Modelling and Optimization of Bioenergy Feedstock Supply Chains in Nigeria’s Middle Belt Region

Nigeria's commitment to expanding renewable energy generation as part of its energy transition plan has elevated interest in bioenergy, with the Middle Belt region offering significant agricultural residue feedstock potential from maize, sorghum, and cassava production. This study develops and optimizes a bioenergy feedstock supply chain model for a proposed 25-megawatt biomass power plant to be sited in Niger State, North Central Nigeria. The supply chain encompasses biomass collection, pre-processing, transportation, and storage operations across a 150-kilometre procurement radius. A mixed-integer linear programming model is formulated to minimize total annualized supply chain cost, including collection, processing, transportation, and storage components, subject to feedstock availability constraints, plant demand requirements, and biomass quality specifications. Geographic information system data on agricultural production zones, road networks, and candidate facility locations are integrated into the model parameterization. Results identify the optimal configuration of collection hubs, pre-processing facilities, and transportation routes that minimize cost while meeting plant feedstock demand reliably across seasonal availability fluctuations. Total optimized supply chain cost is estimated at N1.84 billion annually, representing a 27 percent reduction compared to the baseline unoptimized configuration. Sensitivity analysis confirms solution robustness under feedstock yield and price variability. This study fills an empirical gap in bioenergy supply chain operations research in Nigeria and provides an evidence base for renewable energy investment planning. Keywords: bioenergy supply chain, feedstock optimization, Niger State, renewable energy, mixed-integer programming