An improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-based collaborative optimization control strategy of wind-hydrogen-electrochemical energy storage coupled system with the interconversion characteristics between hydrogen with electricity under multiple application scenarios is introduced in this paper. After identifying the grid-connected wind power based on the ICEEMDAN algorithm, the normalized maximum discrepancy of the modal functions divides the high-frequency modal components into the fluctuating power smoothed by lithium iron phosphate batteries and hydrogen storage, with wind power curtailment from grid connection being consumed by electrolysis of water for hydrogen in alkaline electrolyzers. Another novelty is a collaborative optimization strategy for hydrogen-electrochemical energy storage under two application scenarios, comparing the smoothing effect and the ability to eliminate wind curtailment with different energy storage schemes. Demonstrate the method's effectiveness through the certain operational data from a Chinese wind farm. Simulation results indicate that the coupled system results in 19.45% and 7.79% cost reduction compared to other schemes, and the collaborative optimization control strategy achieves complete wind curtailment, which further improves the capacity of consuming curtailed wind power while smoothing fluctuations and providing certain engineering application value.
REFERENCES
1.
P.
Yun
,
Y.
Ren
, and
Y.
Xue
, “
Energy-storage optimization strategy for reducing wind power fluctuation via Markov prediction and PSO method
,” Energies
11
(12
), 3393
(2018
).2.
M.
Washim Akram
,
M.
Arman Arefin
, and
A.
Nusrat
, “
Prospect of green power generation as a solution to energy crisis in Bangladesh
,” Energy Syst.
13
, 749
–787
(2021
).3.
G.
Ren
,
J.
Wan
,
J.
Liu
et al, “
Characterization of wind resource in China from a new perspective
,” Energy
167
(15
), 994
–1010
(2019
).4.
M.
David
,
C.
Ocampo-Martínez
, and
R.
Sánchez-Peña
, “
Advances in alkaline water electrolyzers: A review
,” J. Energy Storage
23
, 392
–403
(2019
).5.
B.
Widera
, “
Renewable hydrogen implementations for combined energy storage, transportation and stationary applications
,” Therm. Sci. Eng. Prog.
16
(1
), 100460
(2020
).6.
D.
Apostolou
and
P.
Enevoldsen
, “
The past, present and potential of hydrogen as a multifunctional storage application for wind power
,” Renewable Sustainable Energy Rev.
112
, 917
–929
(2019
).7.
W. C.
Nadaleti
,
G.
Borges dos Santos
, and
V. A.
Lourenço
, “
The potential and economic viability of hydrogen production from the use of hydroelectric and wind farms surplus energy in Brazil: A national and pioneering analysis
,” Int. J. Hydrogen Energy
45
(3
), 1373
–1384
(2020
).8.
K.
Hacatoglu
,
I.
Dincer
, and
M. A.
Rosen
, “
Sustainability of a wind-hydrogen energy system: Assessment using a novel index and comparison to a conventional gas-fired system
,” Int. J. Hydrogen Energy
41
(19
), 8376
–8385
(2016
).9.
M. H.
Aboukalam da Cruz
,
M.
Etancelin
,
F.
Marias
et al, “
Dynamic modelling of an alkaline water electrolysis system and optimization of its operating parameters for hydrogen production
,” Int. J. Hydrogen Energy
35
, 12982
–12999
(2023
).10.
Y.
Jiang
,
Z.
Deng
, and
S.
You
, “
Size optimization and economic analysis of a coupled wind-hydrogen system with curtailment decisions
,” Int. J. Hydrogen Energy
44
(36
), 19658
–19666
(2019
).11.
F.
Wei
,
Q.
Sui
,
X.
Li
et al, “
Optimal dispatching of power grid integrating wind-hydrogen systems
,” Electr. Power Energy Syst.
125
, 106489
(2021
).12.
E. M.
Barhoumi
,
M. S.
Salhi
,
P. C.
Okonkwo
et al, “
Techno-economic optimization of wind energy based hydrogen refueling station case study Salalah city Oman
,” Int. J. Hydrogen Energy
48
(26
), 9529
–9539
(2023
).13.
Y.
Sahri
,
Y.
Belkhier
,
S.
Tamalouzt
et al, “
Energy management system for hybrid PV/wind/battery/fuel cell in microgrid-based hydrogen and economical hybrid battery/super capacitor energy storage
,” Energies
14
(18
), 5722
–5722
(2021
).14.
M.
Trifkovic
,
M.
Sheikhzadeh
,
K.
Nigim
et al, “
Modeling and control of a renewable hybrid energy system with hydrogen storage
,” IEEE Trans. Control Systems Technol.
22
(1
), 169
–179
(2014
).15.
M. B.
Abdelghany
,
M. F.
Shehzad
,
D.
Liuzza
et al, “
Optimal operations for hydrogen-based energy storage systems in wind farms via model predictive control
,” Int. J. Hydrogen Energy
46
(57
), 29297
–29313
(2021
).16.
H.
Kojima
,
K.
Nagasawa
,
N.
Todoroki
et al, “
Influence of renewable energy power fluctuations on water electrolysis for green hydrogen production
,” Int. J. Hydrogen Energy
48
(12
), 4572
–4593
(2023
).17.
B.
Bendjedia
,
N.
Rizoug
,
M.
Boukhnifer
et al, “
Influence of secondary source technologies and energy management strategies on energy storage system sizing for fuel cell electric vehicles
,” Int. J. Hydrogen Energy
43
(25
), 11614
–11628
(2018
).18.
L.
Ji
,
Y.
Zhao
,
F.
Wang
et al, “
Status of hydrogen energy technology and its application in the field of energy storage and power generation
,” Metallic Funct. Mater.
26
(06
), 23
–31
(2019
).19.
