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

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

Applied Energy, 2025, vol. 380, issue C, No S0306261924024139

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.

Keywords: Inner approximation; Aggregation; 5G base station backup battery; Marginal price; Iterative framework (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.125029

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