Development and optimization of an intelligent Energy Management System (EMS) for hybrid microgrids in rural Congo

In many villages of the Democratic Republic of Congo, night still falls into darkness due to lack of access to electricity. Less than 20% of rural areas have reliable power supply, which hinders education, healthcare, and economic activities. Hybrid microgrids, combining local renewable energies such as solar, mini-hydro, and biomass with backup fossil generators, offer a realistic and sustainable alternative. However, for these systems to operate efficiently, an intelligent EMS is required to optimally manage production, storage, and distribution.
This research proposes an EMS model based on multi-objective optimization, integrating linear programming, genetic algorithms, and predictive control through artificial intelligence. Simulations carried out with HOMER Pro and MATLAB/Simulink show encouraging results: a 30% reduction in energy costs, reliability above 95%, and a 40% decrease in CO₂ emissions. In a pilot village in Kasai, comprising 500 households, the system ensured an average consumption of 2 kWh/day per household, transforming daily life.
Beyond the figures, the human impact is considerable: schools can extend classes after sunset, health centers have electricity to preserve vaccines, and small local businesses see their productivity increase. The intelligent EMS is therefore not limited to a technical solution; it becomes a lever for community development and empowerment. Future prospects include integrating energy blockchain to facilitate local payments and using AI to anticipate maintenance needs, thereby ensuring system sustainability.