R.
Yan
,
Y.
Chen
, and
X.
Zhu
, “
Optimization of operating hydrogen storage system for coal–wind–solar power generation
,” Energies
15
(14
), 5015
–5015
(2022
).20.
R.
Fang
and
Y.
Liang
, “
Control strategy of electrolyzer in a wind-hydrogen system considering the constraints of switching times
,” Int. J. Hydrogen Energy
44
(46
), 25104
–25111
(2019
).21.
L.
Kong
,
J.
Yu
, and
G.
Cai
, “
Modeling, control and simulation of a photovoltaic/hydrogen/supercapacitor hybrid power generation system for grid-connected applications
,” Int. J. Hydrogen Energy
44
, 25129
–25144
(2019
).22.
Y.
Lu
,
C.
Chong
,
L.
Liang
et al, “
Modeling and control of wind-hydrogen coupling system based on electricity-hydrogen hybrid energy storage
,” Smart Power
48
(3
), 7
–14
(2020
).23.
Y.
Xu
,
Y.
Xu
, and
Y.
Huang
, “
Generation of typical operation curves for hydrogen storage applied to the wind power fluctuation smoothing mode
,” Global Energy Interconnect.
5
(4
), 353
–361
(2022
).24.
S.
Fukaume
,
Y.
Nagasaki
, and
M.
Tsuda
, “
Stable power supply of an independent power source for a remote island using a hybrid energy storage system composed of electric and hydrogen energy storage systems
,” Int. J. Hydrogen Energy
47
(29
), 13887
–13899
(2022
).25.
Z.
Zhao
, “
Improved fuzzy logic control-based energy management strategy for hybrid power system of FC/PV/battery/SC on tourist ship
,” Int. J. Hydrogen Energy
47
(16
), 9719
–9734
(2022
).26.
N.
Harpak
,
G.
Davidi
, and
F.
Patolsky
, “
Breathing parylene-based nanothin artificial SEI for highly-stable long life three-dimensional silicon lithium-ion batteries
,” Chem. Eng. J.
429
, 132077
(2022
).27.
Y.
Wang
,
Y.
Sun
,
Y.
Zhang
et al, “
Optimal modeling and analysis of microgrid LFP battery energy storage system under different power supply states
,” J. Power Sources
521
, 230931
(2022
).28.
A. X. Y.
Mah
,
W. S.
Ho
,
M. H.
Hassim
et al, “
Optimization of a standalone photovoltaic-based microgrid with electrical and hydrogen loads
,” Energy
235
, 121218
(2021
).29.
L.
Ding
,
J.
Gao
,
G.
Shi
et al, “
Robust optimal dispatch of integrated energy system considering with coupled wind and hydrogen system
,” J. Phys.: Conf. Ser.
2215
(1
), 012001
(2022
).30.
L.
Phan Van
,
L.
Hoang Hieu
,
K.
Do Chi
et al, “
An improved state machine-based energy management strategy for renewable energy microgrid with hydrogen storage system
,” Energy Rep.
9
, 194
–201
(2023
).31.
Y.
Huangfu
,
C.
Tian
,
S.
Zhuo
et al, “
An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogenhybrid energy system
,” Int. J. Hydrogen Energy
48
, 3154
–3170
(2023
).32.
H.
Armghan
,
M.
Yang
,
N.
Ali
et al, “
Quick reaching law based global terminal sliding mode control for wind/hydrogen/battery DC microgrid
,” Appl. Energy
316
(15
), 119050
(2022
).33.
M. E.
Torres
,
M. A.
Colominas
,
G.
Schlotthauer
et al, “
A complete ensemble empirical mode decomposition with adaptive noise
,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
, 2011
.34.
M. A.
Colominas
,
G.
Schlotthauer
, and
M. E.
Torres
, “
Improved complete ensemble EMD: A suitable tool for biomedical signal processing
,” Biomed. Signal Process. Control
14
, 19
–29
(2014
).35.
China Electricity Council, “
Technical regulations for connection of wind farms to the electricity system
,” Report No. GB/T 19963.1-2021, 2021.36.
Z.
Xie
,
H.
Wang
, and
W.
Wang
, “
Wind power fluctuation smoothing strategy of hybrid energy storage system using adaptive VMD and energy management control
,” Electr. Meas. Instrum.
58
(10
), 87
–94
(2021
).37.
C.
Xie
,
J.
Zhang
, and
X.
Li
, “
Hybrid energy storage control strategy based on EEMD and fuzzy control
,” Electr. Meas. Instrum.
56
(20
), 124
–129
(2019
).38.
J.
Linghu
,
L.
Kang
,
M.
Liu
et al, “
Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter
,” Energy
189
, 116204
(2019
).39.
J.
Zhu
,
M.
Knapp
,
D. R.
Sørensen
et al, “
Investigation of capacity fade for 18650-type lithium-ion batteries cycled in different state of charge (SoC) ranges
,” J. Power Sources
489
, 229422
(2021
).40.
A. C.
Ince
,
M.
Fazıl Serincan
,
C. O.
Colpan
et al, “
A mini review on mathematical modeling of co-electrolysis at cell, stack and system levels
,” Fuel Process. Technol.
244
(1
), 107724
(2023
).41.
Saur
, “
Wind-to-hydrogen project: Electrolyzer capital cost study
,” Report No. NREL/TP-550-44103
, [
National Renewable Energy Laboratory (NREL)
,
Golden
,
CO
, 2017
].42.
B.
Gregor
,
S.
Max
, and
S.
Simon
, “
Estimating long-term global supply costs for low-carbon hydrogen
,” Appl. Energy
302
, 117481
(2021
).© 2023 Author(s). Published under an exclusive license by AIP Publishing.
2023
Author(s)
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