Strategic Electric Vehicle Charging in Community Microgrids: Enhancing Grid Stability, Reducing Emissions, and Optimizing Costs

This paper investigates Electric Vehicle (EV) charging strategies within a community microgrid (CMG) framework, focusing on optimizing grid stability, minimizing emissions, and reducing system costs. The study analyzes user behavior and charging needs based on data such as charging times, state of charge (SOC) at arrival and departure, and EV battery capabilities. Three main charging strategies are considered: Earliest, Least Laxity, and Optimal. The Earliest strategy ensures EV readiness by prioritizing immediate charging, potentially raising peak-hour demand; the Least Laxity strategy improves demand distribution by prioritizing EVs with limited charging times; and the Optimal strategy balances demand with grid availability, reducing peak demand and leveraging low-price periods. The CMG under study incorporates renewable sources (solar and wind) and dispatchable resources (microturbines and fuel cells) to analyze how these charging strategies impact grid load and emissions. Additionally, the paper explores the CMG’s potential for energy independence through different grid interaction levels: only buying, only selling, and complete independence. The model optimizes charging schedules using mixed-integer linear programming (MILP) to achieve economic efficiency and emission reduction, comparing the effectiveness of each charging strategy within real-time grid conditions. This work demonstrates that strategic charging can effectively manage EV load, support grid stability, and enhance CMG reliability while balancing costs and emissions.