A Review of Decision-Making Approaches in Microgrid Energy Management Systems

The increasing integration of renewable energy sources into microgrid systems has driven significant advances in energy management system (EMS) design, yet the diversity of proposed approaches makes systematic comparison challenging. This paper presents a comprehensive review of decision-making approaches for microgrid EMSs, organized along three dimensions: architecture-based, decision-method-based, and application-context classification. The review covers deterministic mathematical programming; heuristic and meta-heuristic optimization; stochastic and robust optimization; data-driven intelligent and agent-based methods, including fuzzy logic, machine learning, reinforcement learning, and multi-agent systems; and model predictive control and its variants. For each category, representative studies are analyzed with respect to optimization objective, uncertainty handling, key components, and control architecture. The results show that classical methods offer transparency and optimality guarantees but are limited under uncertainty and nonlinearity conditions, while AI-based and MPC approaches provide adaptability and real-time performance at the cost of higher data requirements. This comparative analysis aims to guide researchers and practitioners in selecting appropriate EMS strategies for microgrid applications.