The Optimization of Hybrid Renewables for Rural Electrification: Techniques and the Design Problem

Abstract

Presently, there is mounting pressure to decarbonize the power grid via the use of renewable energy (RE) sources. However, most of these sources could perhaps not easily replace conventional power plants due to the fact that they have nonlinear characteristics, and most of these issues still need to be resolved. In order to effectively manage the non-linearity of some RE sources and improve the energy harvesting capability of these systems, various methods have been adopted. Some of these techniques have one or more objective functions and commonly use commercial packages to minimize system cost and improve output power simultaneously. Rural electrification is still pitiably prohibitive, especially in Sub-Saharan Africa, and off-grid RE technologies remain a reliable way of meeting rural electricity needs. Exploiting these sources could help offset rural electricity demand sustainably while mitigating the negative impacts of using a diesel generator and other sources that are not environmentally friendly. This study reviews issues regarding the hybrid RE system design with insights on the most often conflicting constraints. Also highlighted are the recent trends in hybrid system optimization with RE technologies and the various considerations scholars use in hybrid applications. Moreover, the study has discussed the essential aspects of evaluating the performance of a hybrid RE system and its applicability in rural electrification. The study has explored various methods and concludes that hybrid techniques based on artificial intelligence (AI) offer better performance for system optimization.