IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i8p1991-d161077.html
   My bibliography  Save this article

Operation and Economic Assessment of Hybrid Refueling Station Considering Traffic Flow Information

Author

Listed:
  • Suyang Zhou

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Yuxuan Zhuang

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Wei Gu

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Zhi Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

Abstract

It is anticipated that the penetration of “Green-Energy” vehicles, including Electric Vehicle (EV), Fuel Cell Vehicle (FCV), and Natural Gas Vehicle (NGV) will keep increasing in next decades. The demand of refueling stations will correspondingly increase for refueling these “Green-Energy” vehicles. While such kinds of “Green-Energy” vehicles can provide both social and economic benefits, effective management of refueling various kinds of these vehicles is necessary to maintain vehicle users’ comfortabilities and refueling station’s return on investment. To tackle these problems, this paper proposes a novel energy management approach for hybrid refueling stations with EV chargers, Hydrogen pumps and gas pumps. Firstly, the detailed models of EV chargers, Hydrogen pumps with electrolyte and hydrogen tank, the gas pumps with gas tank, renewable resources, and battery energy storage systems are established. The forecasting methodologies for renewable energy, electricity price and the traffic flow are also presented to support the hybrid refueling station modeling and operation. Then, a management approach is adopted to manage the refueling various kinds of vehicles with considerations of the refueling station profitability. Finally, the proposed management approach is verified under four different kinds of tariffs- Economy-7, Economy-10, Flat-rate, and Real-Time Pricing (RTP), finding that the proposed management approach has the best performance under RTP tariff. The economic assessment of the Energy Storage System (ESS) is also performed. It is found that the ESS can make the saving up to $127 per day. Different sizes of gas storage tank are compared in the final section as well. The result shows that increasing the size of the tank does not bring attractive extra benefits with the consideration of the investment on enlarging the tank size.

