Optimal sizing of stand-alone photovoltaic system by minimizing the loss of power supply probability. (1st July 2017)

• Record Type: Journal Article
• Authors: Abdul Aziz, Nur Izzati; Sulaiman, Shahril Irwan; Shaari, Sulaiman; Musirin, Ismail; Sopian, Kamaruzzaman
• Is Part Of: Solar energy. Volume 150(2017)
• Page Start: 220
• Page End: 228
• Publication Date: 2017-07-01
• Subjects: Firefly algorithm (FA) -- Stand-alone photovoltaic (SAPV) -- Sizing -- Optimization -- Loss of power supply probability (LPSP); Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2017.04.021
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: ISA had facilitated the sizing process when dealing with numerous set of system components. Incorporation of FASA in sizing algorithm had improved overall computation time when compared to ISA. FASA required minimum number of generations for convergence when compared to other CI-based sizing algorithms. FASA produced minimum computation time with least number of population when compared to other CI-based sizing algorithms. FASA is at least 1.93 times faster than PSO, EP, and GA in achieving the optimal solution for each design case. Abstract: This paper presents Firefly Algorithm-based Sizing Algorithm (FASA) for sizing optimization of a Stand-Alone Photovoltaic (SAPV) system. Firefly Algorithm (FA) was used to optimally select the model of each system component such that a technical performance indicator is consequently optimized. Prior to implementation of FASA, an Iterative-based Sizing Algorithms known as ISA had been developed to determine the optimal solutions which were used as benchmark for FASA. Although ISA was capable in determining the optimal design solutions when there are numerous models for each system component being considered, the computation time of ISA can be very long as ISA tested every possible combination of PV module, battery, charge controller and inverter during sizing process. Therefore, FASA was introduced to accelerate the sizing optimization for SAPV system. FA was incorporated into sizing algorithm with the technical performance indicator was set to optimize the Loss of Power Supply Probability (LPSP). Besides that, two design cases of PV-battery system, i.e. system with standard charge controller denoted as Case 1 and system with MPPT-based charge controller denoted as Case 2 were investigated. The results showed that FASA had successfully found the optimal LPSP in all design cases. In addition, sizing algorithm with FA was also discovered to outperform sizing algorithm with selected computational intelligence in producing the lowest computation time in the sizing optimization.