Sustainable Green Energy Management: Optimizing Scheduling of Multi-Energy Systems Considered Energy Cost and Emission Using Attractive Repulsive Shuffled Frog-Leaping

Kumaran Kadirgama, Omar I. Awad (), M. N. Mohammed, Hai Tao and Ali A. H. Karah Bash
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Kumaran Kadirgama: Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
Omar I. Awad: Mechanical Engineering Department, College of Engineering, Gulf University, Sanad 26489, Bahrain
M. N. Mohammed: Mechanical Engineering Department, College of Engineering, Gulf University, Sanad 26489, Bahrain
Hai Tao: School of Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang 330099, China
Ali A. H. Karah Bash: Department of Electrical and Electronics Engineering, University of Gaziantep, Gaziantep 27310, Turkey

Sustainability, 2023, vol. 15, issue 14, 1-19

Abstract: As energy systems become increasingly complex, there is a growing need for sustainable and efficient energy management strategies that reduce greenhouse gas emissions. In this paper, multi-energy systems (MES) have emerged as a promising solution that integrates various energy sources and enables energy sharing between different sectors. The proposed model is based on using an Attractive Repulsive Shuffled Frog-Leaping (ARSFL) algorithm that optimizes the scheduling of energy resources, taking into account constraints such as capacity limitations and environmental regulations. The model considers different energy sources, including renewable energy and a power-to-gas (P2G) network with power grid, and incorporates a demand–response mechanism that allows consumers to adjust their energy consumption patterns in response to price signals and other incentives. The ARSFL algorithm demonstrates superior performance in managing and minimizing energy purchase uncertainty compared to the particle swarm optimization (PSO) and genetic algorithm (GA). It also exhibits significantly reduced execution time, saving approximately 1.59% compared to PSO and 2.7% compared to GA.

Keywords: renewable energy; electricity-to-gas technology; optimal dispatch; multi-energy system; energy hub (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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