Suggested Citation

  • Suyang Zhou & Yuxuan Zhuang & Wei Gu & Zhi Wu, 2018. "Operation and Economic Assessment of Hybrid Refueling Station Considering Traffic Flow Information," Energies, MDPI, vol. 11(8), pages 1-20, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1991-:d:161077
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/8/1991/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/8/1991/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    2. Kagiri, Charles & Wanjiru, Evan M. & Zhang, Lijun & Xia, Xiaohua, 2018. "Optimized response to electricity time-of-use tariff of a compressed natural gas fuelling station," Applied Energy, Elsevier, vol. 222(C), pages 244-256.
    3. Michael Kuby & Seow Lim, 2007. "Location of Alternative-Fuel Stations Using the Flow-Refueling Location Model and Dispersion of Candidate Sites on Arcs," Networks and Spatial Economics, Springer, vol. 7(2), pages 129-152, June.
    4. Luo, Lizi & Gu, Wei & Zhang, Xiao-Ping & Cao, Ge & Wang, Weijun & Zhu, Gang & You, Dingjun & Wu, Zhi, 2018. "Optimal siting and sizing of distributed generation in distribution systems with PV solar farm utilized as STATCOM (PV-STATCOM)," Applied Energy, Elsevier, vol. 210(C), pages 1092-1100.
    5. Shunichi Hienuki, 2017. "Environmental and Socio-Economic Analysis of Naphtha Reforming Hydrogen Energy Using Input-Output Tables: A Case Study from Japan," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    6. Shubo Hu & Hui Sun & Feixiang Peng & Wei Zhou & Wenping Cao & Anlong Su & Xiaodong Chen & Mingze Sun, 2018. "Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads," Energies, MDPI, vol. 11(7), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhi Wu & Yuxuan Zhuang & Suyang Zhou & Shuning Xu & Peng Yu & Jinqiao Du & Xiner Luo & Ghulam Abbas, 2020. "Bi-Level Planning of Multi-Functional Vehicle Charging Stations Considering Land Use Types," Energies, MDPI, vol. 13(5), pages 1-17, March.
    2. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    3. Suyang Zhou & Jinyi Chen & Zhi Wu & Yue Qiu, 2021. "Electrification of Online Ride-Hailing Vehicles in China: Intention Modelling and Market Prediction," Energies, MDPI, vol. 14(21), pages 1-21, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guido C. Guerrero-Liquet & Santiago Oviedo-Casado & J. M. Sánchez-Lozano & M. Socorro García-Cascales & Javier Prior & Antonio Urbina, 2018. "Determination of the Optimal Size of Photovoltaic Systems by Using Multi-Criteria Decision-Making Methods," Sustainability, MDPI, vol. 10(12), pages 1-18, December.
    2. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    3. Luo, Lizi & Wu, Zhi & Gu, Wei & Huang, He & Gao, Song & Han, Jun, 2020. "Coordinated allocation of distributed generation resources and electric vehicle charging stations in distribution systems with vehicle-to-grid interaction," Energy, Elsevier, vol. 192(C).
    4. Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2020. "Location optimisation method for fast-charging stations along national roads," Journal of Transport Geography, Elsevier, vol. 88(C).
    5. Shihui Tian & Guowei Hua & T. C. E. Cheng, 2019. "Optimal Deployment of Charging Piles for Electric Vehicles Under the Indirect Network Effects," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(01), pages 1-17, February.
    6. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    7. Kuby, Michael & Capar, Ismail & Kim, Jong-Geun, 2017. "Efficient and equitable transnational infrastructure planning for natural gas trucking in the European Union," European Journal of Operational Research, Elsevier, vol. 257(3), pages 979-991.
    8. Farzaneh Ferdowsi & Hamid Reza Maleki & Sanaz Rivaz, 2020. "Air refueling tanker allocation based on a multi-objective zero-one integer programming model," Operational Research, Springer, vol. 20(4), pages 1913-1938, December.
    9. S. A. MirHassani & R. Ebrazi, 2013. "A Flexible Reformulation of the Refueling Station Location Problem," Transportation Science, INFORMS, vol. 47(4), pages 617-628, November.
    10. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    11. Makena Coffman & Paul Bernstein & Sherilyn Wee, 2017. "Electric vehicles revisited: a review of factors that affect adoption," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 79-93, January.
    12. Liu, Haoxiang & Wang, David Z.W., 2017. "Locating multiple types of charging facilities for battery electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 30-55.
    13. Wee, Sherilyn & Coffman, Makena & Allen, Scott, 2020. "EV driver characteristics: Evidence from Hawaii," Transport Policy, Elsevier, vol. 87(C), pages 33-40.
    14. Vavilapalli, Sridhar & Umashankar, S. & Sanjeevikumar, P. & Ramachandaramurthy, Vigna K. & Mihet-Popa, Lucian & Fedák, Viliam, 2018. "Three-stage control architecture for cascaded H-Bridge inverters in large-scale PV systems – Real time simulation validation," Applied Energy, Elsevier, vol. 229(C), pages 1111-1127.
    15. Reza Sirjani, 2018. "Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization," Sustainability, MDPI, vol. 10(3), pages 1-15, March.
    16. David Schindl & Nicolas Zufferey, 2015. "A learning tabu search for a truck allocation problem with linear and nonlinear cost components," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(1), pages 32-45, February.
    17. Hwang, Seong Wook & Kweon, Sang Jin & Ventura, Jose A., 2017. "Locating alternative-fuel refueling stations on a multi-class vehicle transportation network," European Journal of Operational Research, Elsevier, vol. 261(3), pages 941-957.
    18. Luo, Lizi & Gu, Wei & Wu, Zhi & Zhou, Suyang, 2019. "Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation," Applied Energy, Elsevier, vol. 242(C), pages 1274-1284.
    19. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    20. Guo, Fang & Yang, Jun & Lu, Jianyi, 2018. "The battery charging station location problem: Impact of users’ range anxiety and distance convenience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 1-18.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1991-:d:161077. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.