Hybrid energy storage lifespan optimization based on an enhanced fuel-cell degradation model and meta-heuristic algorithm

• Published in: Energy reports 2024-12, Vol.12, p.5712-5727
• Main Authors: Nkwanyana, Thamsanqa B., Siti, Mukwanga W., Wang, Zenghui, Mulumba, Willy
• Format: Article
• Language: English
• Subjects: Dragonfly; Firefly; Hybrid energy storage system; Optimal control; Polymer electrolyte membrane fuel cell; Renewable energy sources
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2024.11.028

Enhancing the supply of power in terms of availability, dependability, and security are the current goals of the electricity industry. Incorporating renewable energy sources (RES) into the electrical grid to address the issue of clean energy scarcity has consequently garnered a great deal of attention. However, the utilization of hybrid energy storage systems (HESS) is required due to the high degree of electricity supply instability of RES paired with variations in energy demand levels. HESS still faces issues with lifespan deterioration and the need to increase electricity availability. This research, therefore, suggests an improved polymer electrolyte membrane fuel cell (PEMFC) degradation model and the study proposed the multi-objective fire dragonfly algorithm (MOFDA) technique. This strategy integrates the dragonfly algorithm and firefly algorithm, and the proposed equation incorporates the ameliorating factor as an exponential function for enhancement to deal with the system’s instability, which causes the HESS to age quickly. The comparison of the HESS with the proposed PEMFC model and the HESS model shows a huge improvement in the parameters that affect the lifespan predicted period. The research delves into the efficacy of a proposed technique that optimizes six benchmark trusses functions using MOFDA, a multi-objective algorithm. MOFDA’s performance is compared to that of seven other competitive multi-objective algorithms. The Friedman test indicates that each algorithm yields different results, and the MOFDA algorithm is the best overall solution, with both p-values being less than the significance value of α = 0.06. To ensure a fair evaluation, the population size and maximum number of iterations are both set to 100 and 200, respectively. Each multi-objective metaheuristic algorithm independently runs 30 times. The system is modeled and evaluated using MATLAB and Simulink to assess the performance of the study.