Optimization-Based Metaheuristic Techniques for Sizing and Managing Uncertainty in Hybrid Renewable Energy Systems Considering Demand-Side Challenges | ||
| SVU-International Journal of Engineering Sciences and Applications | ||
| Volume 6, Issue 2, December 2025, Pages 104-129 PDF (2.58 M) | ||
| Document Type: Original research articles | ||
| DOI: 10.21608/svusrc.2025.385645.1286 | ||
| Authors | ||
| Mohamed Hassanein; Mohamed A. Mohamed* ; Ahmed Hassan | ||
| Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt | ||
| Abstract | ||
| Providing electricity to remote areas is more expensive and technically challenging than grid-connected areas. Meanwhile, the effects of climate change have been exacerbated by overreliance on fossil fuels such as coal, oil, and natural gas used for power generation, transportation, and industry, leading to high CO2 emissions and environmental degradation. In response to fuel depletion and energy crises, researchers have turned to clean, renewable alternatives including wind, photovoltaic (PV), geothermal, hydropower, and green hydrogen. Despite their sustainability, the intermittent nature of renewable resources raises concerns about energy reliability, especially in off-grid applications. To address this problem, Integrated Hybrid Renewable Energy Systems (IHRES) combine multiple renewable sources with storage solutions such as batteries, supercapacitors, and fuel cells in addition to a backup diesel generator (DG). This paper presents a techno-economic analysis of a standalone PV/wind/DG/battery system for New Minya, Egypt, using real-time meteorological data. Advanced metaheuristic algorithms, along with Demand Side Management (DSM), Load Following (LF), and Cycle Charging (CC) strategies, are applied to optimize system sizing and minimize the Cost of Energy (COE), while meeting constraints such as Loss of Power Supply Probability (LPSP) and dummy energy. Among eight tested optimization algorithms, the Salp Swarm Algorithm (SSA) demonstrated the best performance, delivering the most reliable and cost-effective microgrid configuration. | ||
| Keywords | ||
| Microgrids; Demand Side Management (DSM); Cost of Energy (COE); Loss of Power Supply Probability (LPSP); Uncertainty | ||
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