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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1485135
This article is part of the Research Topic Advanced Data-Driven Uncertainty Optimization for Planning, Operation, and Analysis of Renewable Power Systems View all 6 articles

Coordinated Scheduling of 5G Base Station Energy Storage for Voltage Regulation in Distribution Networks

Provisionally accepted
Peng Sun Peng Sun Mengwei Zhang Mengwei Zhang *Hengxi Liu Hengxi Liu Yiming Dai Yiming Dai Qian Rao Qian Rao
  • Hunan University, Changsha, China

The final, formatted version of the article will be published soon.

    With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage resources often remain idle, leading to inefficiency. To enhance the utilization of base station energy storage (BSES), this paper proposes a co-regulation method for distribution network (DN) voltage control, enabling BSES participation in grid interactions. In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations. Secondly, a BSES aggregation model is constructed by using the power feasible domain maximal inner approximation method and Minkowski summation to evaluate the charging and discharging potential and adjustable capacity of BSES clusters. Subsequently, a BSES demand assessment and optimal scheduling model for low voltage regulation in DN is developed. This model optimizes the charging and discharging strategies of BSES to alleviate low voltage problems in DN. Finally, the simulation results effectively verify the feasibility of the proposed optimal scheduling method of BSES for voltage regulation in DN.

    Keywords: 5G base station energy storage, Aggregation, Distribution network, Voltage regulation, co-regulation

    Received: 23 Aug 2024; Accepted: 13 Sep 2024.

    Copyright: © 2024 Sun, Zhang, Liu, Dai and Rao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mengwei Zhang, Hunan University, Changsha, China

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