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Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations

Author

Listed:
  • Bao, Peng
  • Xu, Qingshan
  • Yang, Yongbiao
  • Nan, Yu
  • Wang, Yucui

Abstract

Amidst high penetration of renewable energy, virtual power plant (VPP) technology emerges as a viable solution to bolster power system controllability. This paper integrates a novel flexible load, 5G base stations (gNBs) with their backup energy storage systems (BESSs), into a VPP for power system real-time economic dispatch (RTED). Leveraging BESSs dispatchable capacity, the VPP offers power support and gains economic incentives, where the dispatchable capacity is estimated based on distribution network reliability and gNB availability requirements. Then, an efficient and refined VPP management strategy is proposed, where numerous gNBs are aggregated into limited number of virtual generators (VGs). The VPP dispatchable capacity can be efficiently and accurately evaluated via VGs states. Each VG is assigned a control cost function, enabling optimal control decisions resembling power system RTED, to minimize overall VPP control costs. Finally, a power support and incentive determination mechanism is presented based on the Leontief union bargaining model, ensuring reciprocal transactions between VPP and power system. To solve the Leontief model and address information privacy issues between the two parties, a distributed iterative algorithm is developed. Simulations based on actual data and IEEE 118-bus system illustrate that the proposed approach can smoothen intra-day load profiles, achieving mutual economic benefits for the power system and VPP without compromising the security of 5G network operations.

Suggested Citation

  • Bao, Peng & Xu, Qingshan & Yang, Yongbiao & Nan, Yu & Wang, Yucui, 2024. "Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261924000333
    DOI: 10.1016/j.apenergy.2024.122650
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