Optimal Size Of Photovoltaic Pumping System Using Nature-Inspired Algorithms

In this article, an optimal design model using metaheuristic techniques to overcome such difficult optimization problems has been proposed. For this purpose, various well-known metaheuristic algorithms have been used, namely: ant lion optimizer (ALO), bat algorithm (BA), cuckoo search algorithm (CSA), and gray wolf optimization algorithm (GWO). To provide optimal system configurations, an objective function based on the total net present cost (TNPC) has been specified while taking into account a number of constraints, including the loss of power supply probability (LPSP). The effectiveness of the used algorithms in solving such problem is assessed and their performances compared with each other. To assess the suggested model's effectiveness, a case study has been conducted to investigate a stand-alone photovoltaic water pumping system (PVWPS), designed to supply water to a one-hectare field of date palm trees. This field of an oasis located in the Ghardaia region of Algeria consists of 130 date palm trees spaced 9 m apart. The simulation results show the effectiveness of the GWO algorithm over the other algorithms. GWO converges toward the optimal solutions for different heads and all reliability levels required by the customer. Another aspect, supported by the developed methodology, is the accounting of the excess water volume. It has been found that the relative surplus is inversely proportional to the PVWPS reliability level. For a head of 30 m, the relative excess is estimated at 120% for LPS0%, while it is 27% for LPSP of 10%. The same observations are made for the other heads.