Grid-Aware Electric Fleet Charging Management for Heavy-Duty Vehicle

This study explores depot charging scheduling for heavy-duty electric fleets, emphasizing energy dynamics and grid resilience. From a demand-side management (DSM) perspective, it integrates grid-aware planning by analyzing the energy flow from the grid to charging infrastructure and its impact on recharging strategies. To optimize charging times while enhancing grid stability, the study proposes a recharging scheduling framework that incorporates key metrics such as peak shaving and alignment with grid interchange data. The methodology is built on a metaheuristic Tabu Search algorithm, designed to improve grid resilience and mitigate peak demand impacts. A case study demonstrates the effectiveness of the proposed framework compared to conventional approaches that do not consider grid stability. These findings offer valuable insights for policymakers, grid operators, and fleet managers, aiding in the development of resilient and efficient large-scale electrified fleet operations.