Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia

Abstract

This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO ( A W P S O c f ) and sigmoid function PSO ( S F P S O c f ) , are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed A W / S F P S O c f methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of S F P S O c f in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of A W / S F P S O c f methods is verified by using the iterative method.

Optimum Design of a Standalone Solar Photovoltaic System Based on Novel Integration of Iterative-PESA-II and AHP-VIKOR Methods

“…Meanwhile, other studies used heuristic techniques in order to find the optimal size of a SAPV system using techno-economic objective functions such as artificial bee colony (ABC) [41], genetic algorithm (GA) [42,43], generalized regression neural network (GRNN) [44], firefly (FL) [45], and particle swarm optimization (PSO) [46]. The main advantage of these searching algorithms is the ability to converge the optimal solution in a short time.…”

“…In addition to that, some of the studies combined two or more in order to enhance the converge to the optimal values and reduce the execution time, such as the research works presented in [43,47,48]. Hussein et al [46] proposed an optimal design of the SAPV system using multi-objective particle swarm optimization (MOPSO) in Malaysia. Two variants of the PSO algorithm were presented with reference to a sigmoid function PSO (SFPSO c f ) and adaptive weights PSO (AWPSO c f ) using techno-economic criteria.…”

“…The performance results demonstrated that the SFPSO c f algorithm has a trivial superiority in selecting an optimal configuration in terms of accuracy. However, the main demerit of the method proposed in reference [46] the use of a non-scale (NS) approach that can conduct only one solution on the basis of given weights. In addition, the proposed algorithm cannot cover all the Pareto front (PF) solution when all possible status of weights within [0,1] are considered.…”

“…In this work, the hourly load demand of a typical house in a rural area in Malaysia was considered, which can be found in details in the load demand in [46]. The proposed hybrid method was analyzed using hourly meteorological data for one year.…”

“…The proposed hybrid method was analyzed using hourly meteorological data for one year. Furthermore, the data used was taken from Subung meteorological station in Subung Jaya, Klang Valley, Malaysia (latitude 3.12 • north and longitude 101.6 • east) [46].…”