Thermo-economic analysis of a multi-pressure supercritical CO₂ pumped thermal energy storage system integrated with waste heat recovery

doi:10.11949/0438-1157.20250447

Abstract

Abstract:

Pumped thermal energy storage (PTES) is a large-scale, long-duration physical energy storage technology for achieving stable operation of high-proportional renewable energy power systems, while advancing the large-scale application of efficient energy storage and enhancing the deep peak regulation capability of thermal power plants are vital pathways for both maximizing renewable energy integration and facilitating the low-carbon transition of coal-fired power. To this end, a novel multi-pressure supercritical CO₂ PTES system integrated with waste heat recovery is developed. This system achieves efficient recovery and utilization of low-grade flue gas waste heat from thermal power plants. Furthermore, by incorporating a multi-stage thermal storage topology and distributed regenerative devices, it significantly mitigates irreversible losses caused by temperature glide during heat exchange processes within the PTES system. Consequently, the proposed system enhances the flexible peak shaving capability of thermal power units and ensures the secure grid integration of renewable energy sources. Based on the establishment of thermodynamic and economic models, sensitivity analysis was employed to study the influence of key operating parameters on the system's thermodynamic and economic performance. Furthermore, a genetic algorithm was applied to conduct thermo-economic multi-objective optimization. The results show that the performance indicators of the system under the design condition are exergy efficiency of 53.83% and levelized cost of energy(LCOE) of 2063.26 CNY·MWh⁻¹. Among the key parameters, the isentropic efficiency of the discharge expander has the most significant impact on the thermodynamic performance, while the isentropic efficiency of the charging turbine has the most significant impact on economic performance. According to the TOPSIS method, the optimal exergy efficiency obtained in the Pareto optimal frontier solution set is 58.20% and the levelized cost of electricity is 1142.24 CNY·MWh⁻¹, which are 8.12% higher and 44.64% lower than the design condition, respectively.

Key words: pumped thermal energy storage, low-temperature waste heat recovery, supercritical carbon dioxide, optimal design, genetic algorithm

摘要：

热泵储电是实现高比例新能源电力系统稳定运行的大规模长时物理储能技术，而推动大规模高效储能应用以及改善火电深度调峰能力是保障新能源电力充分消纳与煤电低碳转型的重要途径。为此，开发了一种集成余热回收的多压超临界CO₂热泵储电系统，不仅可以实现火电厂低品位烟气余热的高效回收与利用，并且通过引入多级储热拓扑与分布式回热装置，大幅降低了热泵储电系统在换热过程由于温度滑移问题导致的不可逆损失，实现了火电机组灵活调峰和可再生能源安全并网。在建立系统热力学与经济学模型的基础上，采用敏感性分析方法研究了关键运行参数对系统热力学性能与经济性能的影响规律，进而采用遗传算法开展了系统的热经济学多目标优化分析。结果表明，系统在设计工况下的性能指标㶲效率为53.83%，平准化度电成本为2063.26 CNY·MWh^-1；在关键参数中，放电膨胀机等熵效率对系统热力学性能影响最显著，而充电膨胀机等熵效率对系统经济学性能影响最显著。根据TOPSIS方法在Pareto最优前沿解集中获得的最优工况㶲效率为58.20%、平准化度电成本为1142.24 CNY·MWh^-1，较设计工况分别提升8.12%与降低44.64%。

关键词: 热泵储电, 低温余热回收, 超临界二氧化碳, 优化设计, 遗传算法

CLC Number:

TQ 028.8

Chenyang ZHOU, Haojie SHANG, Yang HU, Tianhang CAO, Erren YAO, Guang XI. Thermo-economic analysis of a multi-pressure supercritical CO₂ pumped thermal energy storage system integrated with waste heat recovery[J]. CIESC Journal, 2025, 76(12): 6587-6600.

周晨阳, 商浩杰, 胡杨, 曹天航, 姚尔人, 席光. 集成余热回收的多压超临界CO₂热泵储电系统热经济学特性研究[J]. 化工学报, 2025, 76(12): 6587-6600.

