Analysis of the performance of the multi-objective hybrid hydropower-photovoltaic-wind system to reduce variance and maximum power generation by developed owl search algorithm

Xiaojun Ren, Yongtang Wu, Dongmin Hao, Guoxu Liu and Nicholas Zafetti

Energy, 2021, vol. 231, issue C

Abstract: The use of renewable resources to generate energy is one of the most important policy goals in the energy sector. One method to use renewable resources is to apply integrated systems. In this study, an optimal multi-objective integrated system has been applied to power generation. The proposed system includes wind turbines, hydroelectric power plants, and photovoltaic systems. To achieve maximum power generation with minimum fluctuations, a Developed Owl search algorithm (DOSA) with three pareto front solutions has been used. Also, the efficiency of the integrated system in different climate conditions is evaluated. The results of the pareto front show that the developed owl search algorithm is the closest algorithm to the pareto front. Therefore, it is the most accurate to improve the overall multi-objective integrated system. Among pareto front solutions, the second solution provides acceptable results for increasing power generation and reduction of fluctuations. Also, the results of the evaluation of the efficient integrated system in different climate conditions show that in wet conditions when solar radiation and wind are reduced, hydropower causes current to continue in the system by generating electricity and compensating for wind and PV energy loss.

Keywords: Renewable sources; Hydropower; Wind energy; Optimization algorithms; Photovoltaic (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:231:y:2021:i:c:s0360544221011580

DOI: 10.1016/j.energy.2021.120910

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