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

Ali, ZM; Mostafa, MH · 2026 · PubMed Central

DOI: 10.1038/s41598-026-50822-6

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Summary

This study addresses the challenge of managing uncertainty in grid-connected microgrids (MGs) caused by the integration of renewable energy sources, electric vehicles (EVs), and battery storage. The rapid adoption of EVs and distributed renewable generators introduces significant variability in generation, load demand, and electricity prices. While deterministic scheduling methods are computationally efficient, they fail to capture this volatility, often resulting in suboptimal or unrealistic schedules. The authors identify a gap in existing literature, noting that few studies combine renewable intermittency, EV charging behavior, and market price fluctuations into a unified stochastic framework. Consequently, this paper proposes a stochastic energy management (SEM) framework designed to optimize MG operation under these combined uncertainties. The methodology employs a data-driven probabilistic approach to model uncertainties in photovoltaic (PV) generation, wind turbine (WT) output, load demand, and market prices. Uncertainties are represented using probability density functions and a roulette-wheel sampling mechanism to generate scenarios, which are then processed through a fast-forward scenario reduction technique to maintain statistical accuracy while reducing computational burden. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) model, implemented in the General Algebraic Modeling System (GAMS) and solved using the BONMIN solver. The framework explicitly accounts for battery degradation and replacement costs to ensure realistic long-term economic evaluation. The model is tested on a modified IEEE 33-bus distribution system featuring five PV units, five WTs, a battery storage system, and four EV charging stations. Simulation results demonstrate that the proposed stochastic framework significantly enhances microgrid performance compared to scenarios without storage. The optimized scheduling strategy reduces daily operational costs by approximately 16.26%. Furthermore, the integration of coordinated battery storage improves voltage profiles and reduces system power losses during peak demand periods. A key finding is the mitigation of stress on distribution infrastructure, evidenced by a reduction in maximum transformer loading from 3.69 MW to 2.96 MW. The analysis also highlights that battery parameters, such as depth-of-discharge and charging efficiency, critically influence both operational costs and long-term storage performance. The significance of this work lies in its demonstration that explicitly modeling operational uncertainty through stochastic optimization yields more reliable and cost-effective energy management strategies for microgrids with high renewable penetration and EV integration. By providing a unified framework that captures multiple sources of variability, the study offers a robust tool for operators to balance supply and demand, enhance grid stability, and maximize the economic benefits of storage and renewable assets. The findings underscore the importance of moving beyond deterministic models to accommodate the complex dynamics of modern, flexible power systems.

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