BI-OBJECTIVE OPTIMIZATION OF HYBRID RENEWABLE ENERGY SYSTEMS USING MODIFIED GRAY WOLF OPTIMIZATION ALGORITHM: A RELIABILITY-CONSTRAINED CASE STUDY OF RURAL NIGERIA.

Abstract

Reliable and affordable electricity access remains a major challenge in rural regions of developing countries, particularly in sub-Saharan Africa. Hybrid renewable energy systems (HRES), integrating photovoltaic (PV), wind turbine (WT), and battery energy storage systems (BESS), offer a viable decentralized solution. This study presents a reliability-constrained bi-objective optimization framework for the optimal sizing of a PV-wind-battery system for the Dirin Gari rural community in Nigeria using a Modified Gray Wolf optimization (MGWO) algorithm. The proposed MGWO incorporates a regression-based learning mechanism that utilizes historical best solutions, along with a diversity adjustment strategy to enhance search capability and convergence stability. The optimization problem is formulated to minimize system cost while satisfying reliability constraints defined by Loss of Power Supply probability (LPSP). A parametric approach is adopted to evaluate system performance across a wide range of reliability levels (20.9%-97.8%), enabling a comprehensive assessment of cost-reliability trade-offs. Results show that lower LPSP values improve system reliability but significantly increase cost due to higher storage requirements, while higher LPSP levels reduce cost at the expense of supply quality. The optimal configuration at 20.9% LPSP yields component capacities of 395.4 kWh (PV), 606.5 kWh (WT), and 142.5 kWh (BESS), with an estimated lifecycle cost of 1.62 million USD. Comparative analysis proves that MGWO achieves improved cost efficiency and faster convergence than the conventional GWO