Performance Improvement of Rural Electrification–Based PV Energy System: A Case Study in Sub-Saharan Africa

The adoption of rural electrification–based PV solar energy is still weak and often subjected to rejection for some reasons such as its high cost and low performance to efficiently respond to the energy demand. Improving the two above-mentioned reasons could make it attractive. The optimal use of the energy generated by a PV system could lead to a significant reduction of its energy cost, while boosting its performance. The performance improvement of a PV system for rural electrification has been demonstrated in this study. A techno-economic analysis has been performed on two different PV systems scenarios based on an optimal design using Firefly algorithm. The reduction of the power losses for operating a solar mill led to the reduction of the cost of the PV solar energy and the reduction of the cereals milling cost. The cost of energy (COE) for the optimal system designed is 0.1702 $/kWh when the system is used only for electrification, while it is 0.1684 $/kWh when the system combines electrification with cereals milling. The combination of electrification and milling increased the load power demand by 5.039%, reduced the power losses by about 8%, and reduced the battery discharge by about 2.57%, in comparison with the configuration where milling is not considered. The optimal milling system designed corresponds to a cost of milling (COM) of 0.0063 $/kg and a throughput of 62.4608 kg/h. It came out that the milling system designed (composed of an electric motor of 2 kW with a speed of 1000 rpm, and a hammer mill of 1.6778 kW with a speed of 3800 rpm) could be able to fulfill the daily milling demand of 938 people per day. It appeared that photovoltaic electric milling could be more cost-effective than diesel milling mostly used in sub-Saharan Africa. The sensitivity analysis on the COE has shown that load, project lifetime, and interest rate are the parameters that most influence the COE. Likewise, the sensitivity analysis on the COM has shown that the project lifetime and the electrical energy purchased are the most influential parameters on the variation of the COM.