IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v226y2018icp582-594.html
   My bibliography  Save this article

Real-time renewable energy incentive system for electric vehicles using prioritization and cryptocurrency

Author

Listed:
  • Zhang, Tianyang
  • Pota, Himanshu
  • Chu, Chi-Cheng
  • Gadh, Rajit

Abstract

Significant increase in the installation and penetration of Renewable Energy Resources (RES) has raised intermittency and variability issues in the electric power grid. Solutions based on fast-response energy storage can be costly, especially when dealing with higher renewable energy penetration rate. The rising popularity of electric vehicles (EVs) also brings challenges to the system planning, dispatching and operation. Due to the random dynamic nature of electric vehicle charging and routing, electric vehicle load can be challenging to the power distribution operators and utilities. The bright side is that the load of EV charging is shiftable and can be leveraged to reduce the variability of renewable energy by consuming it locally. In this paper, we proposed a real-time system that incorporates the concepts of prioritization and cryptocurrency, named SMERCOIN, to incentivize electric vehicle users to collectively charge with a renewable energy-friendly schedule. The system implements a ranking scheme by giving charging priority to users with a better renewable energy usage history. By incorporating a blockchain-based cryptocurrency component, the system can incentivize user with monetary and non-monetary means in a flat-rate system. The effectiveness of the system mechanism has been verified by both numerical simulations and experiments. The system experiment has been implemented on campus of the University of California, Los Angeles (UCLA) for 15 months and the results show that the usage of solar energy has increased significantly.

Suggested Citation

  • Zhang, Tianyang & Pota, Himanshu & Chu, Chi-Cheng & Gadh, Rajit, 2018. "Real-time renewable energy incentive system for electric vehicles using prioritization and cryptocurrency," Applied Energy, Elsevier, vol. 226(C), pages 582-594.
    • Handle: RePEc:eee:appene:v:226:y:2018:i:c:p:582-594
    DOI: 10.1016/j.apenergy.2018.06.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918308912
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.06.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. McPhail, Donald, 2014. "Evaluation of ground energy storage assisted electric vehicle DC fast charger for demand charge reduction and providing demand response," Renewable Energy, Elsevier, vol. 67(C), pages 103-108.
    2. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    3. Mengelkamp, Esther & Gärttner, Johannes & Rock, Kerstin & Kessler, Scott & Orsini, Lawrence & Weinhardt, Christof, 2018. "Designing microgrid energy markets," Applied Energy, Elsevier, vol. 210(C), pages 870-880.
    4. Sikorski, Janusz J. & Haughton, Joy & Kraft, Markus, 2017. "Blockchain technology in the chemical industry: Machine-to-machine electricity market," Applied Energy, Elsevier, vol. 195(C), pages 234-246.
    5. Schuller, Alexander & Flath, Christoph M. & Gottwalt, Sebastian, 2015. "Quantifying load flexibility of electric vehicles for renewable energy integration," Applied Energy, Elsevier, vol. 151(C), pages 335-344.
    6. Haghi, Ehsan & Raahemifar, Kaamran & Fowler, Michael, 2018. "Investigating the effect of renewable energy incentives and hydrogen storage on advantages of stakeholders in a microgrid," Energy Policy, Elsevier, vol. 113(C), pages 206-222.
    7. Zhang, Jian & Cho, Heejin & Luck, Rogelio & Mago, Pedro J., 2018. "Integrated photovoltaic and battery energy storage (PV-BES) systems: An analysis of existing financial incentive policies in the US," Applied Energy, Elsevier, vol. 212(C), pages 895-908.
    8. Quashie, Mike & Marnay, Chris & Bouffard, François & Joós, Géza, 2018. "Optimal planning of microgrid power and operating reserve capacity," Applied Energy, Elsevier, vol. 210(C), pages 1229-1236.
    9. Nienhueser, Ian Andrew & Qiu, Yueming, 2016. "Economic and environmental impacts of providing renewable energy for electric vehicle charging – A choice experiment study," Applied Energy, Elsevier, vol. 180(C), pages 256-268.
    10. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    11. Zhao, Zhen-Yu & Chen, Yu-Long & Chang, Rui-Dong, 2016. "How to stimulate renewable energy power generation effectively? – China's incentive approaches and lessons," Renewable Energy, Elsevier, vol. 92(C), pages 147-156.
    12. Sadeghi-Barzani, Payam & Rajabi-Ghahnavieh, Abbas & Kazemi-Karegar, Hosein, 2014. "Optimal fast charging station placing and sizing," Applied Energy, Elsevier, vol. 125(C), pages 289-299.
    13. DeForest, Nicholas & MacDonald, Jason S. & Black, Douglas R., 2018. "Day ahead optimization of an electric vehicle fleet providing ancillary services in the Los Angeles Air Force Base vehicle-to-grid demonstration," Applied Energy, Elsevier, vol. 210(C), pages 987-1001.
    14. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    15. Schill, Wolf-Peter & Gerbaulet, Clemens, 2015. "Power System Impacts of Electric Vehicles in Germany: Charging with Coal or Renewables," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 156, pages 185-196.
    16. Rubino, Luigi & Capasso, Clemente & Veneri, Ottorino, 2017. "Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility," Applied Energy, Elsevier, vol. 207(C), pages 438-464.
    17. Zerrahn, Alexander, 2017. "Wind Power and Externalities," Ecological Economics, Elsevier, vol. 141(C), pages 245-260.
    18. Zhang, Li & Shaffer, Brendan & Brown, Tim & Scott Samuelsen, G., 2015. "The optimization of DC fast charging deployment in California," Applied Energy, Elsevier, vol. 157(C), pages 111-122.
    19. Arcos-Aviles, Diego & Pascual, Julio & Guinjoan, Francesc & Marroyo, Luis & Sanchis, Pablo & Marietta, Martin P., 2017. "Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting," Applied Energy, Elsevier, vol. 205(C), pages 69-84.
    20. Chandra Mouli, G.R. & Bauer, P. & Zeman, M., 2016. "System design for a solar powered electric vehicle charging station for workplaces," Applied Energy, Elsevier, vol. 168(C), pages 434-443.
    21. Vallés, Mercedes & Bello, Antonio & Reneses, Javier & Frías, Pablo, 2018. "Probabilistic characterization of electricity consumer responsiveness to economic incentives," Applied Energy, Elsevier, vol. 216(C), pages 296-310.
    22. Iversen, Emil B. & Morales, Juan M. & Madsen, Henrik, 2014. "Optimal charging of an electric vehicle using a Markov decision process," Applied Energy, Elsevier, vol. 123(C), pages 1-12.
    23. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    24. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    25. López-González, A. & Domenech, B. & Gómez-Hernández, D. & Ferrer-Martí, L., 2017. "Renewable microgrid projects for autonomous small-scale electrification in Andean countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1255-1265.
    26. Javidsharifi, Mahshid & Niknam, Taher & Aghaei, Jamshid & Mokryani, Geev, 2018. "Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices," Applied Energy, Elsevier, vol. 216(C), pages 367-381.
    Full references (including those not matched with items on IDEAS)

