This paper proposes a new power generating system that combines wind power (WP), photovoltaic (PV), trough concentrating solar power (CSP) with a supercritical carbon dioxide (S-CO2) Brayton power cycle, a thermal energy storage (TES), and an electric heater (EH) subsystem. The wind power/photovoltaic/concentrating solar power (WP−PV−CSP) with the S-CO2 Brayton cycle system is powered by renewable energy. Then, it constructs a bi-level capacity-operation collaborative optimization model and proposes a non-dominated sorting genetic algorithm-II (NSGA-II) nested linear programming (LP) algorithm to solve this optimization problem, aiming to obtain a set of optimal capacity configurations that balance carbon emissions, economics, and operation scheduling. Afterwards, using Zhangbei area, a place in China which has significant wind and solar energy resources as a practical application case, it utilizes a bi-level optimization model to improve the capacity and annual load scheduling of the system. Finally, it establishes three reference systems to compare the annual operating characteristics of the WP−PV−CSP (S-CO2) system, highlighting the benefits of adopting the S-CO2 Brayton cycle and equipping the system with EH. After capacity-operation collaborative optimization, the levelized cost of energy (LCOE) and carbon emissions of the WP−PV−CSP (S-CO2) system are decreased by 3.43% and 92.13%, respectively, compared to the reference system without optimization.
. [J]. Frontiers in Energy, 2024, 18(5): 665-683.
Yangdi Hu, Rongrong Zhai, Lintong Liu. Capacity-operation collaborative optimization of the system integrated with wind power/photovoltaic/concentrating solar power with S-CO2 Brayton cycle. Front. Energy, 2024, 18(5): 665-683.
Recuperator heat transfer capacity UArecup/(kW·K−1)
61.62
616.18
3080.88
6161.76
12323.52
Pre-cooler heat transfer capacity UAcooler/(kW·K−1)
64.45
644.48
3222.40
6444.80
12889.60
Tab.4
Input parameters
Collection efficiency
DNI/(W·m−2)
Tin/K
Calculated data/%
Reference data/%
Error/%
600
490
81.93
81.90
0.03
600
510
79.92
80.26
0.34
600
530
77.91
77.84
0.07
600
550
75.51
74.47
1.04
600
570
69.16
69.98
0.82
600
590
62.11
64.19
2.08
700
490
82.58
82.29
0.29
700
510
80.64
81.00
0.36
700
530
78.94
79.14
0.20
700
550
75.31
76.55
1.24
700
570
74.54
73.12
1.42
700
590
69.53
68.72
0.81
Tab.5
Fig.3
Parameter
Decision variable
Unit
Boundary condition
Ri,1
NLoop
−
[165, 776]
Ri,2
CCSP
MW
[0, 200]
Ri,3
CTES
h
[0, 20]
Ri,4
CEH
MW
[0, 60]
Ri,5
CPV
MW
[0, 200]
Ri,6
NWT
−
[0, 66]
Tab.6
Fig.4
Fig.5
Parameter
Value
Unit
PV
Investment costs (ICs)
1003
$/kW
Operation and maintenance (O&M) costs
2.15
$/(kW·a)
Auxiliary equipment
350
$/kW
Inverter
105
$/kW
WP
ICs
909.98
$/kW
O&M costs
0.857
$/(kW·a)
Auxiliary equipment
35
$/kW
Inverter
105
$/kW
SF
ICs
242
$/m2
Auxiliary equipment
0.285 × IC
$/m2
O&M costs
0.017 × IC
$/(m2·a)
TES
ICs
31.4
$/kWh
Auxiliary equipment
0.285 × IC
$/kWh
O&M costs
0.017 × IC
$/(kWh·a)
S-CO2Brayton cycle
IC of compressor
$
IC of turbine
$
IC of heater
$
IC of recuperator
$
IC of cooler
$
Auxiliary equipment
340
$/kW
Fixed O&M costs
66
$/(kW·a)
Variable O&M costs
3500
$/kWh
Tab.7
Fig.6
Number
NLoop
CCSP/MW
CTES/h
CEH/MW
CPV/MW
NWT
LCOE/($·kWh−1)
CO2 emissions/(104 t·a−1)
1
165
10
14
27
23
0
0.1360
2.6299
…
24
165
12
14
33
34
13
0.1446
2.0756
…
55
291
26
14
42
48
26
0.1736
1.5584
…
108
659
74
12
60
104
50
0.2727
0.6406
…
163
395
169
5
40
74
17
0.3936
0.1208
…
200
456
178
4
39
186
43
0.5259
0.0021
Tab.8
Weight
NLoop
CCSP/MW
CTES/h
CEH/MW
CPV/MW
NWT
LCOE/($·kWh−1)
CO2 emissions/(104 t·a−1)
[0.45, 0.55]
257
64
14
59
117
49
0.2581
0.7186
[0.29, 0.71]
776
98
13
60
132
49
0.3342
0.3647
[0.21, 0.79]
395
170
5
39
79
18
0.3975
0.0914
[0.17, 0.83]
456
167
5
43
68
22
0.3990
0.0831
[0.14, 0.86]
456
167
5
43
68
22
0.3990
0.0831
[0.12, 0.88]
456
167
6
46
71
23
0.4088
0.0501
[0.10, 0.90]
456
167
7
45
72
19
0.4196
0.0370
[0.09, 0.91]
456
167
7
45
72
19
0.4196
0.0370
