Multi-time scale energy management strategy based on MPC for 5G base stations considering backup energy storage and air conditioning

Best Paper Award in the 2023 International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE 2023)
The increasing development of 5G technology has focused attention on the energy consumption of its base stations. As a result, it is crucial to establish energy-efficient 5G networks and reduce the operating costs associated with 5G base stations. In this paper, a multi-time-scale energy management strategy based on model predictive control (MPC) is proposed to achieve this aim. Firstly, a 5G base station model that takes into account several factors is established, including backup energy storage, inverter air conditioning scheduling potential, photovoltaic output fluctuations, load, and temperature. Secondly, a day-ahead optimal economic dispatch model for minimizing operational costs is developed. Thirdly, an intraday rolling optimization strategy based on MPC to dynamically adjust the day-ahead operation scheme is proposed. Finally, comprehensive case studies are carried out, which indicate that the proposed strategy can effectively improve the robustness and economy of the system.
Peer Reviewed
Award-winning
Postprint (published version)