高效太阳能电解水制氢系统及其性能的多目标优化

doi:10.11949/0438-1157.20221595

摘要/Abstract

摘要：

针对日益重要的清洁可持续绿氢生产技术需求，开发了一种基于太阳能，集发电和制氢于一体的高效系统。该系统由塔式太阳能发电和热能存储系统、质子交换膜(PEM)电解水系统和含有回热器的再热式蒸汽朗肯循环及含有回热器的有机朗肯循环余热回收子系统组成，可实现能量梯级利用。在Aspen Plus中建立了各子系统的模拟模型，并用Fortran语言编写太阳能定日镜场和PEM电解槽数学模型，基于非支配排序遗传算法-Ⅱ (NSGA-Ⅱ)和Aspen Plus与MATLAB软件的交互和多目标优化权衡最大㶲效率、最大日净输电量、最小氢气的平准化成本(LCOH)，实现该系统的优化。所建立的模型可以高效准确地模拟、分析和优化该集成系统。帕累托前沿表明，该系统最优的㶲效率、日净输电量和LCOH分别为52.19%、247.352 MWh/d和6.05 USD/kg；优化后，最佳氢气产能为4.796 t/d，㶲效率提高3.00%，日净输电量增加31.14%，LCOH降低4.87%。该研究对于大规模太阳能耦合发电和制氢工艺的开发具有重要的指导意义。

关键词: 塔式太阳能发电, 电解, 制氢, 集成, 算法, 多目标优化

Abstract:

Given the growing demand for clean and sustainable production technologies of green hydrogen, an efficient solar-based system with power and hydrogen production integrated has been developed. The system consists of a tower solar power generation and thermal energy storage system, a proton exchange membrane (PEM) electrolysis water system, a reheated steam Rankine cycle with a regenerator and an organic Rankine cycle waste heat recovery subsystem with a regenerator. Energy cascade utilization can be realized. The integrated system is simulated in Aspen Plus software, and the mathematical models of the solar heliostat field and PEM electrolyzer are written in Fortran language and embedded. Based on the non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) and the interaction between Aspen Plus and MATLAB software, the trade-off between maximum exergy efficiency, maximum output electrical energy per day, and minimum levelized cost of hydrogen (LCOH) is performed through multi-objective optimization. The Pareto frontier shows that the optimal exergy efficiency and output electrical energy per day are 52.19% and 247.352 MWh/d, increased by 3.00% and 31.14%, respectively; the LCOH is 6.05 USD/kg, decreased by 4.87%, and the optimal hydrogen production capacity is 4.796 t/d. This study has specific guiding significance for large-scale solar power generation and hydrogen production.

Key words: solar power tower, electrolysis, hydrogen production, integration, algorithm, multi-objective optimization

中图分类号:

TQ 021.8

张生安, 刘桂莲. 高效太阳能电解水制氢系统及其性能的多目标优化[J]. 化工学报, 2023, 74(3): 1260-1274.

Sheng’an ZHANG, Guilian LIU. Multi-objective optimization of high-efficiency solar water electrolysis hydrogen production system and its performance[J]. CIESC Journal, 2023, 74(3): 1260-1274.

图/表 17

图1 基于太阳能的PEM电解水制氢系统原理图

Fig.1 Principle diagram of PEM water electrolysis system based on solar energy

图2 基于太阳能的PEM电解水制氢系统Aspen Plus模拟流程

Fig.2 Aspen Plus flowsheet of PEM water electrolysis system based on solar energy

图3 含有回热器的再热式SRC和含有回热器的ORC温熵图

Fig.3 Temperature-entropy diagrams of reheat SRC with recuperator and ORC with recuperator

表1 再热式SRC与含有回热器的ORC的模拟结果与文献数据比较

Table 1 Comparison of simulation results of reheat SRC and ORC with recuperator with literature data