[0.08, 0.92]
456
167
7
45
72
19
0.4196
0.0370
Tab.9
Fig.7
Item
Reference System 1
Reference System 2
Reference System 3
WP−PV−CSP (S-CO2) system
Capacity configuration
NLoop
456
456
395
456
CCSP/MW
167
167
130
167
CTES/h
7
7
8
7
CEH/MW
0
45
20
45
CPV/MW
72
72
80
72
NWT
19
19
30
19
Economic parameter
LCOE/($·kWh−1)
0.4319
0.4401
0.4345
0.4196
System operation result
PPV/MW
50576
50576
49821
50576
PWP/MW
18555
18555
24596
18555
PCSP/MW
77021
81280
58611
82003
Ppurchase/MW
6531
2273
19656
1550
Pab/MW
67358
31750
59014
32039
QHT/MW
22109612
25757981
18107612
25487392
CO2 emissions/(t·a−1)
1563
543.97
4704
370
Tab.10
Fig.8
Fig.9
Fig.10
Fig.11
Fig.12
Abbreviations
CSP
Concentrating solar power
EH
Electric heater
HT
Hot tank
PV
Photovoltaic
S-CO2
Supercritical carbon dioxide
SF
Solar field
TES
Thermal energy storage
WP
Wind power
WT
Wind turbine
Variables
A
Area/m2
B
Coal consumption/t
C
Capacity/MW
DNI
Direct solar irradiation/(W·m−2)
GI
Global irradiance/(W·m−2)
h
Height/m
IC
Investment costs/$
LCOE
Levelized cost of electricity/($·kWh−1)
m
Mass flow rate/(kg·s−1)
P
Power/MW
Q
Quantity of heat/MW
T
Temperature/°C
v
Wind speed/(m·s−1)
W
Work/MW
η
Efficiency
Subscripts
a
Ambient
ab
Abandoned
C
Compressor
c
Charge
d
Discharge
HE
Heat exchanger
INV
Inverterin input
NOM
Normal
O&M
Operation and maintenance
out
Output
ref
Reference
s
Standard
T
Turbine
1
B V Ermolenko, G V Ermolenko, Y A Fetisova. et al.. Wind and solar PV technical potentials: Measurement methodology and assessments for Russia. Energy, 2017, 137: 1001–1012 https://doi.org/10.1016/j.energy.2017.02.050
2
S Mostafa Nosratabadi, R Hemmati, M Bornapour. et al.. Economic evaluation and energy/exergy analysis of PV/wind/PEMFC energy resources employment based on capacity, type of source and government incentive policies: Case study in Iran. Sustainable Energy Technologies and Assessments, 2021, 43: 100963 https://doi.org/10.1016/j.seta.2020.100963
3
C Xu, L Ge, H Feng. et al.. Review on status of wind power generation and composition and recycling of wind turbine blades. Thermal Power Generation, 2022, 51: 29–41
4
A Kamal, B Mohsine, A Abdelali. et al.. Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: A review. Renewable & Sustainable Energy Reviews, 2018, 93: 652–673 https://doi.org/10.1016/j.rser.2018.05.032
5
Y Zhang, H Sun, J Tan. et al.. Capacity configuration optimization of multi-energy system integrating wind turbine/photovoltaic/hydrogen/battery. Energy, 2022, 252: 124046 https://doi.org/10.1016/j.energy.2022.124046
6
Y Cao, M S Taslimi, S M Dastjerdi. et al.. Design, dynamic simulation, and optimal size selection of a hybrid solar/wind and battery-based system for off-grid energy supply. Renewable Energy, 2022, 187: 1082–1099 https://doi.org/10.1016/j.renene.2022.01.112
7
S Guo, Y He, H Pei. et al.. The multi-objective capacity optimization of wind-photovoltaic-thermal energy storage hybrid power system with electric heater. Solar Energy, 2020, 195: 138–149 https://doi.org/10.1016/j.solener.2019.11.063
8
L Pilotti, M Colombari, A F Castelli. et al.. Simultaneous design and operational optimization of hybrid CSP-PV plants. Applied Energy, 2023, 331: 120369 https://doi.org/10.1016/j.apenergy.2022.120369
9
K Riffelmann, G Weinrebe, M Balz. Hybrid CSP-PV plants with integrated thermal storage. AIP Conference Proceedings, 2022, 2445: 030020 https://doi.org/10.1063/5.0086610
10
Y Gedle, M Schmitz, H Gielen. et al.. Analysis of an integrated CSP-PV hybrid power plant. AIP Conference Proceedings, 2022, 2445: 030009 https://doi.org/10.1063/5.0086236
11
Y Ma, T Morozyuk, M Liu. et al.. Optimal integration of recompression supercritical CO2 Brayton cycle with main compression intercooling in solar power tower system based on exergoeconomic approach. Applied Energy, 2019, 242: 1134–1154 https://doi.org/10.1016/j.apenergy.2019.03.155
12
Y Liu, Y Wang, D Huang. Supercritical CO2 Brayton cycle: A state-of-the-art review. Energy 2019, 189: 115900