Figures/Tables 22

Fig.1 A multi-pressure supercritical CO₂ pumped thermal energy storage system integrated with waste heat recovery

Table 1 The procurement cost equation of system equipment［24-26］

设备	成本函数
充电压缩机	$P E C c o m_c h a = 91562 W c o m_c h a 445 0.67$
放电压缩机	$P E C c o m_d i s = 91562 W c o m_d i s 445 0.67$
充电膨胀机	$P E C t u r c h a = 1054.42 m i n 0.92 - η t u r c h a l n ε t u r c h a [1 + e x p (0.036 T i n - 54.4)]$
放电膨胀机	$P E C t u r d i s = 1054.42 m i n 0.92 - η t u r d i s l n ε t u r d i s$ $[1 + e x p (0.036 T i n - 54.4)]$
换热器	$P E C e x_s = 16000 A r e a e x_s 100 0.6$
储罐	$P E C t a n k = 1.128 e x p 11.662 - 0.6104 l n (264.17 V t a n k + 0.04536 [l n (264.17 V t a n k)] 2$
冷凝器	$P E C e x_c o = 16000 A r e a e x_c o 100 0.6$
烟气换热器	$P E C e x_g a s = 16000 A r e a e x_g a s 100 0.6$
回热器	$P E C e x_r e = 16000 A r e a e x_r e 100 0.6$

Table 1 The procurement cost equation of system equipment［24-26］

设备	成本函数
充电压缩机	$P E C c o m_c h a = 91562 W c o m_c h a 445 0.67$
放电压缩机	$P E C c o m_d i s = 91562 W c o m_d i s 445 0.67$
充电膨胀机	$P E C t u r c h a = 1054.42 m i n 0.92 - η t u r c h a l n ε t u r c h a [1 + e x p (0.036 T i n - 54.4)]$
放电膨胀机	$P E C t u r d i s = 1054.42 m i n 0.92 - η t u r d i s l n ε t u r d i s$ $[1 + e x p (0.036 T i n - 54.4)]$
换热器	$P E C e x_s = 16000 A r e a e x_s 100 0.6$
储罐	$P E C t a n k = 1.128 e x p 11.662 - 0.6104 l n (264.17 V t a n k + 0.04536 [l n (264.17 V t a n k)] 2$
冷凝器	$P E C e x_c o = 16000 A r e a e x_c o 100 0.6$
烟气换热器	$P E C e x_g a s = 16000 A r e a e x_g a s 100 0.6$
回热器	$P E C e x_r e = 16000 A r e a e x_r e 100 0.6$

Table 2 Model validation

流股点	压力/kPa	温度/K		相对误差/%
流股点	压力/kPa	文献^[9]	本文	相对误差/%
1	1500	523.15	523.15	0
2	8000	739.95	736.22	0.50
3	8000	545.45	546.51	0.19
4	8000	373.15	373.15	0.00
5	1500	251.94	254.97	1.20
6	1500	319.15	319.15	0
7	8000	729.95	726.22	0.51
8	1500	558.05	557.45	0.11
9	1500	408.85	410.22	0.34
10	1500	327.45	327.99	0.16
11	1500	259.94	262.97	1.17
12	8000	403.75	404.76	0.25
13	8000	535.15	536.09	0.18

Table 3 Charge and discharge initial parameters

参数	数值	文献
环境温度/℃	25	[9]
环境压力/kPa	101.325	[9]
压缩机进口压力/kPa	7600	[29]
压缩机出口压力/kPa	25000	[29]
充电压缩机进口温度/℃	220	[30]
放电压缩机进口温度/℃	32	[29]
充电循环质量流量/(kg·s^-1)	100	[9]
放电循环最小换热端差/℃	5	[24]
充电循环最小换热端差/℃	5	[24]
充电压缩机等熵效率/%	85	[9]
充电膨胀机等熵效率/%	90	[9]
放电压缩机等熵效率/%	80	[9]
放电膨胀机等熵效率/%	90	[9]

