy used for charging purposes based on synthetic data and functionalities for optimized energy procurement in the day-ahead energy market to determine the aggregated charging schedule of the fleet. The aggregated charging schedule provides the basis for the proposed charging control algorithm for individual EVs. This algorithm allows for charging flexibility according to the user's driving preferences while considering data privacy. To avoid data privacy conflicts, the scheduling of the individual charging is decoupled from the upcoming individual driving schedules of the users themselves. This means that the user is not asked to plan and submit expected driving profiles. Instead, realization is by a user-oriented disaggregation that utilizes the aggregated charging schedule as an input to coordinate the individual charging processes itself. The resulting integrative market solution allows to fulfill the contract positions in energy market participation. The simulation results verify the optimality of the proposed methodology for day-ahead operation and its applicability for aggregation entities that serve as an intermediate between the vehicle owners and electrical utilities or the energy market.