A Study of Myopic, Predictive, and Reinforcement Learning Energy Management Strategies for Isolated Microgrids

Abstract

The increasing penetration of renewable energy sources in isolated microgrids has intensified the need for Energy Management Systems (EMS) capable of reducing diesel dependence while ensuring reliable operation. This study compares three EMS strategies for a photovoltaic–diesel–battery microgrid representative of remote communities in the Brazilian Amazon: a Myopic strategy (M1), a Predictive strategy (M2), and a PPO-RL strategy (M3), evaluated under identical physical and operational constraints using real irradiance and demand data over the complete year of 2025 at a 15 min resolution. The three strategies achieved very similar diesel consumption, with a difference of less than 0.7% between the best and worst cases; M2 achieved the lowest consumption (218,863.19 L), the highest renewable penetration (52.14%), and the lowest curtailment (1.06%), with M3 performing comparably but with more frequent generator starts. An illustrative, qualitative analysis of the highest- and lowest-PV days suggested that differences among strategies become more pronounced under high photovoltaic availability, while low renewable availability drives all strategies toward similar diesel-dominated operation. These findings indicate that the main benefit of advanced EMS strategies lies not in large diesel savings, but in improved coordination and utilization of renewable and storage resources. Additionally, a simplified economic analysis of the photovoltaic–battery hybridization, based on M2’s diesel savings, showed an internal rate of return of approximately 30.7% p.a. and a payback period of approximately 3.2 years, well above the 14% p.a. benchmark, confirming the hybridization as an economically attractive investment regardless of which EMS strategy is adopted.