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ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 9 (2026): Volume 10, Issue 9, September 2026 | Pages: 26-33
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 09-09-2026
Hot, Arid climates lead to performance drops of Renewable energy assets with heat problems and dust buildup. In one year. This makes a scheduling problem with 2 to the power of 365 possible schedules. Previous studies have explored these through experiments or a single kind of optimization trick. They often don't compare their results with statistics so it is hard to say which way really works better. The objective of this paper is to find the way to search when there is a fixed amount of time to check things. It compares four methods, a Genetic Algorithm, a sigmoid based Binary Particle Swarm Optimization, a newer V-shaped BPSO and a random search. Each of these methods were tested 30 times. Each had a budget of 6,000 checks. The data used were collected from 4215 days of weather data in Najaf, Iraq. It also contained heat loss and dust models. The Genetic Algorithm resulted in the average profit of 94.22 with a slight variation. The next one was V-shaped BPSO with 33.44. The sigmoid BPSO and the random search lost money with -98.23 and -130.04, respectively. A Mann-Whitney U test showed that the Genetic Algorithm performed much better than the others and this result is not due to chance. Additional robustness tests indicated that the difference between BPSO and GA was decreased when a V-shaped transfer function was used instead of the Sigmoid. It made GA more profitable reaching 140.40 ± 0.31 when the number of evaluations was increased to 30,000. Sensitivity analyses showed that the ranking was robust to changes in energy prices and cleaning costs. For another loss model including heat-dust interactions, the results of the algorithms did not change much. GAs schedule focuses on cleaning in the months with the most dust build up. This provides a real-world test of the results. This research offers proven advice for choosing algorithms for similar scheduling problems, not only in the solar energy sector.
Genetic algorithm, Particle swarm optimization, Combinatorial optimization, Maintenance scheduling, Photovoltaic soiling, Renewable energy
Mustafa Husham Abbas. (2026). Comparative Evaluation of Evolutionary Algorithms for PV Maintenance Scheduling: Application to Najaf Climate. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(9), 26-33. Article DOI https://doi.org/10.47001/IRJIET/2026.109003
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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