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

Author

Listed:
  • 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
    Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China
    Engineering College, University of Kirkuk, Kirkuk 36001, Iraq)

  • 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)

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.

Suggested Citation

  • Kumaran Kadirgama & Omar I. Awad & M. N. Mohammed & Hai Tao & Ali A. H. Karah Bash, 2023. "Sustainable Green Energy Management: Optimizing Scheduling of Multi-Energy Systems Considered Energy Cost and Emission Using Attractive Repulsive Shuffled Frog-Leaping," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10775-:d:1190238
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    References listed on IDEAS

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    Cited by:

    1. Aleksei Vakhnin & Ivan Ryzhikov & Christina Brester & Harri Niska & Mikko Kolehmainen, 2024. "Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland," Energies, MDPI, vol. 17(12), pages 1-32, June.
    2. Ningning Cui & Emmanuel Nketiah & Xiaoyu Ma, 2023. "Do Green Energy and Information Technology Influence Greenhouse Gas Emitting Countries to Attain Sustainable Development?," Sustainability, MDPI, vol. 15(18), pages 1-19, September.

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