电力工程技术2026，Vol.45Issue(5)：40-49,10.DOI:10.12158/j.2096-3203.2026.05.004
计及风光不确定性的数据中心电算协同多目标优化策略
A multi-objective power-computing collaborative optimization strategy for data centers considering wind and solar uncertainty
摘要
Abstract
The rapid expansion of data centers is an inevitable trend in the intelligent era.To address the challenges of high energy consumption and carbon emissions,a multi-objective optimization model for power-computing collaboration in data centers is proposed,with uncertainty in renewable energy output considered.An improved k-means algorithm is applied to reduce annual forecast scenarios of wind and solar power output.A set of typical representative scenarios is extracted to mitigate the impact of renewable energy uncertainty on system operation.Based on the flexibility of computing loads and energy storage systems,a collaborative optimization model is constructed.The objectives minimize the daily total cost of data center operation and the curtailment rate of wind and solar energy.The optimization model is solved under the optimal scenario of renewable energy output using the non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)with an elitism selection strategy.The results demonstrate that joint scheduling of wind,solar,and storage systems,together with flexible computing loads can effectively reduce daily operating costs,and improve renewable energy utilization,and maintain user satisfaction.关键词
数据中心/不确定性/多目标优化/算力负载调度/带有精英选择策略的非支配遗传算法(NSGA-Ⅱ)/场景缩减Key words
data center/uncertainty/multi-objective optimization/computing load scheduling/non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)/scenario reduction分类
信息技术与安全科学引用本文复制引用
王一航,张靖,何宇,严儒井,敖炫..计及风光不确定性的数据中心电算协同多目标优化策略[J].电力工程技术,2026,45(5):40-49,10.基金项目
国家自然科学基金资助项目(52406227) （52406227）
贵州省科技支撑计划(黔科合支撑[2025]一般021,黔科合支撑DXGA[2025]一般007) （黔科合支撑[2025]一般021,黔科合支撑DXGA[2025]一般007）
