Optimal economic and environmental assessment using transit search optimization for a hybrid renewable energy system in Egypt
DOI:
https://doi.org/10.18686/cest815Keywords:
Depth of Discharge (DOD); photovoltaic (PV) cells; Particle Swarm Optimization (PSO); Levelized Cost of Energy (LCOE); Hybrid Renewable Energy Systems (HRES); Transient Search Optimization (TSO)Abstract
This paper presents Hybrid Renewable Energy Systems (HRES) such as photovoltaic (PV) solar panels, wind turbines (WT), and battery storage. The primary objective is to demonstrate the monetary viability of hybrid renewable energy sources. The studies are cost of electricity (COE), greenhouse gas (GHG) emissions, and battery Depth of Discharge (DOD). Advanced optimization algorithms, especially Particle Swarm Optimization (PSO) and Transient Search Optimization (TSO), which account for high battery degradation in extreme temperature environments, were used in conjunction with the HOMER Pro software application to enhance the overall system performance in five single sites in Egypt: Salloum, Rafah, Kharge, Sadat City, and Abu Simbel. and the results show that the proposed hybrid gadget successfully meets the electrical load requirements, where operational constraints are overcome. In particular, the TSO algorithm completed the huge upgrade, achieving a minimum cost of energy generation (COE) of $0.023/kWh in Abu Simbel and a floor-total real cost of $26,061.98 in Kharge environmental TSO algorithm minimized the environmental ninety-nine.87 kg/year. Compared to the PSO and HOMER Pro algorithms, this represents a significant reduction. With a positioning error rate (LPSP) of less than 1% and three days of operational autonomy, the TSO algorithm demonstrated high reliability across all sites. These results reduce the adjusted COE and GHG at all the cases, supporting the economic and environmental feasibility of using the TSO algorithm to determine the optimal size of hybrid renewable energy systems (HRES) in all the different conditions and get the best solution.
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Copyright (c) 2026 Moayed Mohamed, Fatma Khalied, Ahmed Elnozahy, Ali M. El-Rifaie
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