Implementation of a microgrid energy management system considering fair EV charging, uncertainties and contingencies: A multi-objective approach

The integration of distributed energy resources (DERs), such as battery energy storage systems (BESSs), photovoltaic (PV) systems, and electric vehicle (EV) chargers, presents new challenges for energy management system (EMS) in microgrids. A key challenge is developing models that incorporate real-time analysis, three-phase systems, and transitions between grid-connected and isolated modes while accounting for uncertainties in PV generation and demand. Additionally, ensuring fairness in EV charging within microgrids is essential for user satisfaction and system performance. This work addresses a multi-objective optimization problem (MOOP) aimed at minimizing operational costs from the main grid and energy non-supplied (ENS) for EVs, incorporating a fairness index to ensure equitable energy distribution among connected vehicles. This index considers factors such as state of charge (SoC), energy capacities, and charging time availability at electric vehicle charging station (EVCS). The proposed model is simulated using Hardware-in-the-Loop (HIL) within a practical Internet of Things (IoT) framework, accounting for multiple contingencies and uncertainties. To evaluate the approach, data from CAMPUSGRID, a three-phase AC microgrid at Universidade Estadual de Campinas (UNICAMP), is utilized. The results present a Pareto front, enabling the selection of an optimal compromise solution for the MOOP. This solution achieves low ENS for EVs while yielding nearly 18 % savings in operational costs. Furthermore, the fairness index is tested with competing factors (SoC, energy capacities, and available charging time), demonstrating that EVs with higher energy capacity and demand are slightly favored, ensuring equitable energy distribution under diverse conditions.