BESS Aided Renewable Energy Supply using Deep Reinforcement Learning for 5G and Beyond

Hao Yuan, Guoming Tang, Deke Guo, Kui Wu, Xun Shao, Keping Yu, Wei Wei

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become significant concerns for the mobile operators. As the continuous decline of the renewable energy cost, equipping the power-hungry BSs with renewable energy generators could be a sustainable solution. In this work, we propose an energy storage aided renewable energy supply solution for the BS, which could supply clean energy to the BS and store surplus energy for backup usage. Specifically, to flexibly regulate the battery’s discharging/charging, we propose a deep reinforcement learning based regulating policy, which can adapt to the dynamical renewable energy generations as well as the varying power demands. Our experiments using the real-world data on renewable energy generations and power demands demonstrate that, our power supply solution can achieve an cost saving ratio of 77.9%, compared to the case with traditional power grid supply.

Original languageEnglish
JournalIEEE Transactions on Green Communications and Networking
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • 5G base stations
  • BESS
  • deep reinforcement learning.
  • renewable energy supply

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications

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