Distributed BESS Scheduling for Power Demand Reshaping in 5G and Beyond Networks

Peng Qin1; Guoming Tang2; Yang Fu1; Yi Wang3

2023

10.1109/TGCN.2023.3332494

IEEE Transactions on Green Communications and Networking   影响因子和分区

2473-2400

2473-2400

The mobile network operators are upgrading their network facilities and shifting to the 5G era at an unprecedented pace. The huge operating expense (OPEX), mainly the energy consumption cost, has become the major concern of the operators. In this work, we investigate the energy cost-saving potential by transforming the backup batteries of base stations (BSs) to a distributed battery energy storage system (BESS). Specifically, to minimize the total energy cost, we model the distributed BESS discharge/charge scheduling as an optimization problem by incorporating comprehensive practical considerations. Then, considering the dynamic BS power demands in practice, we propose a multi-agent deep reinforcement learning (MADRL) based approach to make distributed BESS scheduling decisions in real-time. Particularly, QMIX framework is leveraged to learn the partial policy of each agent in the training phase; while in the execution phase, each BS can make scheduling decisions based on local information. The experiments using real-world BS deployment and traffic load data demonstrate that with our QMIX-based distributed BESS scheduling, the peak power demand charge of BSs can be reduced by more than 26.59%, and the yearly OPEX saving for 2282 5G BSs could reach up to U.S. $ 196,000.

5G cellular networks backup batteries BESS power demand reshaping

SCI ; EI

英语

其他

20234715088180

5G mobile communication systems ; Deep learning ; Digital storage ; Multi agent systems ; Personnel training ; Reinforcement learning ; Secondary batteries ; Wireless networks

Ergonomics and Human Factors Engineering:461.4 ; Secondary Batteries:702.1.2 ; Electric Power Systems:706.1 ; Radio Systems and Equipment:716.3 ; Data Storage, Equipment and Techniques:722.1 ; Data Communication, Equipment and Techniques:722.3 ; Artificial Intelligence:723.4 ; Personnel:912.4

2-s2.0-85177025301

IEEE

1.School of Electrical and Electronic Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China
2.National University of Defense Technology, Changsha, China
3.Sustech Institute of Future Networks, Southern University of Science and Technology, Shenzhen, China

Peng Qin,Guoming Tang,Yang Fu,et al. Distributed BESS Scheduling for Power Demand Reshaping in 5G and Beyond Networks[J]. IEEE Transactions on Green Communications and Networking,2023,PP(99):1-1.

Peng Qin,Guoming Tang,Yang Fu,&Yi Wang.(2023).Distributed BESS Scheduling for Power Demand Reshaping in 5G and Beyond Networks.IEEE Transactions on Green Communications and Networking,PP(99),1-1.

Peng Qin,et al."Distributed BESS Scheduling for Power Demand Reshaping in 5G and Beyond Networks".IEEE Transactions on Green Communications and Networking PP.99(2023):1-1.