流程	参数	本模型	文献数据	相对误差/%
含有回热器再热式SRC^[23]	最高温度/℃	500	500	N/A
	冷凝温度/℃	60.18	60.06	0.20
	高压汽轮机出口温度/℃	345.3	345.2	0.03
	最高压力/bar	80	80	N/A
	中间压力/bar	30	30	N/A
	最低压力/bar	0.2	0.2	N/A
	热效率/%	38.03	39.80	4.44
含有回热器的ORC^[24]	介质质量流率/(kg/s)	33.424	33.424	N/A
	最高温度/℃	100.4	100.0	0.40
	冷凝温度/℃	30	30	N/A
	热流股回热输出温度/℃	40	40	N/A
	最高压力/bar	12.67	12.67	N/A
	最低压力/bar	1.801	1.801	N/A
	热效率/%	13.70	13.07	4.82

图4 PEM电解槽模拟和实验数据对比(a)及水分解所需热量与过电势产生热量对比(b)

Fig.4 Comparison of data predicated by the PEM electrolyzer model and experimental data (a) and comparison of heat demanded by water splitting and that generated by overpotentials (b)

图5 基于MATLAB软件的NSGA-Ⅱ优化流程

Fig.5 The optimization procedure with NSGA-Ⅱ based on MATLAB software

表2 流程的基本参数值［20-21，30］

Table 2 The basic parameters of the proposed process［20-21，30］

参数	取值	参数	取值
直射太阳光辐照量, DNI	995.3 W/m²	高压蒸汽轮机进口温度	525℃
光学效率, $η_{O p t}$	0.75	高压蒸汽轮机进口压力	170 bar
单个定日镜面积， $A_{H e l i o}$	121 m²	蒸汽的循环质量流量	15.29 kg/s
中央接收塔发射率, $ε$	0.88	SRC泵的效率	0.80
中央接收塔面积， $A_{r}$	60 m²	SRC汽轮机的效率	0.88
中央接收塔表面温度， $T_{r e v, s u r f}$	1100℃	ORC循环工质	R245fa
充热时间， $t_{S o l a r}$	8 h	ORC泵的效率	0.80
风速，V_a	3 m/s	ORC膨胀机的效率	0.85
PEM电流密度， $i$	2000 A/m²	ORC膨胀机进口温度	110℃
热盐罐出口温度	565℃	ORC膨胀机进口压力	10 bar
冷盐罐出口温度	290℃	ORC膨胀机出口压力	2.17 bar
储罐数量	2	ORC的质量流率	160 kg/s

表2 流程的基本参数值［20-21，30］

Table 2 The basic parameters of the proposed process［20-21，30］

参数	取值	参数	取值
直射太阳光辐照量, DNI	995.3 W/m²	高压蒸汽轮机进口温度	525℃
光学效率, $η_{O p t}$	0.75	高压蒸汽轮机进口压力	170 bar
单个定日镜面积， $A_{H e l i o}$	121 m²	蒸汽的循环质量流量	15.29 kg/s
中央接收塔发射率, $ε$	0.88	SRC泵的效率	0.80
中央接收塔面积， $A_{r}$	60 m²	SRC汽轮机的效率	0.88
中央接收塔表面温度， $T_{r e v, s u r f}$	1100℃	ORC循环工质	R245fa
充热时间， $t_{S o l a r}$	8 h	ORC泵的效率	0.80
风速，V_a	3 m/s	ORC膨胀机的效率	0.85
PEM电流密度， $i$	2000 A/m²	ORC膨胀机进口温度	110℃
热盐罐出口温度	565℃	ORC膨胀机进口压力	10 bar
冷盐罐出口温度	290℃	ORC膨胀机出口压力	2.17 bar
储罐数量	2	ORC的质量流率	160 kg/s

