Deep Reinforcement Learning Based Collaborative Energy Management for Base Station and Microgrid

With the rapid expansion of 5G networks, the number of base stations and their energy consumption have significantly increased, making energy efficiency a critical challenge. To address this issue, this paper proposes a collaborative energy management model for 5G base stations and microgrids. By introducing the FL-PPO algorithm, the model achieves collaborative optimization while protecting base station data privacy. The base stations utilize the FL-PPO algorithm to implement dynamic sleep strategies, effectively reducing energy consumption, while the microgrid adopts the Dueling DQN algorithm for energy management, enhancing operational efficiency. Experimental results demonstrate that the proposed method significantly reduces base station energy consumption while increasing microgrid revenue.