An optimal size selection of hybrid renewable energy system based on Fractional-Order Neural Network Algorithm: A case study

Autor: Lei Zhou, Qun Guo, Babak Daneshvar Rouyendegh, Xinghua Guo
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Energy Reports, Vol 7, Iss, Pp 7261-7272 (2021)
ISSN: 2352-4847
Popis: This paper provides a new technique for techno-economic analysis of an off-grid hybrid renewable energy system (HRES). In this study, a photovoltaic (PV) system has been utilized as a primary mover of the HRES which uses a Proton Exchange Membrane Fuel Cells (PEMFC) system as a backup system. Also, H 2 storage tank and Electrolyzer (EL) are utilized for supplying the PEMFC. The system has been designed to provide an optimum size selection for the HRES components with considering a suitable total Net Present Cost and loss of power supply probability (LPSP). To get the best results, a new improved metaheuristic, called Fractional-Order Neural Network Algorithm (FONNA) has been utilized for the optimization. The designed system was then applied to a rural building in Yuli County, China. To analyze the system performance, a sensitivity analysis based on the cost variation of the PV, FC, H 2 storage tanks and EL is assessed. Simulations show that by using the suggested FONNA, 2.49% LPSP and 5.49% PEE, that will be achieved by selecting 45 ELs, 20 FCs, 25 PVs, and 35 H 2 storage tanks. Final results indicate that the suggested approach provides an efficient HRES for use in the studied location.
Databáze: OpenAIRE
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