表3 集成系统的模拟和分析结果

Table 3 Simulation and analysis results of the integrated system

参数	数值
产品
电能	188.618 MWh/d
氢气	4.834 t/d
氧气	36.366 t/d
效率和技术经济性结果
能量效率， $η_{e n}$	20.52 %
㶲效率， $η_{e x}$	50.67 %
发电效率， $η_{P G E}$	37.52 %
定日镜面数	1769
中央接收塔高度	158 m
年度化成本 (TAC)	15.48 MUSD/a
氢气的平准化成本 (LCOH)	6.36 USD/kg

表3 集成系统的模拟和分析结果

Table 3 Simulation and analysis results of the integrated system

参数	数值
产品
电能	188.618 MWh/d
氢气	4.834 t/d
氧气	36.366 t/d
效率和技术经济性结果
能量效率， $η_{e n}$	20.52 %
㶲效率， $η_{e x}$	50.67 %
发电效率， $η_{P G E}$	37.52 %
定日镜面数	1769
中央接收塔高度	158 m
年度化成本 (TAC)	15.48 MUSD/a
氢气的平准化成本 (LCOH)	6.36 USD/kg

图6 电解水的进料量对系统性能的影响

Fig.6 Effect of flow rate of electrolyzed water on the system performance

图7 HPT出口压力对系统性能的影响

Fig.7 Effect of outlet pressure of HPT on system performance

图8 ORC膨胀压力对系统性能的影响

Fig.8 Effect of expansion pressure of ORC on system performance

图9 PEM电解槽的参数对系统㶲效率和LCOH的影响

Fig.9 Effects of parameters of PEM electrolyzer on system exergy efficiency and LCOH

表4 多目标优化的决策变量和约束

Table 4 Decision variables and constraints for multi-objective optimization

变量与约束条件	取值范围	单位
决策变量
电解水的进料质量流率	0.2~0.9	kg/s
HPT出口压力	30~45	bar
PEM电解温度	40~90	℃
PEM的电流密度	500~8000	A/m²
ORC的膨胀压力	4~15	bar
ORC的循环质量流率	150~180	kg/s
熔盐泵P100出口压力	5~10	bar
蒸汽轮机等熵效率	75~95	%
流股8的温度T₈	36~56	℃
约束条件
熔盐泵P100的压头H_P100	H_P100>H_t	m
熔盐流股MS4和MS6的温度	T_MS4≥290，T_MS6≥290	℃
日净输电量 ${\dot{W}}_{N e t, d a y}$	${\dot{W}}_{N e t, d a y} \geq 0$	MWh/d
氧气流股8的温度T₈	T₈≥T₂+10	℃

表4 多目标优化的决策变量和约束

Table 4 Decision variables and constraints for multi-objective optimization

变量与约束条件	取值范围	单位
决策变量
电解水的进料质量流率	0.2~0.9	kg/s
HPT出口压力	30~45	bar
PEM电解温度	40~90	℃
PEM的电流密度	500~8000	A/m²
ORC的膨胀压力	4~15	bar
ORC的循环质量流率	150~180	kg/s
熔盐泵P100出口压力	5~10	bar
蒸汽轮机等熵效率	75~95	%
流股8的温度T₈	36~56	℃
约束条件
熔盐泵P100的压头H_P100	H_P100>H_t	m
熔盐流股MS4和MS6的温度	T_MS4≥290，T_MS6≥290	℃
日净输电量 ${\dot{W}}_{N e t, d a y}$	${\dot{W}}_{N e t, d a y} \geq 0$	MWh/d
氧气流股8的温度T₈	T₈≥T₂+10	℃

图10 㶲效率与LCOH的帕累托前沿

Fig.10 Pareto frontier for the exergy efficiency versus LCOH

图11 日净输电量与LCOH的帕累托前沿

Fig.11 Pareto frontier for the net output electricity per day versus LCOH

表5 点A、B、C、D、E和F对应目标函数和操作参数值

Table 5 The objective functions and operation parameters corresponding points A， B， C， D， E and F

