A Low-carbon Planning Method for Distribution Network Considering Multi-station Integration of Energy Station, Substation and Data Center

李林虹; 贺帅佳; 高红均; 刘俊勇

doi:10.13335/j.1000-3673.pst.2024.1934

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A Low-carbon Planning Method for Distribution Network Considering Multi-station Integration of Energy Station, Substation and Data Center

更新时间：2026-03-13

- A Low-carbon Planning Method for Distribution Network Considering Multi-station Integration of Energy Station, Substation and Data Center
- Vol. 50, Issue 2, Pages: 494-504(2026)
- 作者机构：
  
  四川大学电气工程学院,四川省,成都市,610065
- 作者简介：
- 基金信息：
- DOI：10.13335/j.1000-3673.pst.2024.1934
  CLC： TM715
- Published：2026
- 稿件说明：
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李林虹, 贺帅佳, 高红均, et al. A Low-carbon Planning Method for Distribution Network Considering Multi-station Integration of Energy Station, Substation and Data Center[J]. 2026, 50(2): 494-504.
DOI：

李林虹, 贺帅佳, 高红均, et al. A Low-carbon Planning Method for Distribution Network Considering Multi-station Integration of Energy Station, Substation and Data Center[J]. 2026, 50(2): 494-504. DOI： 10.13335/j.1000-3673.pst.2024.1934.

摘要

在“双碳”目标下，多站融合为配电网低碳规划带来了新的机遇。因此，该文提出了一种综合考虑能源站、变电站和数据中心多站融合的配电网低碳规划方法。首先，建立了多站融合框架，并对数据中心能耗与需求响应特性、能源站电-热-冷能源转换过程和变电站余热回收过程进行建模。其次，考虑能源站和配电网电-热耦合关系，建立了多站融合下能源站支撑的配电网低碳规划模型，采用概率期望方法表征光伏出力和负荷的不确定性，并基于交替方向乘子法(alternating direction method of multipliers，ADMM)实现模型求解。最后，基于改进的IEEE-33节点进行算例仿真，结果表明，多站融合与需求响应措施能够显著降低配电网投资成本与运行成本，并减少配电网碳排放。

Abstract

With the "dual-carbon" goal

the multi-station integration opens up new opportunities for low-carbon planning in distribution networks. In this context

this paper presents a low-carbon planning method for distribution networks by integrating energy stations

substations

and data center. First

a multi-station integration framework is developed to model the data center’s electricity-heat-cooling energy consumption and demand response

energy station’s energy conversion processes

and substation’s waste heat recovery. Second

a low-carbon planning model is created based on the electric-thermal energy coupling between the energy station and the distribution network. This model uses probabilistic expectations to address uncertainties in photovoltaic outputs and loads

which the ADMM algorithm solves. Finally

a simulation using the improved IEEE-33 node confirms that multi-station integration

combined with demand response

significantly reduces both investment and operating costs

as well as carbon emissions of distribution network.

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