Stochastic optimization framework for microgrid energy management integrating electric vehicles, renewable sources, and storage

Abstract

The rapid growth of electric vehicles (EVs) and renewable distributed generators (DGs) is transforming microgrid (MG) operation and introducing significant uncertainty into energy management. This study proposes a stochastic energy management (SEM) framework for a grid-connected microgrid integrating photovoltaic (PV) systems, wind turbines (WTs), battery storage (BS), and EV charging stations. Uncertainties in renewable generation, load demand, and electricity prices are modeled using a data-driven probabilistic scenario generation approach based on probability density functions and a roulette-wheel sampling mechanism, followed by fast-forward scenario reduction. The resulting optimization problem is formulated as a mixed-integer nonlinear programming model and tested on the IEEE 33-bus distribution system. Simulation results demonstrate that coordinated battery storage operation significantly enhances microgrid performance. In particular, the optimized scheduling strategy reduces operational costs by approximately 16.26% compared with scenarios without storage. In addition, battery integration improves voltage profiles, reduces system losses during peak demand periods, and mitigates stress on distribution infrastructure by lowering the maximum transformer loading from 3.69 MW to 2.96 MW. The findings highlight the importance of explicitly modeling operational uncertainty and demonstrate that stochastic optimization can provide more reliable and cost-effective energy management strategies for microgrids with high renewable penetration and significant EV integration.