Development of an Adaptive Model Predictive Control Framework for Integrated Energy Management in Nigerian Industrial Microgrids with High Renewable Penetration

The integration of high proportions of variable renewable energy into Nigerian industrial microgrids serving manufacturing and petroleum facility loads introduces complex dynamic optimisation challenges that exceed the capabilities of conventional rule-based energy management systems, requiring advanced control architectures that can adapt to forecast uncertainty, equipment degradation, and evolving production schedules while maintaining supply reliability and minimising operational costs. This research develops a novel adaptive model predictive control framework for real-time energy management in industrial microgrids incorporating solar photovoltaic generation, battery energy storage, diesel generation, and controllable industrial loads, calibrated for the operational conditions and electricity tariff structures of Nigerian industrial facilities in the North Central and South West zones. The framework integrates stochastic renewable generation forecasting using quantile regression neural networks with a rolling-horizon model predictive control optimisation engine solved by mixed-integer linear programming at five-minute control intervals. An online learning mechanism updates the internal process models of the controller using recursive Bayesian estimation to account for battery degradation, demand pattern shifts, and solar panel soiling effects over time. A co-simulation validation environment coupling MATLAB-Simulink power system models with the Python-implemented controller is developed and validated against operational data from a Nigerian manufacturing facility. Simulation results demonstrate that the adaptive framework reduces total energy management cost by 23 percent relative to rule-based control and 11 percent relative to non-adaptive model predictive control, while maintaining supply reliability above 99.7 percent. Keywords: model predictive control, microgrid energy management, renewable integration, battery storage, industrial facility Nigeria.