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Enhancing dynamic energy network management using a multiagent cloud-fog structure

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  • Haghnegahdar, Lida
  • Chen, Yu
  • Wang, Yong

Abstract

Smart Grid benefits from information and communications technology (ICT) to integrate data from different sources across the network. The growing market for electric vehicles (EV) and electric devices is increasing energy demand. To manage a large amount of complex intelligent equipment, EV charging and discharging, and data-intensive devices, a reliable modern smart grid with a service-oriented, secure, efficient, and cost-effective system is compelling. Cloud computing is a promising approach to achieve that objective as monitoring power grid data flow and data center management are the key. This paper proposes an optimization model for energy management within the power cloud. Our energy system model enables accessible services to computing resources and real-time data stream processing within an integrated environment. Using digital technology, the proper implementation of our model reduces costs and energy consumption and improves the smart grid reliability for customers and energy providers in a distributed manner. This paper presents the implementation of a three-procedure optimization algorithm within a novel Multi-Agent Cloud-fog Structure (MACS) to meet the requirements raised by smart grid communication and distribution. Our model promotes reliable energy consumption adjustments by end-users who can choose power supplied by solar, wind, geothermal, biomass, or other renewable sources or from non-renewable sources in ways that reveal opportunities for demand-supply balance and energy saving.

Suggested Citation

  • Haghnegahdar, Lida & Chen, Yu & Wang, Yong, 2022. "Enhancing dynamic energy network management using a multiagent cloud-fog structure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:rensus:v:162:y:2022:i:c:s1364032122003458
    DOI: 10.1016/j.rser.2022.112439
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    References listed on IDEAS

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    1. Jiajun Zhou & Xifan Yao, 2017. "A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4765-4784, August.
    2. Adia Khalid & Sheraz Aslam & Khursheed Aurangzeb & Syed Irtaza Haider & Mahmood Ashraf & Nadeem Javaid, 2018. "An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid," Energies, MDPI, vol. 11(12), pages 1-17, December.
    3. Yang-Kuei Lin & Chin Soon Chong, 2017. "Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1189-1201, June.
    4. Chetan M. Bulla & Mahantesh N. Birje, 2021. "A Multi-Agent-Based Data Collection and Aggregation Model for Fog-Enabled Cloud Monitoring," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 11(1), pages 73-92, January.
    5. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    6. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    7. Feng Xiang & Yefa Hu & Yingrong Yu & Huachun Wu, 2014. "QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 663-685, December.
    8. Rasool Bukhsh & Nadeem Javaid & Zahoor Ali Khan & Farruh Ishmanov & Muhammad Khalil Afzal & Zahid Wadud, 2018. "Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid," Energies, MDPI, vol. 11(12), pages 1-21, November.
    9. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
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    Cited by:

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    2. Zhang, Hongyan & Gao, Shuaizhi & Zhou, Peng, 2023. "Role of digitalization in energy storage technological innovation: Evidence from China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).

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