Robust Energy Management of Hybrid Thermo-Electrical Microgrids Under Uncertainty Using a Fuzzy Monte Carlo-Based Dispatch Strategy

Efficient energy management in hybrid thermo-electrical microgrids is crucial for balancing thermal and electrical loads, particularly under uncertain demand and dynamic pricing conditions. Traditional deterministic and probabilistic methods often fail to capture such uncertainties, leading to suboptimal dispatch strategies and higher operational costs. This paper presents a robust energy management framework based on the fuzzy Monte Carlo method to model uncertainties in both thermal and electrical loads. Unlike conventional approaches, the proposed method enhances robustness and flexibility in responding to real-time price fluctuations by integrating a value-based pricing mechanism. The core contribution is the development of a coordinated energy dispatch strategy that simultaneously manages fuel cells, battery storage, and utility grid interaction, while incorporating demand response programs. The fuzzy Monte Carlo method enables a more comprehensive representation of both aleatory and epistemic uncertainties, supporting adaptive decision-making under volatile conditions. A realistic microgrid case study, including fuel cell, battery storage, and grid connection, is used to evaluate the proposed system. Simulation results demonstrate improved cost-effectiveness, operational stability, and load-balancing performance compared to conventional methods. The results validate the proposed framework as a viable and resilient solution for next-generation microgrid energy management systems under uncertainty.