参数	A	B	C	D	E	F
决策变量
电解水的进料质量流率/（kg/s）	0.864	0.445	0.200	0.787	0.450	0.268
HPT出口压力/bar	38.88	32.31	33.45	42.193	42.29	41.53
PEM电解温度/℃	66.55	56.03	40.50	69.46	69.27	54.67
PEM的电流密度/（A/m²）	4044.50	2609.08	5953.25	4622.80	4622.82	5026.40
ORC的膨胀压力/bar	7.488	13.019	10.178	14.247	14.261	14.677
ORC的循环质量流率/（kg/s）	162.97	160.32	166.77	158.54	158.47	162.87
熔盐泵P100出口压力/bar	8.657	9.655	8.577	8.889	8.72	8.457
HPT等熵效率/%	92.91	92.52	91.22	95	95	95
LPT等熵效率/%	95	95	95	90.48	95	82.54
流股8的温度T₈/℃	51.84	48.11	38.64	47.29	47.31	48.97
目标函数
㶲效率/%	48.38	52.39	54.57	—	—	—
日净输电量/（MWh/d）	—	—	—	61.673	247.352	310.180
LCOH/（USD/kg）	5.08	6.19	9.65	5.09	6.05	8.49

表6 本研究与其他太阳能制氢方法的比较

Table 6 Comparison between this study and other solar-hydrogen methods

系统	LCOH/（USD/kg）	下降(+)/上升(-)比例/%	文献
太阳能驱动超临界CO₂布雷顿循环的高温SOEC制氢	9.28	+82.32	[31]
太阳能驱动氦闭式布雷顿循环发电的PEM制氢	7.00	+37.52	[32]
太阳能驱动超临界蒸汽朗肯循环发电的Cu-Cl循环制氢	7.58	+48.92	[33]
太阳能驱动的蒸汽朗肯循环发电的PEM制氢	6.00	+17.88	[34]
光伏和电池储能系统的碱性电解槽制氢	6.55	+28.68	[35]
太阳能-燃气轮机与蒸汽轮机混合发电的PEM制氢	5.72	+12.38	[36]
太阳能-风能-生物乙醇膜反应器耦合制氢	4.16	-18.27	[37]
光伏-风-蓄电池-蓄热多联产的PEM制氢	1.42	-72.10	[38]
太阳能-电-氢多联产	5.09	—	本文

