数据中心储能型不间断电源与综合能源系统协同优化调度研究

杨东俊明; 范俊秋; 李庆生; 张裕; 杜刃刃

doi:10.11959/j.issn.1000-0801.DXKX260163

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数据中心储能型不间断电源与综合能源系统协同优化调度研究

更新时间：2026-06-23

- 数据中心储能型不间断电源与综合能源系统协同优化调度研究
- Coordinated Optimal Dispatch of Energy-Storage-Based Uninterruptible Power Supply in Data Centers and Integrated Energy Systems
- 电信科学 2026年
- 作者机构：
  
  1.贵州电网有限责任公司电网规划研究中心，贵阳 550002
  2.贵州电网有限责任公司电力调度控制中心，贵阳 550002
- 作者简介：
- 基金信息：
  
  中国南方电网重点科技项目(GZKJXM20250099)
- DOI：10.11959/j.issn.1000-0801.DXKX260163
  中图分类号： TP393
- 收稿：2026-03-10，
  
  修回：2026-05-15，
  
  录用：2026-06-23，
- 稿件说明：
移动端阅览
杨东俊明, 范俊秋, 李庆生, 等. 数据中心储能型不间断电源与综合能源系统协同优化调度研究[J/OL]. 电信科学, 2026.

Yang Dong-jun-ming, FAN Jun-qiu, LI Qing-sheng, et al. Coordinated Optimal Dispatch of Energy-Storage-Based Uninterruptible Power Supply in Data Centers and Integrated Energy Systems[J/OL]. Telecommunications Science, 2026.
杨东俊明, 范俊秋, 李庆生, 等. 数据中心储能型不间断电源与综合能源系统协同优化调度研究[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260163.

Yang Dong-jun-ming, FAN Jun-qiu, LI Qing-sheng, et al. Coordinated Optimal Dispatch of Energy-Storage-Based Uninterruptible Power Supply in Data Centers and Integrated Energy Systems[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260163.

摘要

为了解决为数据中心供能时存在的可再生能源消纳率低、运行成本高、不间断电源的调节潜力未能得到充分利用等问题，该研究构建了一种基于数据中心储能型不间断电源（EUPS）的综合能源系统双层优化调度模型。上层以降低可再生能源弃电和系统的运行成本为目标，优化设备运行策略，下层利用模型预测控制（MPC）减少EUPS的荷电状态波动。案例结果表明，该方案可使系统运行成本较无储能基线降低9.3%，MPC的引入使得较无MPC控制时运行成本上升3.5%，但电池等效循环寿命提升41.4%。该方案较无储能时可使风机光伏弃电减少19.7%，且在调度过程中，EUPS的荷电状态与其初始值的平均绝对误差相对于无MPC控制时减少31.25%。研究结果表明，所提方案能够有效提升数据中心微电网的可再生能源消纳能力和系统的运行灵活性，为绿电直供背景下数据中心源网荷储协同运行提供参考。

Abstract

To address the issues of low renewable energy consumption

high operational costs

and the underutilization of the regulation potential of uninterruptible power supply (UPS) when supplying energy to data centers

this paper proposes a bi-level optimal dispatch model for an integrated energy system based on energy-storage-based uninterruptible power supply (EUPS) in data centers. The upper-level model aims to minimize renewable energy curtailment and overall system operating costs by optimizing the operational strategies of system components

while the lower-level model employs model predictive control (MPC) to reduce fluctuations in the state of charge (SOC) of the EUPS. Case study results demonstrate that the proposed approach can reduce system operating costs by 9.3%

decrease wind and photovoltaic curtailment by 19.7%

and reduce the mean absolute error of the SOC by 31.25%. The results indicate that the proposed method effectively enhances renewable energy utilization and operational flexibility in data center microgrids

providing a useful reference for the coordinated operation of source–grid–load–storage systems for data centers under the paradigm of direct green power supply.

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