综合智慧能源2026，Vol.48Issue(8)：107-116,10.DOI:10.3969/j.issn.2097-0706.2026.08.009
基于主从博弈的多数据中心与电网协同优化运行方法
Stackelberg game-based coordinated optimal operation method for multiple data centers and power grids
摘要
Abstract
With the advancement of the"East Data,West Computing"project and the accelerated construction of new power systems,data centers have become rapidly growing energy-intensive loads in power systems.Their flexible regulation capability is expected to provide low-cost flexible response resources for the power grid.However,existing research has not fully addressed two critical challenges:the game of interests between the grid operator and multiple data centers,and data privacy protection in multi-agent collaboration,thereby hindering the engineering application of relevant theoretical results.To address these challenges,a bi-level distributed optimization method integrating Stackelberg game theory with an adaptive alternating direction method of multipliers(ADMM)was proposed.A high-fidelity multi-data-center flexibility model was established by integrating IT equipment power consumption,dynamic power usage effectiveness(PUE)cooling models,queueing-theoretic task delay,and cross-data-center workload migration,thereby characterizing the adjustable potential of data centers across both spatial and temporal dimensions.A Stackelberg game-based bi-level optimization model was established,with the distribution system operator(DSO)as the upper-level leader and each data center as the lower-level follower.The upper level aimed to minimize the total system operating cost by setting time-of-use incentive electricity prices,while the lower level autonomously optimized electricity consumption strategies accordingly.The bi-level model was transformed into a single-level mixed-integer quadratic programming by applying Karush-Kuhn-Tucker(KKT)conditions and complementary slackness linearization.On this basis,an ADMM-based distributed solution framework was designed.An adaptive penalty parameter strategy based on the primal to dual residual ratio was adopted to accelerate convergence.Each agent was required only to exchange aggregated response quantities,without sharing task queues,server configurations,or other core operational data.Simulation results based on a modified IEEE 33-bus distribution test system demonstrated that the proposed algorithm converged after 34 iterations,accelerating convergence by 74.40%compared with a fixed penalty parameter scheme,while avoiding the risk of dual residual oscillations.The proposed method reduced total system operating cost by 18.3%and narrowed the net load peak-valley difference by 11.2%compared with the baseline scheme.Compared with the centralized optimization,the proposed method incurred a cost increase of only approximately 10.5%.Sensitivity analysis showed that as the number of data centers increased from 3 to 20,the computation time per iteration increased from 0.49 s to14.46 s,exhibiting a near-linear growth trend.The proposed method achieves an effective balance among economic efficiency,convergence performance,and data privacy protection.Each data center needs to report only aggregated regulation response quantities to complete the optimization iterations,thereby effectively preserving the data privacy of each agent.关键词
数据中心/主从博弈/算电协同/交替方向乘子法/分布式优化/配电系统运营商/数据隐私保护Key words
data center/Stackelberg game/computing-electricity coordination/alternating direction method of multipliers/distributed optimization/distribution system operator/data privacy protection分类
能源科技引用本文复制引用
张昌林,范鹏飞,韩建沛..基于主从博弈的多数据中心与电网协同优化运行方法[J].综合智慧能源,2026,48(8):107-116,10.基金项目
国家自然科学基金项目(52507116) （52507116）
北京市自然科学基金项目(3254046)National Natural Science Foundation of China(52507116) （3254046）
Beijing Natural Science Foundation(3254046) （3254046）
