Planning of a charging station for electric and hydrogen vehicles under hydrogen storage and fuel cell systems using a novel stochastic p-robust optimization technique

被引:0
|
作者
Jian, Peiru [1 ]
Xiang, Si [2 ]
Sabzalian, Mohammad Hosein [3 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Wuhan 430074, Hubei, Peoples R China
[2] Grandall Law Firm Wuhan, Hongtai Bldg,1 Huanle Ave, Wuhan, Peoples R China
[3] Univ Santiago Chile USACH, Fac Engn, Dept Mech Engn, Ave Libertador Bernardo OHiggins 3363, Santiago 9170022, Chile
关键词
Hydrogen vehicles (HVs) and electric vehicles (EVs); Hydrogen storage system (HSS); Stochastic optimization technique (SOT); Stochastic p-robust optimization technique (SPROT); Off-grid charging station (OGCS); Maximum relative regret (MRR); ENERGY MANAGEMENT; PERFORMANCE;
D O I
10.1016/j.ijhydene.2024.09.228
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
This article presented a robust plan for an off-grid charging station (OGCS) for electric vehicles (EVs) and hydrogen vehicles (HVs) based on a photovoltaic (PV) system and a hydrogen storage system (HSS). This OGCS simultaneously supplies HVs and EVs continuously throughout the day. Also, HSS and fuel cell (FC) systems have been allocated in the OGCS to be used when we do not have access to the power of the PV system. In addition, a diesel generator (DG) is also designed to prevent in cases where we have extreme uncertainty, including the lack of energy in the PV system and the high load of the system, which may lead to load interruption. Uncertainties of electric and hydrogen loads of EVs and HVs in addition to PV production power are simulated using scenario-based stochastic optimization technique (SOT). Finally, a new framework based on stochastic p-robust optimization technique (SPROT) is applied to optimize the maximum relative regret (MRR) in the worst scenario in order to achieve robust planning in the uncertain environment. The obtained results from the proposed SPROT are compared with SOT. The compared results indicate a 4.51% raise in the average cost in SPROT and a 45.73% decrease in MRR that leads to robust planning. Finally, installed capacity of PV system will decrease from 1688 to 1685 kW, while installed capacity of DG will increase from 78 to 123 kW.
引用
收藏
页码:702 / 712
页数:11
相关论文
共 17 条
  • [1] Optimal economic management of an electric vehicles aggregator by using a stochastic p-robust optimization technique
    Sriyakul, Thanaporn
    Jermsittiparsert, Kittisak
    [J]. JOURNAL OF ENERGY STORAGE, 2020, 32
  • [2] Off-grid solar powered charging station for electric and hydrogen vehicles including fuel cell and hydrogen storage
    Mehrjerdi, Hasan
    [J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2019, 44 (23) : 11574 - 11583
  • [3] Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming
    Cai, Tingting
    Dong, Mingyu
    Liu, Huanan
    Nojavan, Sayyad
    [J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2022, 47 (01) : 443 - 458
  • [4] Energy management of an intelligent parking lot equipped with hydrogen storage systems and renewable energy sources using the stochastic p-robust optimization approach
    Habib, Salman
    Aghakhani, Sina
    Nejati, Mobin Ghasempour
    Azimian, Mahdi
    Jia, Youwei
    Ahmed, Emad M.
    [J]. ENERGY, 2023, 278
  • [5] Optimal performance of hybrid energy system in the presence of electrical and heat storage systems under uncertainties using stochastic p-robust optimization technique
    Yu, Dongmin
    Wu, Juntao
    Wang, Weidong
    Gu, Bing
    [J]. SUSTAINABLE CITIES AND SOCIETY, 2022, 83
  • [6] Thermal behavior in a Type IV hydrogen storage tank of fuel cell electric vehicles under different charging conditions
    Liu, Zhan
    Yuan, Kaifeng
    Li, Xiaozhao
    Yang, Danan
    [J]. International Journal of Hydrogen Energy, 2025, 99 : 517 - 527
  • [7] Robust design of off-grid solar-powered charging station for hydrogen and electric vehicles via robust optimization approach
    Wang, Yun
    Kazemi, Milad
    Nojavan, Sayyad
    Jermsittiparsert, Kittisak
    [J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2020, 45 (38) : 18995 - 19006
  • [8] Risk-based design of hydrogen storage-based charging station for hydrogen and electric vehicles using downside risk constraint approach
    Guo, Qun
    Zhou, Hui
    Lin, Wang
    Nojavan, Sayyad
    [J]. JOURNAL OF ENERGY STORAGE, 2022, 48
  • [9] Optimised operation of power sources of a PV/battery/hydrogen-powered hybrid charging station for electric and fuel cell vehicles
    Garcia-Trivino, Pablo
    Torreglosa, Juan P.
    Jurado, Francisco
    Fernandez Ramirez, Luis M.
    [J]. IET RENEWABLE POWER GENERATION, 2019, 13 (16) : 3022 - 3032
  • [10] Optimal planning and allocation of Plug-in Hybrid Electric Vehicles charging stations using a novel hybrid optimization technique
    Subramaniam, Ayyappan
    Singh, Lal Raja Singh Ravi
    [J]. PLOS ONE, 2023, 18 (07):