Researches on data center integrated energy systems based on knowledge graph

doi:10.3969/j.issn.2097-0706.2023.07.001

Abstract

Abstract:

As a key infrastructure in digital economy era， the energy consumption and carbon emissions of data centers are becoming increasingly prominent， and a series of research has been conducted by global scholars in the past decade. To comprehensively reveal the progress of global data center energy consumption researches， this article proposes a perspective based on integrated energy system and a metric method based on knowledge graph， and conducts a review of the latest research on the integrated energy system of data center from various aspects such as research trends， theoretical method and engineering cases. Firstly， a qualitative and quantitative analysis is conducted on international collaboration， characteristic distribution， and research hotspots in the field of global data center energy research based on scientific publications. Based on this， the article focuses on the research of data center integrated energy systems， which involve energy cascading utilization， multi-energy complementarity， and source-grid-load-storage integration. It analyzes the planning and design， operational evaluation， computational solution methods， and key points of data center integrated energy systems， revealing that the energy systems of data centers are constantly moving towards multidimensional global optimization that includes economic and low-carbon aspects. Finally， the article discusses the inapplicability of traditional PUE in data center integrated energy systems and preliminarily proposes evaluation indicators and systems for data center energy systems.

Key words: data center, integrated energy system, knowledge graph, PUE, carbon neutrality, multi-energy complementarity, source-grid-load-storage integration

CLC Number:

TK01

WANG Yongzhen, HAN Yibo, HAN Kai, HAN Juntao, SONG Kuo, ZHANG Lanlan. Researches on data center integrated energy systems based on knowledge graph[J]. Integrated Intelligent Energy, 2023, 45(7): 1-10.

Figures/Tables 5

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Table 1

References 64

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文献	求解方法/求解工具	优化目标	优化变量
[3]	单目标；Matlab	能源成本	不同地理分布的数据中心负载及热电联产系统出力
[49]	单目标；GAMS，CPLEX	初始投资成本、运维成本，碳排放量	容量配置、调度策略
[45]	单目标优化对比	外部电力采购成本/总体能源成本	耗电量、发电量
[46]	单目标；CPLEX	运行成本	热电联产机组的调度、工作负荷、冷却资源、储能充放电
[47]	单目标；GUROBI，CPLEX，MOSEK	年度成本（包括投资成本和超过调度期限的电力和氢气成本）	各设备调度
[48]	多目标对比分析	提高能源利用率、减少废热排放	各设备调度
[50]	单目标：GAMS，CPLEX	总运行成本	电力市场交互，设备调度
[51]	多目标转化为单目标（加权方法）；Matlab，CPLEX	年度总成本、碳排放	各设备装机容量
[52]	单目标；Matlab，GUROBI	运行成本(包括供能成本和碳交易成本)	冷热电供应调度
[53]	多目标；GAMS，LINDOGLOBAL；	年碳排放量、年总费用	各设备容量配置及出力情况
[54]	多目标；GAMS	总利润、碳排放	各设备调度、电网购售电、需求响应
[55]	单目标	年度能源成本	设备启停、电网购售电
[56]	单目标；基于粒子群算法、遗传算法	综合经济成本	设备容量大小，混合储能运行策略