IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i12p4836-d370972.html
   My bibliography  Save this article

Modeling and Analysis of Electric Vehicle-Power Grid-Manufacturing Facility (EPM) Energy Sharing System under Time-of-Use Electricity Tariff

Author

Listed:
  • Xiaolin Chu

    (School of Financial Technology, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China)

  • Yuntian Ge

    (Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA)

  • Xue Zhou

    (Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA)

  • Lin Li

    (Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA)

  • Dong Yang

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)

Abstract

Electric vehicles (EVs) have obtained increasing public interest due to the associated economic and environmental benefits. Recently, studies regarding the economic advantages of adopting EVs as energy storages for commercial/residential buildings are emerging. In fact, according to the U.S. Energy Information Administration, the industrial sector consumes more energy than all of the other sectors combined, which is about 54% of the world’s total delivered energy. The energy consumption pattern in manufacturing facilities is based on production schedules and the heat transfer between machines and the ambient surroundings, thus, differs greatly from commercial/residential buildings. However, little research attention has been given to analyse the synergies of integrating EVs and manufacturing facilities to improve energy efficiency. To fill this research gap, in this study, a comprehensive model is established to evaluate the economic and environmental performance of an energy sharing system that consists of the EVs, power grid, and manufacturing facilities (EPM) under Time-of-Use (TOU) electricity tariff. The model is formulated as a mixed integer nonlinear programming format by considering practical production schedules, heat exchange between machines and ambient surroundings, as well as the heating, ventilation, and air conditioning (HVAC) system. The case study results indicate that the presented EPM energy sharing system has great potential to reduce energy cost and CO 2 emissions. In addition, compared to the results from winter scenarios, it is shown that more cost savings can be achieved in summer days.

Suggested Citation

  • Xiaolin Chu & Yuntian Ge & Xue Zhou & Lin Li & Dong Yang, 2020. "Modeling and Analysis of Electric Vehicle-Power Grid-Manufacturing Facility (EPM) Energy Sharing System under Time-of-Use Electricity Tariff," Sustainability, MDPI, vol. 12(12), pages 1-27, June.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:4836-:d:370972
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/12/4836/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/12/4836/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huang, Bin & Bai, Lihui & Roy, Arnab & Ma, Ningning, 2018. "A multi-criterion partner selection problem for virtual manufacturing enterprises under uncertainty," International Journal of Production Economics, Elsevier, vol. 196(C), pages 68-81.
    2. Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying, 2016. "Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 720-732.
    3. Richard L. Revesz & Peter H. Howard & Kenneth Arrow & Lawrence H. Goulder & Robert E. Kopp & Michael A. Livermore & Michael Oppenheimer & Thomas Sterner, 2014. "Global warming: Improve economic models of climate change," Nature, Nature, vol. 508(7495), pages 173-175, April.
    4. Maiyar, Lohithaksha M. & Thakkar, Jitesh J., 2019. "Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability," International Journal of Production Economics, Elsevier, vol. 217(C), pages 281-297.
    5. Mark M. Nejad & Lena Mashayekhy & Ratna Babu Chinnam & Daniel Grosu, 2017. "Online scheduling and pricing for electric vehicle charging," IISE Transactions, Taylor & Francis Journals, vol. 49(2), pages 178-193, February.
    6. Ioakimidis, Christos S. & Thomas, Dimitrios & Rycerski, Pawel & Genikomsakis, Konstantinos N., 2018. "Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot," Energy, Elsevier, vol. 148(C), pages 148-158.
    7. Liu, Mingxi & Shi, Yang & Fang, Fang, 2013. "Optimal power flow and PGU capacity of CCHP systems using a matrix modeling approach," Applied Energy, Elsevier, vol. 102(C), pages 794-802.
    8. Fuad Un-Noor & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Mohammad Nurunnabi Mollah & Eklas Hossain, 2017. "A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development," Energies, MDPI, vol. 10(8), pages 1-84, August.
    9. Sharafi, Masoud & ElMekkawy, Tarek Y. & Bibeau, Eric L., 2015. "Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio," Renewable Energy, Elsevier, vol. 83(C), pages 1026-1042.
    10. Ge, Yuntian & Li, Lin, 2018. "System-level energy consumption modeling and optimization for cellulosic biofuel production," Applied Energy, Elsevier, vol. 226(C), pages 935-946.
    11. Wang, Shengwei & Tang, Rui, 2017. "Supply-based feedback control strategy of air-conditioning systems for direct load control of buildings responding to urgent requests of smart grids," Applied Energy, Elsevier, vol. 201(C), pages 419-432.
    12. Iacobucci, Riccardo & McLellan, Benjamin & Tezuka, Tetsuo, 2018. "Modeling shared autonomous electric vehicles: Potential for transport and power grid integration," Energy, Elsevier, vol. 158(C), pages 148-163.
    13. Heymans, Catherine & Walker, Sean B. & Young, Steven B. & Fowler, Michael, 2014. "Economic analysis of second use electric vehicle batteries for residential energy storage and load-levelling," Energy Policy, Elsevier, vol. 71(C), pages 22-30.
    14. Yang, Jun & He, Lifu & Fu, Siyao, 2014. "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, Elsevier, vol. 128(C), pages 82-92.
    15. Dababneh, Fadwa & Li, Lin & Sun, Zeyi, 2016. "Peak power demand reduction for combined manufacturing and HVAC system considering heat transfer characteristics," International Journal of Production Economics, Elsevier, vol. 177(C), pages 44-52.
    16. Karan, Ebrahim & Mohammadpour, Atefeh & Asadi, Somayeh, 2016. "Integrating building and transportation energy use to design a comprehensive greenhouse gas mitigation strategy," Applied Energy, Elsevier, vol. 165(C), pages 234-243.
    17. Ons Sassi & Ammar Oulamara, 2017. "Electric vehicle scheduling and optimal charging problem: complexity, exact and heuristic approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 519-535, January.
    18. Davidov, Sreten & Pantoš, Miloš, 2017. "Planning of electric vehicle infrastructure based on charging reliability and quality of service," Energy, Elsevier, vol. 118(C), pages 1156-1167.
    19. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.
    20. Ren, Shuyun & Luo, Fengji & Lin, Lei & Hsu, Shu-Chien & LI, Xuran Ivan, 2019. "A novel dynamic pricing scheme for a large-scale electric vehicle sharing network considering vehicle relocation and vehicle-grid-integration," International Journal of Production Economics, Elsevier, vol. 218(C), pages 339-351.
    21. Al-Bahrani, Loau Tawfak & Chandra Patra, Jagdish, 2018. "Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid," Energy, Elsevier, vol. 147(C), pages 1070-1091.
    22. Stadler, M. & Kloess, M. & Groissböck, M. & Cardoso, G. & Sharma, R. & Bozchalui, M.C. & Marnay, C., 2013. "Electric storage in California’s commercial buildings," Applied Energy, Elsevier, vol. 104(C), pages 711-722.
    23. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    24. Gaussin, M. & Hu, G. & Abolghasem, S. & Basu, S. & Shankar, M.R. & Bidanda, B., 2013. "Assessing the environmental footprint of manufactured products: A survey of current literature," International Journal of Production Economics, Elsevier, vol. 146(2), pages 515-523.
    25. El Sehiemy, Ragab A. & Selim, F. & Bentouati, Bachir & Abido, M.A., 2020. "A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems," Energy, Elsevier, vol. 193(C).
    26. Dai, Rui & Hu, Mengqi & Yang, Dong & Chen, Yang, 2015. "A collaborative operation decision model for distributed building clusters," Energy, Elsevier, vol. 84(C), pages 759-773.
    27. Zhao, Xin & Doering, Otto C. & Tyner, Wallace E., 2015. "The economic competitiveness and emissions of battery electric vehicles in China," Applied Energy, Elsevier, vol. 156(C), pages 666-675.
    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. Md Mamun Ur Rashid & Majed A. Alotaibi & Abdul Hasib Chowdhury & Muaz Rahman & Md. Shafiul Alam & Md. Alamgir Hossain & Mohammad A. Abido, 2021. "Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.

