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Cooperative game-based solution for power system dynamic economic dispatch considering uncertainties: A case study of large-scale 5G base stations as virtual power plant

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Listed:
  • Bao, Peng
  • Xu, Qingshan
  • Yang, Yongbiao
  • Zhao, Xianqiu

Abstract

The uncertainty of renewable energy necessitates reliable demand response (DR) resources for power system auxiliary regulation. Meanwhile, the widespread deployment of energy-consuming 5G base stations (gNBs) drives internet service providers (ISPs) to seek energy expenses reduction. This paper intelligently addresses these two complementary needs by integrating gNBs into the power system dynamic economic dispatch (DED). To achieve this, a combined DED model that incorporates both the power system and 5G communication network is developed, where numerous distributed gNBs and their backup energy storage systems (BESSs) are integrated as a virtual power plant (VPP) to offer power support and obtain economic incentives. Instead of traditional optimization methods, the combined DED model is solved by a cooperative game-based solution method in a benefit-balanced manner, facilitating the power system and 5G network to achieve a win-win DED solution. This method comprises a two-stage game model and a solution algorithm based on improved random drift particle swarm optimization (RDPSO). The improved RDPSO constructs an uncertainty set in each iteration, facilitating its solution of stochastic optimization involving power system uncertainties. Lastly, a VPP management function is developed within the 5G core network (5GC) to efficiently evaluate the VPP’s dispatchable capacity and formulate optimal control policies that can minimize control costs. Simulations based on the IEEE 118-bus system and real device data demonstrate that the proposed method can simultaneously reduce the power system’s operating costs and increase the ISP’s economic revenue.

Suggested Citation

  • Bao, Peng & Xu, Qingshan & Yang, Yongbiao & Zhao, Xianqiu, 2024. "Cooperative game-based solution for power system dynamic economic dispatch considering uncertainties: A case study of large-scale 5G base stations as virtual power plant," Applied Energy, Elsevier, vol. 368(C).
  • Handle: RePEc:eee:appene:v:368:y:2024:i:c:s0306261924008468
    DOI: 10.1016/j.apenergy.2024.123463
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    References listed on IDEAS

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    1. Bao, Peng & Xu, Qingshan & Yang, Yongbiao, 2024. "Modeling and aggregated control of large-scale 5G base stations and backup energy storage systems towards secondary frequency support," Applied Energy, Elsevier, vol. 357(C).
    2. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    3. Hlalele, Thabo G. & Zhang, Jiangfeng & Naidoo, Raj M. & Bansal, Ramesh C., 2021. "Multi-objective economic dispatch with residential demand response programme under renewable obligation," Energy, Elsevier, vol. 218(C).
    4. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    5. Basu, M., 2021. "Fuel constrained dynamic economic dispatch with demand side management," Energy, Elsevier, vol. 223(C).
    6. Zhao, Bo & Ren, Junzhi & Chen, Jian & Lin, Da & Qin, Ruwen, 2020. "Tri-level robust planning-operation co-optimization of distributed energy storage in distribution networks with high PV penetration," Applied Energy, Elsevier, vol. 279(C).
    7. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
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