PSO-Based Smart Grid Application for Sizing and Optimization of Hybrid Renewable Energy Systems

• Published in: PloS one Vol. 11; no. 8; p. e0159702
• Main Authors: Mohamed, Mohamed A., Eltamaly, Ali M., Alolah, Abdulrahman I.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.08.2016; Public Library of Science (PLoS)
• Subjects: Adequacy; Algorithms; Alternative energy sources; Analysis; Batteries; Computer Simulation; Conservation of Energy Resources; Earth Sciences; Electric generators; Electric Power Supplies - standards; Electrical engineering; Energy consumption; Energy management; Energy resources; Energy storage; Engineering and Technology; Fossil fuels; Hybrid systems; Hydrogen; Mathematical problems; Minimum cost; Models, Theoretical; Neural networks; Optimization; Optimization techniques; Optimization theory; Particle swarm optimization; People and places; Photovoltaics; Physical Sciences; Renewable energy; Renewable Energy - standards; Renewable resources; Reproducibility of Results; Research and Analysis Methods; Researchers; Saudi Arabia; Sizing; Smart grid; Solar energy; Storage batteries; Swarm intelligence; Turbines; Wind energy; Wind power; Wind turbines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0159702

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers.