计及计算资源自适应调整的数据中心算力-电力协同调度OA

Currently,leveraging the transferable features of computation tasks in data centers to enhance quality of service and reduce operating costs has become a significant development direction in data center operations.However,as major energy consumers,the operation modes of data centers significantly impact the safe operation of distribution networks.Therefore,focusing on the coordinated scheduling of data center clusters and distribution networks,the computation task transfer is integrated with power system operation to construct a hierarchical reinforcement learning model.The model adopts an adaptive adjustment mechanism for computing resources and establishes a hierarchical coordinated framework that incorporates economic dispatch on the electric-power side and task transfer on the computing-power side.On this basis,a hierarchical multi-agent reinforcement learning algorithm based on abstract instructions is designed to generate power exchange instructions between the distribution network and data centers,task transfer commands,and computing resource scheduling strategies.Simulation results demonstrate that the proposed method can effectively reduce system economic costs,enhance renewable energy accommodation capability,significantly decrease execution violations of computation tasks,and improve overall quality of service.