Hybrid Renewable Energy Systems (HRESs) have been touted as an appropriate way for supplying electricity to remote and off-grid areas in developing countries, especially in sub-Saharan Africa (SSA), where rural electrification challenges are the most pronounced. This study proposes a two-step methodology for optimizing and analyzing a stand-alone photovoltaic/wind/battery/diesel hybrid system to meet the electricity needs of Fanisua, an off-grid and remote village of northern Nigeria. In the first step, the MATLAB environment was used to run simulations and optimize the system via the genetic algorithm. Then, techno-economic and emissions analysis was carried out in the second step to compare the proposed system to the existing traditional modes of rural electrification in sub-Saharan Africa, namely, the grid-extension and diesel generator. The break-even distance parameter was adopted in the comparison with grid-extension. Besides, the hypothetical project of replacing the diesel generator by the optimal system was analyzed using the Simple Payback Period (SPP) and Net Present Value (NPV) parameters. The resulting optimal design architecture included an 89.271-kW photovoltaic array, a 100.31-W diesel generator, and 148 batteries with a total annualized cost (TAC) and cost of energy (COE) of USD 43,807 and USD 0.25/kWh, respectively. The break-even distance found was 16.2 km, while the NPV and SPP of the hypothetical project were USD 572,382 and 2.8 years, respectively. The savings in carbon dioxide (CO₂) emissions of the proposed system compared to the grid extension and the diesel generator were found to be 85,401.08 kg/year and 122,062.85 kg/year, respectively. This study highlighted the role that solar PV-based HRESs could play in the sustainable electricity supply in rural areas of sub-Saharan Africa.

The amelioration of photovoltaic (PV) and photovoltaic/thermal (PV/T) systems have garnered increased research interest lately, more so due to the discovery of the thermal property augmentation of nanofluids. The overarching goal of this study is to conduct a comparative analysis of mono, hybrid, and ternary hybrid nanofluids utilized as fluids for heat transfer applications and particularly as cooling mediums in PV/T applications. Al₂O₃, ZnO, Al₂O₃-ZnO, and Al₂O₃-ZnO-Fe₃O₄ nanofluids are synthesized at 1% volume concentration using the two-step method. The zeta potential tests carried out showed that the fluids have high stability. The numerical model developed in this study was validated using real data culled from Cyprus International University. The findings in this study showed that the Al₂O₃-ZnO-Fe₃O₄ ternary hybrid nanofluid and ZnO mono nanofluid were more efficient heat transfer fluids for the PV/T system. The optimum relative electrical PV/T efficiency against that of the PV is 8.13% while the electrical and thermal enhancement recorded in this study was 1.79% and 19.06%, respectively, measured for the ternary hybrid nanofluid based PV/T system. This present study shows that despite the limitation of pumping power and pressure drop associated with nanofluid in thermal systems, the close performance evaluation criterion values as compared with water is positive for practical utilization of nanofluid in PV/T systems.

The world has not been able to achieve minimum greenhouse gas emissions in buildings’ energy consumptions because the energy and emissions optimization techniques have not been fully utilized. Thermal comfort is one of the most important issues for both residential and commercial buildings. Out of the 40% of global energy consumed by buildings, a large fraction is used to maintain their thermal comfort. In this study, a comprehensive review of the recent advancements in building energy conservation and efficiency application is presented based on existing high-quality research papers. Additionally, the retrofit of the heating/cooling and hot water system for an entire community in Cyprus is presented. This study aims to analyze the technical and environmental benefits of replacing existing electric heaters for hot water with heat pump water heating systems and the use of heat pump air conditioners for thermal comfort in place of the existing ordinary air conditioners for space heating and cooling. One administrative building, 86 apartments (including residential and commercial) buildings, and a restaurant building is retrofitted, and the feasibility of the project is determined based on three economic indicators, namely; simple payback period (SPP), internal rate of return (IRR), and net present value (NPV). The electrical energy required by the hot water systems and the heating/cooling system is reduced by 263,564 kWh/yr and 144,825 kWh/yr, respectively. Additionally, the retrofit project will reduce Cyprus’ CO₂ emission by 121,592.8 kg yearly. The SPP, IRR, and NPV for the project show that the retrofit is economically feasible.

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R² values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

Due to environmental and economic drawbacks of fossil fuels, global renewable energy (RE) capacity has increased significantly over the last decade. Solar photovoltaic (PV) is one of the fastest-growing RE technologies. Selecting an appropriate site is one of the most critical steps in utility-scale solar PV planning. This paper aims at proposing a rational multi-criteria decision-making (MCDM) approach based on the Brown–Gibson model for optimal site selection for utility-scale solar PV projects. The proposed model considers the project’s net present value (NPV) along with seven suitability factors and six critical (constraint) factors. The RETScreen software was applied in calculating the NPV, the simple payback period and the carbon emission savings of the project at each alternative site. The weights of the suitability factors were determined using the analytical hierarchy process. Applied to the case study of finding the best location for a 5 MW solar PV project in northern Cameroon, the optimization results showed that Mokolo was the optimal location. The sensitivity analysis results revealed that the rankings of alternative sites based on the project’s NPV and the proposed model are not consistent. Compared to the traditional MCDM approaches, the proposed model provides decision-makers with a more practical thinking method in the optimal location process of utility-scale solar projects.