Performance analysis of autonomous green energy system based on multi and hybrid metaheuristic optimization approaches

• Published in: Energy conversion and management Vol. 269; p. 116058
• Main Authors: Fatih Güven, Aykut, Mahmoud Samy, Mohamed
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-10-2022
• Subjects: Biomass gasifier; Energy management; Fuel cell; Hybrid Firefly Genetic Algorithm; Hydrogen storage system; Microgrid design; Biomass gasifier; Hydrogen storage system; Hybrid Firefly Genetic Algorithm; Energy management; Fuel cell; Microgrid design
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2022.116058

•A hybrid Solar/Wind/Biomass/Fuel cell system for electricity and H2 production.•Techno-economic assessment of the suggested system.•The optimum hybrid system design for hourly varying load demands was examined.•H2 from electrolysis based on solar and wind power is a viable green alternative.•Hybrid Firefly Particle Swarm optimization.
The need for energy is constantly increasing worldwide. It is expected that renewable energy resources and fuel cells, solid wastes, and hydrogen energy technologies will play a significant role in energy generation and storage with their decreasing installation costs and increasing environmental concerns. Hydrogen is a potential fuel used in fuel cells that provides a carbon-free solution. The research is based on the techno-economics of off-grid wind (WT), solar (PV), biomass gasifier (BG), and fuel cell (FC) systems, which include storing excess wind and solar energy by converting it to hydrogen to provide fuel cell energy. It is critical from the standpoint of feasibility. In addition to electricity generation, energy storage integration, sizing methodologies, energy flow management, and the optimization algorithms that go with them are all covered. The annual cost system (ACS) is the primary goal function of the optimization process in this case. A robust rule-based energy management scheme is proposed to coordinate the power flow between various system components of a microgrid that will minimize the ACS and meet the energy demand reliably. The decision variables considered for this optimization process are PV panel power, WT power, and number of H2 tanks. The consumption and meteorological data of the year 2021 are used as the input of the system. The optimal size of each component is obtained by using the proposed Hybrid Firefly Genetic Algorithm (HFGA). To prove the superiority of the proposed optimization method in terms of accuracy and calculation time, it is compared to approaches that are used in similar applications such as Genetic Algorithms, the Firefly Algorithm, Sine-Cosine Algorithm and Cuckoo Search Algorithm. The optimum system configuration yielded 1094.68 KW WT, 2256.17 kW PV, and 775 H2 storage tanks when HFGA was used. The ACS of the system is $2921702.3, the total net present cost is $2.4639x107, while the leveled unit energy cost is $1.3416/kWh 'dir. The renewable energy fraction of the system is 100 % and consists of 51.8 % PV, 2.1 % WT, 11.8 % FC 34.3 % BG components. The results show that the proposed standalone hybrid (PV/WT/BG/FC) energy system is the most cost-effective option for the central campus of the university selected as the study area, and the proposed algorithm has excellent convergence capacity and provides high-quality outputs. The optimization algorithms are programmed using the MATLAB simulation package in this study.