Abstract

Development of hybrid renewable energy system has become a challenging task considering the intermittencies of renewables and multi-dimensional designing aspects (for example technical and economic aspects) of such hybrid systems. Although there are many investigations on the development of hybrid renewable energy systems, the investigations of useful models or effective methods are only a few. Metaheuristic algorithms are gaining much popularity in the optimization of complex systems like hybrid renewable energy system for their ability to give quick, accurate, and optimal solution. The objective of the present study is to obtain a techno-economic optimal design of an off-grid hybrid solar photovoltaic/biogas generator/pumped hydro energy storage/battery system with the help of metaheuristic optimization techniques for a radio transmitter station in India. Performances of two such techniques – water cycle algorithm and moth-flame optimization that have become popular in recent time are evaluated and compared with Genetic Algorithm, which was used as a benchmark metaheuristic in previous studies on hybrid renewable energy system. The objective function is a minimization of total net present cost subject to design constraints. A detailed modeling strategy has been elaborated for the optimization problem. A sensitivity analysis based on loss of load probability type reliability criteria is carried out to investigate the feasibility of the proposed design. The results show that both the considered metaheuristics are effective in finding the optimal design; however, water cycle algorithm has marginally better design solution than the other two algorithms. The optimal configuration by the water cycle algorithm is found to have solar photovoltaic panel area of 548.67 m2 (size 69.2 kW), biogas generator size of 16 kW, battery bank of 21 units, converter size of 30 kW, and upper reservoir volume of 2081.5 m3 with a total net present cost of $0.813 million. Further, on comparing the results with literature, it is found that the present metaheuristic optimization has resulted in an effective hybrid renewable energy system design with a lower techno-economic cost.

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