    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. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.
    2. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M. & Lund, Peter D., 2019. "Energy integration and interaction between buildings and vehicles: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    4. Alqahtani, Mohammed & Hu, Mengqi, 2020. "Integrated energy scheduling and routing for a network of mobile prosumers," Energy, Elsevier, vol. 200(C).
    5. Xiaolin Chu & Dong Yang & Jia Li, 2019. "Sustainability Assessment of Combined Cooling, Heating, and Power Systems under Carbon Emission Regulations," Sustainability, MDPI, vol. 11(21), pages 1-17, October.
    6. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    7. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
    8. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    9. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    10. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    11. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    12. Ruben Garruto & Michela Longo & Wahiba Yaïci & Federica Foiadelli, 2020. "Connecting Parking Facilities to the Electric Grid: A Vehicle-to-Grid Feasibility Study in a Railway Station’s Car Park," Energies, MDPI, vol. 13(12), pages 1-23, June.
    13. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    14. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    15. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    16. Yeongenn Kwon & Taeyoung Kim & Keon Baek & Jinho Kim, 2020. "Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment," Energies, MDPI, vol. 13(11), pages 1-16, June.
    17. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    18. Shen, Zuo-Jun Max & Feng, Bo & Mao, Chao & Ran, Lun, 2019. "Optimization models for electric vehicle service operations: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 462-477.
    19. Freitas Gomes, Icaro Silvestre & Perez, Yannick & Suomalainen, Emilia, 2021. "Rate design with distributed energy resources and electric vehicles: A Californian case study," Energy Economics, Elsevier, vol. 102(C).
    20. Hoarau, Quentin & Perez, Yannick, 2018. "Interactions between electric mobility and photovoltaic generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 510-522.

    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:jsusta:v:12:y:2020:i:12:p:4836-:d:370972. 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.