    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. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    2. Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.
    3. Muratori, Matteo & Elgqvist, Emma & Cutler, Dylan & Eichman, Joshua & Salisbury, Shawn & Fuller, Zachary & Smart, John, 2019. "Technology solutions to mitigate electricity cost for electric vehicle DC fast charging," Applied Energy, Elsevier, vol. 242(C), pages 415-423.
    4. Dutton, Spencer & Marnay, Chris & Feng, Wei & Robinson, Matthew & Mammoli, Andrea, 2019. "Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service," Applied Energy, Elsevier, vol. 238(C), pages 1126-1137.
    5. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
    6. Lauvergne, Rémi & Perez, Yannick & Françon, Mathilde & Tejeda De La Cruz, Alberto, 2022. "Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040," Applied Energy, Elsevier, vol. 326(C).
    7. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2017. "Electricity costs for a Level 3 electric vehicle fueling station integrated with a building," Applied Energy, Elsevier, vol. 191(C), pages 367-384.
    8. Nienhueser, Ian Andrew & Qiu, Yueming, 2016. "Economic and environmental impacts of providing renewable energy for electric vehicle charging – A choice experiment study," Applied Energy, Elsevier, vol. 180(C), pages 256-268.
    9. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2019. "Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics," Applied Energy, Elsevier, vol. 242(C), pages 769-781.
    10. Staudt, Philipp & Schmidt, Marc & Gärttner, Johannes & Weinhardt, Christof, 2018. "A decentralized approach towards resolving transmission grid congestion in Germany using vehicle-to-grid technology," Applied Energy, Elsevier, vol. 230(C), pages 1435-1446.
    11. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
    12. Muratori, Matteo & Kontou, Eleftheria & Eichman, Joshua, 2019. "Electricity rates for electric vehicle direct current fast charging in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    13. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    14. Maria Taljegard & Lisa Göransson & Mikael Odenberger & Filip Johnsson, 2021. "To Represent Electric Vehicles in Electricity Systems Modelling—Aggregated Vehicle Representation vs. Individual Driving Profiles," Energies, MDPI, vol. 14(3), pages 1-25, January.
    15. 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.
    16. Neaimeh, Myriam & Salisbury, Shawn D. & Hill, Graeme A. & Blythe, Philip T. & Scoffield, Don R. & Francfort, James E., 2017. "Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles," Energy Policy, Elsevier, vol. 108(C), pages 474-486.
    17. Yin, Rumeng & He, Jiang, 2023. "Design of a photovoltaic electric bike battery-sharing system in public transit stations," Applied Energy, Elsevier, vol. 332(C).
    18. Julia Vopava & Christian Koczwara & Anna Traupmann & Thomas Kienberger, 2019. "Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach," Energies, MDPI, vol. 13(1), pages 1-23, December.
    19. Se Hoon Baik & Young Gyu Jin & Yong Tae Yoon, 2018. "Determining Equipment Capacity of Electric Vehicle Charging Station Operator for Profit Maximization," Energies, MDPI, vol. 11(9), pages 1-15, September.
    20. Arlt, Marie-Louise & Astier, Nicolas, 2023. "Do retail businesses have efficient incentives to invest in public charging stations for electric vehicles?," Energy Economics, Elsevier, vol. 124(C).

    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:eee:appene:v:226:y:2018:i:c:p:582-594. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.