Benchmarking a novel particle swarm optimization dynamic model versus HOMER in optimally sizing grid-integrated hybrid PV–hydrogen energy systems.

Atteya, Ayatte I.; Ali, Dallia

Abstract

This paper presents the development of an Artificial Intelligence (AI)-based integrated dynamic hybrid PV-H2 energy system model together with a reflective comparative analysis of its performance versus that of the commercially available HOMER software. In this paper, a novel Particle Swarm Optimization (PSO) dynamic system model is developed by integrating a PSO algorithm with a precise dynamic hybrid PV-H2 energy system model that is developed to accurately simulate the hybrid system by considering the dynamic behaviour of its individual system components. The developed novel model allows consideration of the dynamic behaviour of the hybrid PV-H2 energy system while optimizing its sizing within grid-connected buildings to minimize the levelized cost of energy and maintain energy management across the hybrid system components and the grid in feeding the building load demands. The developed model was applied on a case-study grid-connected building to allow benchmarking of its results versus those from HOMER. Benchmarking showed that the developed model's optimal sizing results as well as the corresponding levelized cost of energy closely match those from HOMER. In terms of energy management, the benchmarking results showed that the strategy implemented within the developed model allows maximization of the green energy supply to the building, thus aligning with the net-zero energy transition target, while the one implemented in HOMER is based on minimizing the levelized cost of energy regardless of the green energy supply to the building. Another privilege revealed by benchmarking is that the developed model allows a more realistic quantification of the hydrogen output from the electrolyser because it considers the dynamic behaviour of the electrolyser in response to the varying PV input, and also allows a more realistic quantification of the electricity output from the fuel cell because it considers the dynamic behaviour of the fuel cell in response to the varying hydrogen levels stored in the tank.

Citation

ATTEYA, A.I. and ALI, D. 2024. Benchmarking a novel particle swarm optimization dynamic model versus HOMER in optimally sizing grid-integrated hybrid PV–hydrogen energy systems. Eng [online], 5(4), pages 3239-3258. Available from: https://doi.org/10.3390/eng5040170

• Journal Article Type: Article
• Acceptance Date: Dec 4, 2024
• Online Publication Date: Dec 9, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Dec 11, 2024
• Publicly Available Date: Dec 11, 2024
• Journal: Eng
• Print ISSN: 2673-4117
• Electronic ISSN: 2673-4117
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 4
• Pages: 3239-3258
• DOI: https://doi.org/10.3390/eng5040170
• Keywords: Hybrid photovoltaic-hydrogen energy systems; Hybrid PV-H2 energy systems; Particle swarms; Artificial intelligence (AI); HOMER Energy
• Public URL: https://rgu-repository.worktribe.com/output/2619353