13
V Dostal, P Hejzlar, M J Driscoll. The supercritical carbon dioxide power cycle: Comparison to other advanced power cycles. Nuclear Technology, 2017, 154(3): 283–301 https://doi.org/10.13182/NT06-A3734
14
H Zhao, Q Deng, W Huang. et al.. Thermodynamic and economic analysis and multi-objective optimization of supercritical CO2 Brayton cycles. Journal of Engineering for Gas Turbines and Power: Transactions of the ASME, 2016, 138(8): 081602 https://doi.org/10.1115/1.4032666
15
T Xiao, C Liu, X Wang. et al.. Life cycle assessment of the solar thermal power plant integrated with air-cooled supercritical CO2 Brayton cycle. Renewable Energy, 2022, 182: 119–133 https://doi.org/10.1016/j.renene.2021.10.001
16
R Yuan, B Xu, J Wang. et al.. Analysis of supercritical carbon dioxide power generation system with trough solar collector as heat source. China Survey & Design, 2022, 3(S2): 34–37
17
J Yang, Z Yang, Y Duan. A review on integrated design and off-design operation of solar power tower system with S-CO2 Brayton cycle. Energy, 2022, 246: 123348 https://doi.org/10.1016/j.energy.2022.123348
18
J Yang, Z Yang, Y Duan. -Load matching and techno-economic analysis of CSP plant with S-CO2 Brayton cycle in CSP−PV−wind hybrid system. Energy, 2021, 223: 120016 https://doi.org/10.1016/j.energy.2021.120016
19
X Wang, X Li, Q Li. et al.. Performance of a solar thermal power plant with direct air-cooled supercritical carbon dioxide Brayton cycle under off-design conditions. Applied Energy, 2020, 261: 114359 https://doi.org/10.1016/j.apenergy.2019.114359
20
T Liu, Z Yang, Y Duan. et al.. Techno-economic assessment of hydrogen integrated into electrical/thermal energy storage in PV + wind system devoting to high reliability. Energy Conversion and Management, 2022, 268: 116067 https://doi.org/10.1016/j.enconman.2022.116067
21
J Yang, Z Yang, Y Duan. Novel design optimization of concentrated solar power plant with S-CO2 Brayton cycle based on annual off-design performance. Applied Thermal Engineering, 2021, 192: 116924 https://doi.org/10.1016/j.applthermaleng.2021.116924
22
H Liu, R Zhai, J Fu. et al.. Optimization study of thermal-storage PV-CSP integrated system based on GA-PSO algorithm. Solar Energy, 2019, 184: 391 https://doi.org/10.1016/j.solener.2019.04.017
23
M Chennaif, H Zahboune, M Elhafyani. et al.. Electric system cascade extended analysis for optimal sizing of an autonomous hybrid CSP/PV/wind system with battery energy storage system and thermal energy storage. Energy, 2021, 227: 120444 https://doi.org/10.1016/J.ENERGY.2021.120444
24
Q Tan, S Mei, M Dai. et al.. A multi-objective optimization dispatching and adaptability analysis model for wind-PV-thermal-coordinated operations considering comprehensive forecasting error distribution. Journal of Cleaner Production, 2020, 256: 120407 https://doi.org/10.1016/j.jclepro.2020.120407
25
Z Ding, H Hou, L Duan. et al.. Study on the capacity-operation collaborative optimization for multi-source complementary cogeneration system. Energy Conversion and Management, 2021, 250: 114920 https://doi.org/10.1016/j.enconman.2021.114920
26
M L E Mohammed Chennaif, Z Hassan. Electric system cascade analysis for optimal sizing of an autonomous photovoltaic water pumping system. Advances in Smart Technologies Applications and Case Studies, 2020, 684: 282–290
27
Chennaif M. Elhafyani M L, Zahboune H, et al. The impact of the tilt angle on the sizing of autonomous photovoltaic systems using electric system cascade analysis. In: Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems. Berlin: Springer, 2021, 767–776
28
R Zhai, H Liu, Y Chen. et al.. The daily and annual technical-economic analysis of the thermal storage PV-CSP system in two dispatch strategies. Energy Conversion and Management, 2017, 154: 56–67 https://doi.org/10.1016/j.enconman.2017.10.040
29
