Hierarchical co-optimization of EV scheduling considering customer and system in distribution networks

The global surge in the adoption of electric vehicles (EV) has affected distribution networks, with uncoordinated charging increasing peak load and loss. EV scheduling has the potential to become a business that utilizes vehicle-to-grid (V2G) technology to mitigate system impacts while providing financial incentives to customers. Efficient EV scheduling is a multi-objective optimization problem that simultaneously considers network and customer perspectives for co-optimization. This study introduces a hierarchical EV charging management platform (EVMP) with day-ahead and real-time stages that satisfy customer preferences while ensuring efficient system operation. In this process, in order to solve the computational issues of the optimal power flow (OPF) problem, including integer variables, the computational burden was alleviated by removing integer variables from the OPF problem of the day-ahead stage, and EV dynamics were effectively handled using the rolling-horizon framework in the real-time stage. This study focuses on the relationships between the objective functions at each stage and utilizes the augmented epsilon-constraint method (AUGMECON) to obtain a Pareto front that assists in stakeholder decision making. The Pareto front is constructed by considering stakeholders from both two- and three-way perspectives, allowing the system operator to select a solution from the Pareto front for decision making.