Two-Stage Scheduling Strategy of 5G Base Station Energy Storage Based on Mathematical Optimization and Improved Decision Transformer

With the rapid development of 5G communication, a large number of base stations with storage units have been built, and the energy storages of base stations have significant potential for utilization. Studying reasonable scheduling algorithms to efficiently integrate these units into grid demand response is of great importance. This paper focuses on the stochastic environment in which 5G base station energy storages interact with power grid scheduling and proposes a two-stage scheduling framework. This framework consists of day-ahead mathematical optimization and intra-day reinforcement learning correction to reduce the impact of uncertainty on the scheduling algorithms and enhance their adaptability to environmental randomness. Additionally, to improve the decision-making effectiveness of the intra-day correction algorithm, an algorithm named Time2Vec-Decision Transformer (T2V-DT) is proposed. This algorithm improves the extraction of time features, enhances the quality of actions, and optimizes the overall decision-making performance. A series of experiments have validated the effectiveness of the proposed two-stage framework and algorithm model. The proposed strategy offers an effective solution for 5G base station energy storages to participate in power grid scheduling, fully utilizing the advantages of energy storages.