利用内生位置价格信号获取数据中心时空负荷转移灵活性的双层优化模型

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2025-06-03 DOI:10.1049/stg2.70020
Ding Ma, Yujian Ye, Yizhi Wu, Dezhi Xu, Zhaohao Ding, Goran Strbac
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引用次数: 0

摘要

随着数字信息技术的快速发展,对计算能力的需求不断增长,推动了数据中心行业的扩张,导致了巨大的能源消耗和温室气体排放。直流电源作为电力系统负荷侧的灵活可调资源,对现代能源系统的运行至关重要。在此背景下,本文提出了一种利用时空灵活性的DCs和电力系统协调安全调度策略。首先,分析了数据中心的架构和运行特性,然后开发了功耗模型来量化其时空灵活性。然后,考虑到数据中心作为独立运营商和影响位置电价的灵活资源的双重角色,开发了一个双层优化模型来协调计算能力和电力。最后,对改进后的IEEE 39节点系统进行了仿真分析。结果表明,该策略有效地利用了数据中心的时空灵活性,缓解了系统拥塞。在经济效益方面,DCs的成本效率提高了70%,系统运行成本降低了46%。在环境影响方面,该战略使可再生能源消费增加26.58%,碳排放显著减少63%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bi-Level Optimisation Model for Harvesting Spatial-Temporal Load Shifting Flexibility of Data Centres Using Endogenously Formed Locational Price Signal

With the rapid advancement of digital information technology, the demand for computational power continues to grow, driving the expansion of the data centre (DC) industry, leading to gigantic amount of energy consumption and greenhouse gas emission. DCs, serving as flexible and adjustable resources on the load side of power systems, are essential to the operation of modern energy systems. In this context, this paper proposes a coordinated and secure scheduling strategy for DCs and power system, utilising spatial-temporal flexibility. First, the architecture and operational characteristics of DCs are analysed, followed by the development of an power consumption model to quantify their spatial-temporal flexibility. Then, a bi-level optimisation model is developed to coordinate computational power and electricity, considering DCs' dual roles as independent operators and flexible resources that influence locational electricity prices. Finally, simulation analyses are performed on the modified IEEE 39-node system. The results demonstrate that the proposed strategy effectively utilises the spatial-temporal flexibility of DCs, alleviating system congestion. In terms of economic benefits, it enhances the cost efficiency of the DCs by 70% and reduces the system's operating costs by 46%. Regarding environmental impact, the strategy increases renewable energy consumption by 26.58% and significantly reduces carbon emissions by 63%.

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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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