B K Das, M S H K Tushar, R Hassan. Techno-economic optimisation of stand-alone hybrid renewable energy systems for concurrently meeting electric and heating demand. Sustainable Cities and Society, 2021, 68: 102763 https://doi.org/10.1016/j.scs.2021.102763
30
Z Yang, R Kang, X Luo. et al.. Rigorous modelling and deterministic multi-objective optimization of a super-critical CO2 power system based on equation of state and non-linear programming. Energy Conversion and Management, 2019, 198: 111798 https://doi.org/10.1016/j.enconman.2019.111798
31
L Liu, R Zhai, Y Hu. Performance evaluation of wind-solar-hydrogen system for renewable energy generation and green hydrogen generation and storage: Energy, exergy, economic, and enviroeconomic. Energy, 2023, 276: 127386 https://doi.org/10.1016/J.ENERGY.2023.127386
32
L Liu, R Zhai, Y Hu. Multi-objective optimization with advanced exergy analysis of a wind-solar-hydrogen multi-energy supply system. Applied Energy, 2023, 348: 121512 https://doi.org/10.1016/j.apenergy.2023.121512
33
K Wang, M Li, J Guo. et al.. A systematic comparison of different S-CO2 Brayton cycle layouts based on multi-objective optimization for applications in solar power tower plants. Applied Energy, 2018, 212: 109–121 https://doi.org/10.1016/j.apenergy.2017.12.031
34
J Yang, Z Yang, Y Duan. S-CO2 tower solar thermal power generation system with different installed capacity thermal and economic performance analysis. Acta Energiae Solaris Sinica, 2022, 43: 125–130 https://doi.org/10.19912/j.0254-0096tynxb.2021-0244
35
A S Alsagri, A Chiasson, M Gadalla. Viability assessment of a concentrated solar power tower with a supercritical CO2 Brayton cycle power plant. Journal of Solar Energy Engineering, 2019, 141(5): 051006 https://doi.org/10.1115/1.4043515
36
Y Liu, Y Wang, Y Zhang. et al.. Design and performance analysis of compressed CO2 energy storage of a solar power tower generation system based on the S-CO2 Brayton cycle. Energy Conversion and Management, 2021, 249: 114856 https://doi.org/10.1016/j.enconman.2021.114856
37
S Wu, C Zhou, E Doroodchi. et al.. Techno-economic analysis of an integrated liquid air and thermochemical energy storage system. Energy Conversion and Management, 2020, 205: 112341 https://doi.org/10.1016/j.enconman.2019.112341
38
I H A Mohamad, V K Ramachandaramurthya, P B Sanjeevikumar. et al.. NSGA-II and MOPSO based optimization for sizing of hybrid PV/wind/battery energy storage system. International Journal of Power Electronics and Drive Systems, 2019, 10(1): 463–478
39
Y Du, K Gao. Ecological security evaluation of marine ranching with AHP-entropy-based TOPSIS: A case study of Yantai, China. Marine Policy, 2020, 122: 104223 https://doi.org/10.1016/j.marpol.2020.104223
40
D Niu, G Wu, Z Ji. et al.. Evaluation of provincial carbon neutrality capacity of China based on combined weight and improved TOPSIS model. Sustainability, 2021, 13(5): 2777 https://doi.org/10.3390/su13052777
41
Z Luo, S Yang, N Xie. et al.. Multi-objective capacity optimization of a distributed energy system considering economy, environment and energy. Energy Conversion and Management, 2019, 200: 112081 https://doi.org/10.1016/j.enconman.2019.112081
42
EnergyPlus. Weather data—Hebei Zhangbei 533990 (CSWD). 2023, available at the website of EnergyPlus
43
X Wang, Q Zhu, Y Wang. Optimal allocation of wind-solar storage capacity of microgrid considering carbon emission reduction benefits. IOP Conference Series. Earth and Environmental Science, 2021, 804(3): 032015 https://doi.org/10.1088/1755-1315/804/3/032015
44
M Koleva, O J Guerra, J Eichman. et al.. Optimal design of solar-driven electrolytic hydrogen production systems within electricity markets. Journal of Power Sources, 2021, 483: 229183 https://doi.org/10.1016/j.jpowsour.2020.229183
45
X Chen, H Zhou, W Li. et al.. Multi-criteria assessment and optimization study on 5 kW PEMFC based residential CCHP system. Energy Conversion and Management, 2018, 160: 384–395 https://doi.org/10.1016/j.enconman.2018.01.050