Robust Optimisation Under Uncertainty for Energy Mix Planning in the Nigerian Electricity Sector

Planning the transition of Nigeria's electricity generation mix toward greater renewable energy integration requires decision-making frameworks capable of handling deep uncertainty in technology costs, fossil fuel prices, demand growth trajectories, and hydrological conditions affecting hydropower generation. This study applies robust optimisation methodology, specifically the min-max regret criterion and the scenario-based robust counterpart formulation, to the long-term generation capacity expansion planning problem for the Nigerian electricity sector. The planning model minimises total discounted system cost over a 20-year horizon from 2025 to 2044, selecting among candidate technologies including natural gas combined cycle, solar photovoltaic, onshore wind, battery storage, and large hydropower. Uncertainty is modelled through a discrete scenario set of 48 combinations spanning low, central, and high realisations of key uncertain parameters. The robust optimal plan is compared against the risk-neutral deterministic plan and a stochastic programming solution. Results indicate that the robust plan invests 23 percent more in solar photovoltaic and battery storage relative to the deterministic optimum, providing performance guarantees across all 48 scenarios while incurring a 9 percent cost premium over the deterministic scenario's lowest-cost outcome. The South West and South East zones are identified as priority solar deployment regions based on capacity factor data and demand proximity. Keywords: robust optimisation, energy mix planning, electricity sector, Nigeria, scenario-based programming.