Optimal Design of a Grid-Independent Solar-Fuel Cell-Biomass Energy System Using an Enhanced Salp Swarm Algorithm Considering Rule-Based Energy Management Strategy

• Published in: IEEE access Vol. 12; pp. 23914 - 23929
• Main Authors: Modu, Babangida, Abdullah, Md. Pauzi Bin, Alkassem, Abdulrahman, Garni, Hassan Z. Al, Alkabi, Mishaal
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; Biomass; Biomass energy; Biomass energy production; Costs; Design optimization; Energy management; energy management strategy; Energy storage; fuel cell; Fuel cells; Genetic algorithms; Heuristic methods; Hybrid systems; Hydrogen; Hydrogen fuels; Load fluctuation; Microgrids; Optimization; Optimization techniques; Photovoltaic systems; Renewable energy; Renewable resources; rule-based; Solar energy; Solar PV; Trigonometric functions
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3362241

This paper presented an optimal design of a grid-independent hybrid renewable energy system (HRES) that comprises Photovoltaic, Biomass, Hydrogen Fuel Cell, and battery storage. Renewable energy-based system have been endorsed for remote off-grid communities electrification. However, it is difficult to design an optimal hybrid energy system due to the stochastic resource nature, load variation, and high cost of renewable components. The sizing of components for the proposed HRES is determined through the application of an innovative metaheuristic optimization technique called salp swarm algorithm (SSA). Addressing the limitations of the salp swarm algorithm, which include low precision, optimization dimension and convergence rate, a modified version of the salp swarm algorithm (SSA), known as the Levy and sine cosine operator-based (LSC-SSA), was introduced. The proposed algorithm is compared with standard SSA and Genetic Algorithm (GA). The primary goal of the research is to reduce the annualized cost of the hybrid system, whilst taking into account the reliability constraint. The novelty of this research lies in its approach to enhance the performance of a HRES by optimizing its size and energy management strategy (EMS). It is achieved by employing a combined framework that integrate the proposed LSC-SSA into the supervisory EMS. The potential benefits of this approach include reducing the cost of energy and the annualized system cost. The comparative results validate that the LSC-SSA surpasses the standard SSA, and GA algorithms examined, by realizing significant cost reductions amounting to <inline-formula> <tex-math notation="LaTeX">{\} </tex-math></inline-formula>82,023, and <inline-formula> <tex-math notation="LaTeX">{\} </tex-math></inline-formula>202,127 respectively. Additionally, the outcome indicates that, the LSC-SSA offers the least cost of energy (COE) of <inline-formula> <tex-math notation="LaTeX">{\} </tex-math></inline-formula>0.927/kWh, in comparison with the COE values of <inline-formula> <tex-math notation="LaTeX">{\} </tex-math></inline-formula>0.931/kWh for SSA and <inline-formula> <tex-math notation="LaTeX">{\} </tex-math></inline-formula>0.949/kWh for GA, which are higher. Furthermore, the results indicate that the applied supervisory EMS has effectively assisted the establishing an eco-friendly and economical energy system.