부산대학교 도서관

Base station networks are a crucial component of fifth-generation communication systems. Faced with increasing traffic demands and energy consumption, connecting base stations to microgrids is essential for optimizing resource management within green base station networks and reducing their energy consumption and environmental impact. In this situation, existing methods for renewable energy base station resource management lack flexibility and intelligent optimization for energy trading involving multiple base stations. Therefore, this article proposes an energy trading method based on software-defined networking (SDN) and the nonlinear tangent perturbation–multiagent proximal policy optimization (NTP-MAPPO) algorithm that improves the economic efficiency and renewable energy utilization rate of multi-base-station microgrids. Specifically, we propose a reference scenario for energy trading within a multi-base-station microgrid based on SDN, and then model it using game theory to account for energy sharing among different base station microgrids. We express this model as a Markov decision process and solve it using the improved NTP-MAPPO algorithm, which offers an enhanced exploratory performance. Numerical analysis is subsequently used to validate the effectiveness of the proposed method.