参考文献 38

1	舒新前, 张蕾, 张磊. 煤催化热解制氢技术[M]. 北京: 科学出版社, 2011.
	Shu X Q, Zhang L, Zhang L. Hydrogen Production Technology by Catalytic Pyrolysis of Coal[M]. Beijing: Science Press, 2011.
2	Takeda M, Nara H, Maekawa K, et al. Simulation of liquid level, temperature and pressure inside a 2000 liter liquid hydrogen tank during truck transportation[J]. Physics Procedia, 2015, 67: 208-214.
3	Hanley E S, Deane J, Gallachóir B Ó. The role of hydrogen in low carbon energy futures—a review of existing perspectives[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 3027-3045.
4	李灿. 太阳能转化科学与技术[M]. 北京: 科学出版社, 2020.
	Li C. Solar Energy Conversion Science and Technology[M]. Beijing: Science Press, 2020.
5	曹军文, 张文强, 李一枫, 等. 中国制氢技术的发展现状[J]. 化学进展, 2021, 33(12): 2215-2244.
	Cao J W, Zhang W Q, Li Y F, et al. Current status of hydrogen production in China[J]. Progress in Chemistry, 2021, 33(12): 2215-2244.
6	邓成刚, 李伟科, 梁展鹏, 等. 太阳光热发电-超临界二氧化碳循环系统经济性分析与优化[J]. 热力发电, 2021, 50(5): 59-66.
	Deng C G, Li W K, Liang Z P, et al. Economic analysis and optimization for concentrated solar power-supercritical carbon dioxide Brayton cycle system[J]. Thermal Power Generation, 2021, 50(5): 59-66.
7	Yang H L, Li J, Wang Q L, et al. Performance investigation of solar tower system using cascade supercritical carbon dioxide Brayton-steam Rankine cycle[J]. Energy Conversion and Management, 2020, 225: 113430.
8	Oyedepo S O, Fakeye B A, Mabinuori B, et al. Thermodynamics analysis and performance optimization of a reheat-regenerative steam turbine power plant with feed water heaters[J]. Fuel, 2020, 280: 118577.
9	Bauer T, Pfleger N, Breidenbach N, et al. Material aspects of solar salt for sensible heat storage[J]. Applied Energy, 2013, 111: 1114-1119.
10	苗安康, 袁越, 吴涵, 等. “双碳”目标下绿色氢能技术发展现状与趋势研究[J]. 分布式能源, 2021, 6(4): 15-24.
	Miao A K, Yuan Y, Wu H, et al. Research on development status and trend of green hydrogen energy technologies under targets of carbon peak and carbon neutrality[J]. Distributed Energy, 2021, 6(4): 15-24.
11	Nafchi F M, Baniasadi E, Afshari E, et al. Performance assessment of a direct steam solar power plant with hydrogen energy storage: an exergoeconomic study[J]. International Journal of Hydrogen Energy, 2022, 47(62): 26023-26037.
12	Ghorbani P, Smida K, Razzaghi M M, et al. Modeling and thermoeconomic analysis of a 60 MW combined heat and power cycle via feedwater heating compared to a solar power tower[J]. Sustainable Energy Technologies and Assessments, 2022, 54: 102861.
13	Alirahmi S M, Assareh E, Arabkoohsar A, et al. Development and multi-criteria optimization of a solar thermal power plant integrated with PEM electrolyzer and thermoelectric generator[J]. International Journal of Hydrogen Energy, 2022, 47(57): 23919-23934.
14	Nafchi F M, Baniasadi E, Afshari E, et al. Performance assessment of a solar hydrogen and electricity production plant using high temperature PEM electrolyzer and energy storage[J]. International Journal of Hydrogen Energy, 2018, 43(11): 5820-5831.
15	Nazerifard R, Khani L, Mohammadpourfard M, et al. Design and thermodynamic analysis of a novel methanol, hydrogen, and power trigeneration system based on renewable energy and flue gas carbon dioxide[J]. Energy Conversion and Management, 2021, 233: 113922.
16	Sadeghi S, Ghandehariun S. Thermodynamic analysis and optimization of an integrated solar thermochemical hydrogen production system[J]. International Journal of Hydrogen Energy, 2020, 45(53): 28426-28436.
17	Thanganadar D, Fornarelli F, Camporeale S, et al. Off-design and annual performance analysis of supercritical carbon dioxide cycle with thermal storage for CSP application[J]. Applied Energy, 2021, 282: 116200.
18	刘鉴民. 太阳能热动力发电技术[M]. 北京: 化学工业出版社, 2012.
	Liu J M. Solar Thermal Power Generation Technology[M]. Beijing: Chemical Industry Press, 2012.
19	Sadeghi S, Ghandehariun S, Rezaie B. Energy and exergy analyses of a solar-based multi-generation energy plant integrated with heat recovery and thermal energy storage systems[J]. Applied Thermal Engineering, 2021, 188: 116629.
