Abstract:

Unlike refueling an internal combustion engine vehicle, charging electric vehicles is time-consuming and results in higher energy consumption. Therefore, as the adoption of electric vehicles rises, charging stations will have a number of difficulties in offering high-quality charging services. These charging infrastructures must satisfy charging demands without overloading the power grid. In this work, the authhors investigated the problem of scheduling the charging of electric vehicles to reduce the maximum peak power while satisfying all charging demands. The authors considered a charging station where the installed chargers deliver a preemptive constant charging power. These chargers can either be identical or non-identical. For both cases, this paper addressed two optimization problems. First, the athors studied the problem of finding the minimum number of chargers needed to plug a set of electric vehicles giving different arrival and departure times and required energies. The authors proved that this problem belongs to the complexity class P, and the authors provided polynomial-time algorithms. Then, the authors studied the problem of minimizing the power grid capacity. For identical chargers, the authors proved that the problem is polynomial, whereas it is NP-hard in the case of non-identical chargers. For both cases, these issues were formulated in this paper as a mixed-integer linear programming model. The authors suggested a heuristic and an iterated local search metaheuristic to find near-optimal solutions for the NP-hard problem. This research illustrated the usefulness of the suggested methods for reducing grid capacity using computational findings.