TY - JOUR
T1 - Green Certificate-Driven Photovoltaic Promotion in Distribution Networks Hosting Hydrogen Fueling Stations for Future Sustainable Transportation
T2 - A Risk-Adjusted Dominance Analysis
AU - GUO, Xiaoqiang
AU - LI, Xiao
AU - Mohammed, F. Adil Hussein
AU - Alenizi, Farhan A.
AU - Alasedi, Kasim Kadhim
AU - Mohsen, Karrar Shareef
AU - Alsaalamy, Ali
AU - El-Shafai, Walid
AU - Uktamov, Khusniddin Fakhriddinovich
AU - Mehbodniya, Abolfazl
AU - Bostani, Ali
N1 - Funding Information:
This study is supported via funding from Prince sattam bin Abdulaziz University project number ( PSAU/2023/R/1444 ).
Funding Information:
Natural Science Foundation Project of Sichuan Province (Grant No. 2022NSFSC1922 ), Key Laboratory of Gas Hydrate, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences (No. E229kf15 )
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12
Y1 - 2023/12
N2 - The growing number of hydrogen vehicles (HVs) has necessitated the development of hydrogen fueling stations (HFSs) to meet the hydrogen demand. This development will target environmental concerns related to electricity generation as HFSs consume power to convert electricity into hydrogen. This study focuses on the optimal risk-aware scheduling problem of a distributed network highly penetrated with photovoltaic (PV) resources. The model addresses the optimal operation of HFs under time-of-use, demand response, and multi-market mechanisms with an expanded role for PV generation under the green certificate (GCT) approach. This brings further environmental and economic benefits, as there is a growing global emphasis on the shift to a low-carbon economy. However, the uncertainties arising from PV operation, HVs’ demand, electricity load, and market prices, potentially affect the decision-maker's ability under the risky conditions. Though second-order stochastic dominance (STD) is implemented for risk management. Results show that applying the GCT method increases 5% (from 0.52 to 0.61 MW) of renewable generation and reduces 23% (300 kg) of CO2 emissions. As the conservativity of decision-makers enhances, 10% of further operation costs are imposed on the system. Results indicate that next to curbing CO2 emissions, the flexibility and robustness of the system can be improved.
AB - The growing number of hydrogen vehicles (HVs) has necessitated the development of hydrogen fueling stations (HFSs) to meet the hydrogen demand. This development will target environmental concerns related to electricity generation as HFSs consume power to convert electricity into hydrogen. This study focuses on the optimal risk-aware scheduling problem of a distributed network highly penetrated with photovoltaic (PV) resources. The model addresses the optimal operation of HFs under time-of-use, demand response, and multi-market mechanisms with an expanded role for PV generation under the green certificate (GCT) approach. This brings further environmental and economic benefits, as there is a growing global emphasis on the shift to a low-carbon economy. However, the uncertainties arising from PV operation, HVs’ demand, electricity load, and market prices, potentially affect the decision-maker's ability under the risky conditions. Though second-order stochastic dominance (STD) is implemented for risk management. Results show that applying the GCT method increases 5% (from 0.52 to 0.61 MW) of renewable generation and reduces 23% (300 kg) of CO2 emissions. As the conservativity of decision-makers enhances, 10% of further operation costs are imposed on the system. Results indicate that next to curbing CO2 emissions, the flexibility and robustness of the system can be improved.
KW - Demand response program
KW - Green certificate
KW - Hydrogen fueling station
KW - Hydrogen vehicle
KW - second-order stochastic dominance
UR - http://www.scopus.com/inward/record.url?scp=85170826351&partnerID=8YFLogxK
U2 - 10.1016/j.scs.2023.104911
DO - 10.1016/j.scs.2023.104911
M3 - Article
AN - SCOPUS:85170826351
SN - 2210-6707
VL - 99
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
M1 - 104911
ER -