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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Olumuyiwa Ajibola Awoniyi
University of Abuja
Control & Systems Engineering Electrical & Electronics Engineering

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Performance optimization of hybrid renewable energy systems with real-time load forecasting using grey wolf-based predictive models Olumuyiwa Ajibola Awoniyi; Evans Chinemezu Ashigwuike; Chijioke Ejimofor; Timothy Oluwaseun Araoye
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1382-1395

Abstract

The performance optimization of hybrid renewable energy systems (HRES) is crucial for enhancing the efficiency, reliability, and sustainability of energy production. This study focuses on the integration of real-time load forecasting prediction using a grey wolf optimization (GWO)-based predictive model. The proposed methodology aims to address the challenges associated with the intermittent nature of renewable energy sources, such as solar and wind power, by providing accurate forecasts for load demands and solar irradiance. Real-time data from sensors and environmental parameters are incorporated to forecast the energy load and solar irradiance over short-term periods, which are then used to optimize the energy storage and generation components of the HRES. The GWO algorithm, known for its high accuracy and computational efficiency, is employed to optimize the dispatch of power from various sources while minimizing energy losses and ensuring system stability. The integration of GWO with real-time forecasting not only enhances the predictive capability of the system but also improves the overall economic viability of HRES by reducing operational costs and carbon emissions. This study demonstrates the potential of using intelligent optimization techniques and real-time forecasting for the sustainable operation of hybrid renewable energy systems, contributing to the development of smarter and more resilient energy grids.
Integrated-grid energy system for supplying reliable electricity to health sectors using grey wolf optimizer algorithms Olumuyiwa Ajibola Awoniyi; Evans Chinemezu Ashigwuike; Timothy Oluwaseun Araoye
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.10778

Abstract

This study evaluates the techno-economic and environmental performance of various hybrid renewable energy system (HRES) optimized using the gray wolf optimization (GWO) algorithm, aimed at supplying reliable and sustainable energy to a health centre. Multiple system configurations were analyzed, including combinations of photovoltaic (PV), wind, diesel generator (DG), battery storage, and the electrical grid. Key performance metrics assessed include the cost of energy (COE), net present cost (NPC), payback period, annual utility bill savings, carbon dioxide (CO2) emissions, and annual fuel consumption. Results indicate that all hybrid systems significantly outperform the conventional grid-only base case across all evaluated parameters. The base case exhibits the highest COE ($0.159/kWh) and CO2 emissions (8,549 tons/year), underscoring the economic and environmental drawbacks of sole grid dependency. In contrast, the PV/wind/diesel/grid configuration achieved the lowest COE ($0.013676/kWh) and substantial reductions in emissions (2,349 tons/year), with a favorable payback period of 4.5 years. The optimization results highlight the effectiveness of GWO in balancing economic viability with environmental sustainability. Among the configurations, the PV/wind/grid and PV/wind/diesel/grid systems emerged as the most cost-effective and environmentally beneficial solutions. These findings highlight the potential of intelligently optimized hybrid renewable systems to deliver reliable, low-cost, and low-emission energy to critical infrastructure, such as healthcare centers.
Co-Authors Chijioke Ejimofor Evans Chinemezu Ashigwuike Timothy Oluwaseun Araoye