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A Comparative Analysis of Nigeria's Power Sector with and without Grid-Scale Storage: Future Implications for Emission and Renewable Energy Integration

This research proposes a framework for modeling and comparing two electricity scenarios for Nigeria by 2050, focusing on the inclusion and exclusion of electricity storage technologies. A Central Composite Design (CCD) was used to generate a design matrix for data collection, with EnergyPLAN software used to create energy system simulations on the CCD data for four outputs: total annual cost, CO2 emissions, critical excess electricity production (CEEP), and electricity import. Three machine learning algorithms, support vector regression (SVR), extreme gradient boosting (XGBoost), and multi-layer perceptron (MLP), were tuned using Bayesian optimization to develop models mapping the inputs to outputs. A genetic algorithm was employed for multi-objective optimization to determine the optimal input capacities that minimize the outputs. Results indicated that incorporating electricity storage technologies (EST) leads to a 37% increase in renewable electricity sources (RES) share, resulting in a 19.14% reduction in CO2 emissions. EST such as battery energy storage systems (BESS), pumped hydro storage (PHS), and vehicle-to-grid (V2G) storage allow for the storage of the critical excess electricity that comes with increasing RES share. Integrating EST in Nigeria's 2050 energy landscape is crucial for incorporating more renewable electricity sources into the energy system, thereby reducing CO2 emissions and managing excess electricity production. This study outlines a plan for optimal electricity production to meet Nigeria's 2050 demand, highlighting the need for a balanced approach that combines fossil fuels, renewable energy, nuclear power, and advanced storage solutions to achieve a sustainable and efficient electricity system.

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