Coyote optimization algorithm to solve energy hub economic dispatch problem

Abstract

Regardless of energy type that we need today, it is important to use it efficiently and economically in the production, transmission and distribution stages. In line with the developing technology and needs, a new energy concept has emerged in which different energy types managed together in the past were managed independently. In this concept, energy infrastructures of more than one energy carrier such as electricity, gas and heat are met as Energy Hub (EH) to supply the demands such as electricity, gas, heating, cooling and compressed air by means of energy conversion, distribution and storage devices. EHs are expected to meet the demands energy with low operating costs. Energy hub economic dispatch problem (EHEDP) is a non-linear, non-convex, uniform and non-differential multidimensional optimization problem. In this study, the energy cost of the system is minimized by using the Coyote Optimization Algorithm (COA) for the solution of the EHEDP. The results obtained with COA have been compared with the results of heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Moth Swarm Algorithm (MSA) and Symbiotic Organisms Search Algorithm (SOS) in the literature. The compared results showed that COA performed better than other algorithms in solving EHED problem.