电力系统自动化2026，Vol.50Issue(17)：96-114,19.DOI:10.7500/AEPS20251113004
基于加速深度强化学习的多数据中心算-电资源协同优化调度
Coordinated Optimal Scheduling of Computing Power-Electric Power Resources in Multi-data Centers Based on Accelerated Deep Reinforcement Learning
摘要
Abstract
To improve the participation capability and scheduling efficiency of multiple geographically dispersed data centers in electricity markets,this paper addresses the issues of excessively high dimensionality of action spaces and slow training convergence faced by end-to-end deep reinforcement learning(DRL)when simultaneously considering multi-data-center coordination,quality-of-service constraints,and photovoltaic-energy storage coordination.A data center aggregator(DCA)model is constructed,and a two-layer decision coordinated scheduling framework oriented to computing power-electric power coupling is proposed.By integrating multiple small-scale data centers managed by a single operator,the DCA model optimizes resource management and workload scheduling,and strengthens their competitiveness in dynamic electricity markets.The proposed model adopts a two-layer solution architecture combining DRL and mixed-integer linear programming(MILP).The upper-layer DRL agent handles far-sighted actions that affect future energy states,while the lower-layer MILP solver optimizes myopic actions related to immediate costs,realizing coordinated optimization of long-term strategies and real-time decisions.Simulation results demonstrate that compared with full-action-space DRL,the total cost of the proposed model is reduced by approximately 42.55%,and the training time is shortened to the minute level.In contrast to linear optimization methods,the proposed approach further decreases electricity purchase costs and total comprehensive costs in diverse workload scenarios.The simulation results also verify the impacts of various factors including workload types,energy storage capacity and quality-of-service requirements on scheduling,which reflects the robustness and universality of the proposed method.关键词
数据中心/聚合体/深度强化学习/混合整数线性规划/调度/算-电协同/电力市场Key words
data center/aggregator/deep reinforcement learning(DRL)/mixed-integer linear programming(MILP)/scheduling/computing power-electric power coordination/electricity market引用本文复制引用
董亚晗,王旭,夏威,龚开,柯君,蒋传文..基于加速深度强化学习的多数据中心算-电资源协同优化调度[J].电力系统自动化,2026,50(17):96-114,19.基金项目
国家自然科学基金资助项目(52577122) （52577122）
国家重点研发计划资助项目(2023YFE0119800).This work is supported by National Natural Science Foundation of China(No.52577122)and National Key R&D Program of China(No.2023YFE0119800). （2023YFE0119800）
