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Aggregation and scheduling of massive 5G base station backup batteries using a price-guided orientable inner approximation method

Author

Listed:
  • Li, Kun
  • Fang, Jiakun
  • Ai, Xiaomeng
  • Wang, Shengshi
  • Liu, Jingguan
  • Cui, Shichang
  • Yao, Wei
  • Wen, Jinyu

Abstract

5G base station backup batteries (BSBs) are promising power balance and frequency support resources for future low-inertia power systems with substantial renewable penetrations. The challenge, however, is to properly incorporate massive 5G BSBs into frequency-constrained unit commitment (FC-UC). To this end, this paper proposes a price-guided orientable inner approximation (OIA) method to solve FC-UC with massive BSBs through aggregation. A polytope-based OIA technique is first proposed to realize the oriented inner approximation of BSBs' original feasible regions (FRs). Marginal prices are then employed to indicate the inner approximation orientations, thus ensuring the obtained OIA-FRs align with the power system's operation demands. Finally, an interruptible iterative framework with the proposed price-guided OIA method is developed to enhance FC-UC's optimality through iterations, which allows termination at any iteration while ensuring superior optimality compared to the traditional maximum inner approximation method. Case studies on the IEEE 30-bus system demonstrate the economic benefits of incorporating BSBs into operations and validate the effectiveness of the price-guided OIA method in enhancing FC-UC's optimality and handling renewables' uncertainty. Additional studies on a provincial system verify the practicality and scalability of the proposed interruptible iterative framework.

Suggested Citation

  • Li, Kun & Fang, Jiakun & Ai, Xiaomeng & Wang, Shengshi & Liu, Jingguan & Cui, Shichang & Yao, Wei & Wen, Jinyu, 2025. "Aggregation and scheduling of massive 5G base station backup batteries using a price-guided orientable inner approximation method," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024139
    DOI: 10.1016/j.apenergy.2024.125029
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    References listed on IDEAS

    as
    1. Li, Kun & Wei, Lishen & Fang, Jiakun & Ai, Xiaomeng & Cui, Shichang & Zhu, Mengshu & Wen, Jinyu, 2024. "Incentive-compatible primary frequency response ancillary service market mechanism for incorporating diverse frequency support resources," Energy, Elsevier, vol. 306(C).
    2. N. Dinh & V. Jeyakumar, 2014. "Rejoinder on: Farkas’ lemma: three decades of generalizations for mathematical optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 41-44, April.
    3. Cui, Xueyuan & Liu, Shu & Ruan, Guangchun & Wang, Yi, 2024. "Data-driven aggregation of thermal dynamics within building virtual power plants," Applied Energy, Elsevier, vol. 353(PB).
    4. Zhang, Menglin & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2018. "A systematic approach for the joint dispatch of energy and reserve incorporating demand response," Applied Energy, Elsevier, vol. 230(C), pages 1279-1291.
    5. N. Dinh & V. Jeyakumar, 2014. "Farkas’ lemma: three decades of generalizations for mathematical optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 1-22, April.
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