An improved particle swarm optimization for optimal configuration of standalone photovoltaic scheme components. Issue 3 (17th January 2022)

• Record Type: Journal Article
• Authors: Liu, Hao; Wu, Baojia; Maleki, Akbar; Pourfayaz, Fathollah
• Is Part Of: Energy science & engineering. Volume 10:Issue 3(2022)
• Page Start: 772
• Page End: 789
• Publication Date: 2022-01-17
• Subjects: improved particle swarm optimization -- optimal configuration -- reliability -- standalone photovoltaic system; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.1052
• Languages: English
• ISSNs: 2050-0505

Abstract

Abstract: The hybrid energy system design problem needs efficacy tools to reach optimal results in remote areas. The metaheuristic optimization methods are the best choice to address complex problems. This article presents an improved approach based on an energy management strategy for optimal sizing and configuration of standalone photovoltaic scheme components. Improved particle swarm optimization for optimization and configuration of photovoltaic panel and battery system is applied using MATLAB and hourly solar radiation, ambient temperature data, and load demand. The objective of the optimization problem is to define the optimal sizing of the system components to meet the load demand to compose a cost‐effective and reliable scheme. The optimized system results from the improved approach are compared by original particle swarm optimization and simulated annealing algorithms. So, the optimum design of the hybrid scheme is analyzed and compared based on different reliability values. The results show that the improved approach finds the optimal results easily with lower cost, faster convergence, and better reliability indexes in different reliability indexes in comparison with the other approaches. In this regard, for different reliability values (1%, 3%, and 5%), the improved particle swarm optimization shows approximately 22.9% cost saving in comparison with the simulated annealing, and improved particle swarm optimization shows approximately 0.35% cost saving in comparison with the particle swarm optimization. Also, show the reliability of the standalone hybrid photovoltaic system. Abstract : A new algorithm for optimal design of the standalone photovoltaic system to meet the load demand in a remote area.