20	Zhang S A, Li K Y, Zhu P F, et al. An efficient hydrogen production process using solar thermo-electrochemical water-splitting cycle and its techno-economic analyses and multi-objective optimization[J]. Energy Conversion and Management, 2022, 266: 115859.
21	Sadeghi S, Ghandehariun S, Naterer G F. Exergoeconomic and multi-objective optimization of a solar thermochemical hydrogen production plant with heat recovery[J]. Energy Conversion and Management, 2020, 225: 113441.
22	Ni M, Leung M K H, Leung D Y C. Energy and exergy analysis of hydrogen production by a proton exchange membrane (PEM) electrolyzer plant[J]. Energy Conversion and Management, 2008, 49(10): 2748-2756.
23	Vosough A, Keshavarzi S. Parametric study of an ideal Rankine cycle with a reheat[J]. Applied Mechanics and Materials, 2011, 110/111/112/113/114/115/116: 4166-4170.
24	Saleh B, Koglbauer G, Wendland M, et al. Working fluids for low-temperature organic Rankine cycles[J]. Energy, 2007, 32(7): 1210-1221.
25	Ioroi T, Yasuda K, Siroma Z, et al. Thin film electrocatalyst layer for unitized regenerative polymer electrolyte fuel cells[J]. Journal of Power Sources, 2002, 112(2): 583-587.
26	Tan Z M, Feng X, Yang M B, et al. Energy and economic performance comparison of heat pump and power cycle in low grade waste heat recovery[J]. Energy, 2022, 260: 125149.
27	Zhang D, Hang P, Liu G L. Recycle optimization of an ethylene oxide production process based on the integration of heat exchanger network and reactor[J]. Journal of Cleaner Production, 2020, 275: 122773.
28	Jiang N, Han W Q, Guo F Y, et al. A novel heat exchanger network retrofit approach based on performance reassessment[J]. Energy Conversion and Management, 2018, 177: 477-492.
29	Zhu P F, Wu Z, Guo L L, et al. Achieving high-efficiency conversion and poly-generation of cooling, heating, and power based on biomass-fueled SOFC hybrid system: performance assessment and multi-objective optimization[J]. Energy Conversion and Management, 2021, 240: 114245.
30	Badenhorst H. A novel heat exchanger concept for latent heat thermal energy storage in solar power towers: modelling and performance comparison[J]. Solar Energy, 2016, 137: 90-100.
31	Chen C, Xia Q, Feng S M, et al. A novel solar hydrogen production system integrating high temperature electrolysis with ammonia based thermochemical energy storage[J]. Energy Conversion and Management, 2021, 237: 114143.
32	Hai T, Dhahad H A, Attia E A, et al. Design, modeling and multi-objective techno-economic optimization of an integrated supercritical Brayton cycle with solar power tower for efficient hydrogen production[J]. Sustainable Energy Technologies and Assessments, 2022, 53: 102599.
33	Sadeghi S, Ghandehariun S. A standalone solar thermochemical water splitting hydrogen plant with high-temperature molten salt: thermodynamic and economic analyses and multi-objective optimization[J]. Energy, 2022, 240: 122723.
34	Seyyedi S M, Hashemi-Tilehnoee M, Sharifpur M. Thermoeconomic analysis of a solar-driven hydrogen production system with proton exchange membrane water electrolysis unit[J]. Thermal Science and Engineering Progress, 2022, 30: 101274.
35	Niaz H, Lakouraj M M, Liu J. Techno-economic feasibility evaluation of a standalone solar-powered alkaline water electrolyzer considering the influence of battery energy storage system: a Korean case study[J]. Korean Journal of Chemical Engineering, 2021, 38(8): 1617-1630.
36	Wang G, Wang S K, Cao Y, et al. Design and performance evaluation of a novel hybrid solar-gas power and ORC-based hydrogen-production system[J]. Energy, 2022, 251: 123945.
37	Wang B Z, Yu X L, Chang J W, et al. Techno-economic analysis and optimization of a novel hybrid solar-wind-bioethanol hydrogen production system via membrane reactor[J]. Energy Conversion and Management, 2022, 252: 115088.
38	Al-Ghussain L, Ahmad A D, Abubaker A M, et al. Techno-economic feasibility of hybrid PV/wind/battery/thermal storage trigeneration system: toward 100% energy independency and green hydrogen production[J]. Energy Reports, 2023, 9: 752-772.

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