Study on V2G potential of electric taxis based on map-matching multi-objective optimization

Abstract

The widespread deployment of electric taxis (ETs) and their integration into the power grid have exacerbated load fluctuations. While Vehicle-to-Grid (V2G) technology presents a viable solution, the frequent cycling of charging and discharging accelerates battery degradation, increasing battery replacement costs and potentially undermining the economic feasibility of ET adoption. This study examines ET in Shenzhen by leveraging GPS trajectory data to construct an analytical framework that integrates a map-matching algorithm with a multi-objective optimization model. The framework evaluates the economic implications and grid load fluctuations associated with ETs in parked and driving modes. The results of this study indicate that ETs can effectively reduce grid load fluctuations by up to 0.7683 % in parked mode through rational planning of charging and discharging time. Simultaneously, ETs achieve significant economic benefits of up to USD 5.92 per day. In contrast, the economic benefits in driving mode are lower than in parked mode, leading to a net loss of USD 5.92 per day for ETs with short discharge times. These findings offer valuable insights for optimizing ET charging and discharging and enhancing grid stability.