Introducing an Approach Utilizing the Gray Wolf Optimization Algorithm for Optimal Energy Management in Renewable Energy Systems

Vafajoo, Mohammad; Khodaparast, Ayoub

doi:10.22034/ijcoe.2024.447400.1065

Introducing an Approach Utilizing the Gray Wolf Optimization Algorithm for Optimal Energy Management in Renewable Energy Systems

Articles in Press

Document Type : Original Research Article

Authors

mohammad vafajoo ¹

Ayoub khodaparast ²

¹ malek ashtar university of technology

² Malek ashtar university of technology

10.22034/ijcoe.2024.447400.1065

Abstract

Energy has been a central focus throughout human history, and currently, with the imperative for sustainable development, there is a global increase in the utilization of renewable energy sources. The adoption of renewable energy is considered a key indicator of a nation's progress in energy consumption. Three major factors contribute to the growing market interest in renewable energies. Firstly, there is a concern for national energy security as oil consumption is on the rise and expected to surpass domestic production levels soon. This has prompted a shift towards alternative energy sources such as hydropower, wind, solar, and geothermal power, encouraging large-scale power generation. This article explores the development of an optimal energy management program, considering renewable energy sources like wind power plants and photovoltaic panels. The optimization process employs meta-heuristic algorithms, specifically genetics (multi-objective) and gray wolf algorithms. The results of network analysis reveal that in the initial state, certain buses exhibit a relatively large numerical value for the sensitivity coefficient, while others show a smaller value. The sensitivity coefficient indicates the degree of dependence and sensitivity of a bus on losses. Buses with high sensitivity coefficients are identified as potential candidates for installing Distributed Generators (DGs) to improve overall system efficiency.

Keywords

Energy management

renewable energy

air storage system

meta-heuristic algorithms

Generators

Subjects