Improved owl search algorithm for optimal capacity determination of the gas engine in a CCHP system using 4E analysis

• Published in: International transactions on electrical energy systems Vol. 30; no. 10
• Main Authors: Cao, Ziqi, Kui, Deng, Ashourian, Mohsen
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.10.2020
• Subjects: 4E analysis; Algorithms; combined cooling heating and power; Commercial buildings; Economic analysis; gas engine; Gas turbines; genetic algorithm; Genetic algorithms; improved owl search algorithm; Industrial buildings; Machine learning; optimal sizing; Optimization; Search algorithms; Sizing
• ISSN: 2050-7038; 2050-7038
• DOI: 10.1002/2050-7038.12552

This study presents a new technique for optimal sizing of the gas turbine as the primary mover of a combined cooling heating and power (CCHP) system in a selected commercial building. Because of the vital effect of four substantial parameters including energetic, energetic, economic, and environmental (4E analysis), they used for optimum sizing of the gas engine for the CCHP system. The optimization process has been performed by a newly developed meta‐heuristic, called Opposition‐Based Learning and Lévy flight owl search algorithm (OLOSA) for an industrial building in Iran. During the optimization, eight constraints from the 4E analysis have been considered. Final results of the proposed OLOSA have been compared with genetic algorithm (GA) and the results declare that the optimal size for the gas engine based on OLOSA and GA for the cost value are 0.1923 and 0.5622, respectively and the optimal value of the gas engine is achieved 90.16 kW and is achieved after 9 generations and 39 generations, respectively which shows the excellence of the presented OLOSA toward GA in both terms of accuracy and convergence.