Sustainable Energy Transition in Sub-Saharan Africa: Multi-Objective Optimization for Net-Zero Energy Buildings
Abstract
Sub-Saharan Africa (SSA) is challenged by an energy crisis characterized by rising demand for electricity, unstable electricity supply and high reliance on fossil fuels. Despite abundant renewable energy resources (RES), the region remains energy insecure, exacerbating socio-economic and environmental challenges. Net-Zero Energy Buildings (NZEBs), which generate as much energy as they consume annually through RES, present a viable solution to reduce fossil fuel dependence, reducing energy costs, and mitigate climate change. This study employs Multi-Objective Genetic Algorithm (MOGA) in MATLAB to optimize NZEB systems, integrating solar PV, wind, battery storage, and hydrogen. Two optimization strategies are considered: minimizing Annual Cost of the System (ACS) and Loss of Power Supply Probability (LPSP), and incorporating Total Energy Transfer (TET) minimization for grid stability. The optimized system increases the renewable energy fraction (REF) by 96.72% and decreases the levelized cost of energy (LCOE) from $0.24/kWh to $0.21/kWh, shortening the payback period from 9.29 years to 8.03 years. In addition, it saves more than 1.09 million kg of CO₂ emissions each year and prevents 637,000 m² of deforestation. These findings provide a scalable model for incorporating NZEBs into energy policy in SSA, including strategies for energy-efficient building codes and grid modernization.
Keywords
Net-Zero Energy Buildings (NZEBs), Multi-Objective Genetic Algorithms (MOGA), Grid-connected hybrid renewable energy systems, sustainable energy transition, Sub-Saharan Africa, carbon emissions reduction, land requirement