Capacity Planning and Operational Optimization for Low-Carbon Data Center Integrated Energy System Considering Exergy Efficiency

doi:10.16183/j.cnki.jsjtu.2023.528

Abstract

Abstract:

With the rapid development of the digital economy, the energy consumption and carbon emissions of data centers (DCs) have significantly increased. In recent years, the construction of data center integrated energy systems (DC-IES) has emerged as one of the critical trends in energy conservation and emission reduction for DCs under the global net-zero emission initiative. To support the planning and construction of low-carbon DC-IES, this paper proposes a multi-objective optimization model for capacity allocation and operational planning of DC-IES, integrating energy and economic considerations with a focus on low-carbon performance. Based on the “quality” analysis method of exergy from the second law of thermodynamics, the model proposed comprehensively accounts for the dynamic exergy efficient characteristics of energy conversion devices under varying load conditions, revealing the energy flow distribution characteristics of DC-IES under different objectives. The computational results indicate that compared with the optimization scheme assuming constant equipment efficiency, the scheme considering dynamic equipment efficiency reduces energy loss rate, economic cost, and carbon emissions by 2.6%, 1.9%, and 4.8%, respectively, demonstrating clear advantages. Moreover, compared with the economically optimal scheme, the multi-objective optimization scheme significantly reduces carbon emissions and energy loss rate of the DC-IES by 22.72% and 20.73%, respectively. Furthermore, compared to the scheme scenarios with the minimum exergy loss rate and lowest carbon emissions, the multi-objective optimization scheme reduces economic costs by 54.54% and 60.78%, respectively. Compared with the scheme relying solely on grid electricity supply, the multi-objective optimization scheme that regards the DC as an integrated energy system can reduce carbon emissions by 40.97%.

Key words: data center (DC), integrated energy system (IES), exergy efficiency, computational power, energy performance

CLC Number:

TK01+9

LIN Jiayu, HAN Juntao, WANG Yongzhen, HAN Kai, HAN Yibo, LI Jian. Capacity Planning and Operational Optimization for Low-Carbon Data Center Integrated Energy System Considering Exergy Efficiency[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1327-1337.

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设备名称	η拟合公式(x为负荷率)	转化类型	λ_ex
氢燃料电池^[29]	26.067x⁵+18.01x⁴+60.894x³+88.849x²+21.788x+55.916	氢气—电能/热能	1.204 8
燃气轮机^[30]	0.410 2x³-0.935 6x²+0.827 4x+0.019 2	天然气—电能/热能	0.860 3
电压缩式制冷机^[31]	-5.625x²+9.5x	电能—冷能	0.077 1
吸收式制冷机^[32]	$\left\{\begin{array}{ll}0.83x+0.458 5,& x<0.65\\ 1,& x\ge 0.65\end{array}\right.$	热能—冷能	0.669 6

服务器参数	数值
P_idle/kW	0.5
P_peak/kW	1
α_PUE	1.5
μ×10^-6/h	5.4
数量/台	600
全生命周期/a	10

设备	单位容量成本/ (元·kW^-1)	运维费用/ [元·(kW·h)^-1]
氢燃料电池	5 000	0.2
燃气轮机	2 826	0.1
电压缩式制冷机	950	0.1
吸收式制冷机	756	0.15
蓄电设备	1 800	0.16
蓄热设备	189	0.15

输入能源类型	费用	碳排放量/ [kg·(kW· h)^-1]
电网供电	$\left\{\begin{array}{l}0.37元/(kW·h), t\in \left[\mathrm{0,7}\right]h\\ 0.82元/(kW·h),t\in \left[\mathrm{12,15}\right]\bigcup \left[\mathrm{20,23}\right]h\\ 1.36元/(kW·h),t\in \left[\mathrm{8,11}\right]\bigcup \left[\mathrm{16,19}\right]h\end{array}\right.$	0.623^[36]
天然气	3.95元/m^3[36]	0.184^[36]
氢气	30元/kg^[16]	0

方案	设备装机容量/kW						优化结果
方案	氢燃料电池	燃气轮机	吸收式制冷机	电压缩式制冷机	蓄电设备	蓄热设备	㶲损失率/%	经济成本/ 万元	碳排放量/ (t·a^-1)
1	398	744	629	0	630	390	59.24	7 808	3 103
2	438	730	629	0	544	405	60.81	7 958	3 258
3	0	962	629	208	315	258	74.73	4 756	4 015
4	942	0	629	0	243	0	52.90	19 906	0
5	825	247	629	0	249	158	51.70	17 177	730
6	0	0	0	629	1 380	0	52.39	7 294	5 256