Table 4 Flow stock parameters

流股	温度/K	压力/kPa	比焓/(kJ·kg^-1)	比熵/(kJ·(kg·K)^-1)	质量流量/(kg·s^-1)	㶲/MW
1	306.28	7600	372.27	1.563	100	21.640
2	322.32	7600	442.00	1.787	100	21.947
3	413.06	7600	572.08	2.146	100	24.225
4	493.15	7600	663.65	2.349	100	27.337
5	642.03	25000	805.59	2.383	100	40.529
6	528.87	25000	660.43	2.134	100	33.436
7	463.73	25000	568.86	1.949	100	29.795
8	373.15	25000	404.40	1.551	100	25.199
9	305.15	7600	315.08	1.376	80	17.200
10	348.01	25000	348.00	1.395	80	19.380
11	453.73	25000	553.58	1.916	80	23.408
12	493.15	25000	611.59	2.038	80	25.123
13	632.03	25000	793.04	2.363	80	31.889
14	510.19	7600	682.80	2.387	80	22.491
15	458.73	7600	624.80	2.268	80	20.709
16	332.32	7600	462.19	1.848	80	17.701
17	360.58	2000	114.61	0.345	85.26	1.003
18	458.73	2000	307.50	0.817	85.26	5.465
19	511.01	2000	424.33	1.058	44.83	4.890
20	637.03	2000	748.13	1.622	44.83	11.862
21	435.90	2000	259.58	0.709	64.17	3.088
22	327.32	2000	56.87	0.177	64.17	0.263
23	332.32	101.325	332.86	6.970	328.94	0.603
24	311.28	101.325	311.67	6.904	328.94	0.093

Fig.2 T-s diagram of system working fluid

Fig.3 Critical equipment exergy destruction chord diagram

Fig.4 Effect of mass flow rate during discharging process on energy input, energy output, system efficiency and LCOE

Fig.5 Effect of compressor inlet pressure on energy input, energy output, system efficiency and LCOE

Fig.6 Effect of compressor outlet pressure on energy input, energy output, system efficiency and LCOE

Fig.7 Effect of minimum heat exchange temperature difference during discharging cycle on energy input, energy output, system efficiency and LCOE

Fig.8 Effect of discharging expander isentropic efficiency on energy input, energy output, system efficiency and LCOE

Fig.9 Effect of minimum heat exchange temperature difference during charging cycle on energy input, energy output, system efficiency and LCOE

Fig.10 Effect of discharging compressor isentropic efficiency on energy input, energy output, system efficiency and LCOE

Fig.11 Effect of charging expander isentropic efficiency on energy input, energy output, system efficiency and LCOE

Fig.12 Effect of charging compressor inlet temperature on energy input, energy output, system efficiency and LCOE

Fig.13 Effect of charging compressor isentropic efficiency on energy input, energy output, system efficiency and LCOE

Table 5 Settings for system multi-objective optimization

决策变量	取值
种群数量	150
迭代次数	200
选择函数	Tournament
突变函数	Constraint dependent
超参数组合规模	2
交叉函数	Intermediate
交叉分数	0.8
Pareto分数	0.8

Table 6 The range of the value of the decision variable

决策变量	取值范围
充电压缩机进口温度/℃	200~220
压缩机出口压力/kPa	19000~25000
压缩机进口压力/kPa	7600~9000
充电压缩机等熵效率/%	76~90
充电膨胀机等熵效率/%	80~90
放电压缩机等熵效率/%	74~90
放电膨胀机等熵效率/%	80~90
放电循环质量流量/(kg·s^-1)	80~96
充电循环最小换热端差/℃	5~10
放电循环最小换热端差/℃	5~10

Fig.14 Pareto frontier of multi-objective optimization

Table 7 Optimal solution of decision variables

参数	数值
充电压缩机进口温度/℃	219.84
压缩机出口压力/kPa	24250
压缩机进口压力/kPa	7818
充电压缩机等熵效率/%	85.66
充电膨胀机等熵效率/%	81.93
放电压缩机等熵效率/%	89.66
放电膨胀机等熵效率/%	86.74
放电循环质量流量/(kg·s^-1)	95.93
充电循环最小换热端差/℃	5.04
放电循环最小换热端差/℃	5.05

Fig.15 Comparison with other supercritical CO2 pumped thermal energy storage systems

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