Renewable Energy Systems and Artificial Intelligence: A Theoretical Framework for Forecasting, Optimisation and Control in Weak-Grid and Off-Grid Contexts

Abstract

The electrification of Africa is proceeding largely through distributed solar, mini-grids and hybrid systems, which either connect to central grids that are weak and unreliable or operate where no grid exists at all. Artificial intelligence (AI) is increasingly applied within such systems to forecast generation and demand, to schedule storage, dispatch and load, and to maintain stability in real time. This paper develops a framework for AI in renewable energy systems under weak-grid conditions, drawing on power-systems engineering, the energy-access and mini-grid literatures, and recent research on machine learning for solar forecasting, energy management and inverter control. It distinguishes three engineering functions, forecasting, optimisation and control, and argues that weak-grid conditions alter the problem each must solve. Forecasting must contend with sparse and unreliable data and with demand that is low, uncertain and still evolving; optimisation must weigh reliability and affordability for low-income consumers rather than cost alone; and control must manage frequent islanding and grid failure. Two further themes run through the analysis: the trade-off between the sophistication of AI methods and the maintainability of the systems that use them where technical capacity is limited, and the interaction between AI-enabled distributed systems and the stability of the weak central grids to which they connect. Nigeria's electrification challenge anchors the discussion, and the paper closes with implications for engineers, developers, regulators and funders. It derives propositions linking forecasting accuracy, optimisation objectives and control strategies to the data, affordability and reliability conditions of weak grids, and argues that, where maintenance capacity is limited, distributed architectures of modest sophistication will often deliver more reliable service than centrally optimised systems whose performance depends on technical support that cannot be sustained.