IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i4p686-d207692.html
   My bibliography  Save this article

Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

Author

Listed:
  • Bruno Canizes

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • João Soares

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Zita Vale

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Juan M. Corchado

    (University of Salamanca, 37008 Salamanca, Spain
    Osaka Institute of Technology, 5 Chome-16-1 Omiya, Asahi Ward, Osaka 535-8585, Japan
    University of Technology Malaysia, Pusat Pentadbiran Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia)

Abstract

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users.

Suggested Citation

  • Bruno Canizes & João Soares & Zita Vale & Juan M. Corchado, 2019. "Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City," Energies, MDPI, vol. 12(4), pages 1-40, February.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:686-:d:207692
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/4/686/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/4/686/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Md Morshed Alam & Saad Mekhilef & Mehdi Seyedmahmoudian & Ben Horan, 2017. "Dynamic Charging of Electric Vehicle with Negligible Power Transfer Fluctuation," Energies, MDPI, vol. 10(5), pages 1-20, May.
    2. Roger E. Bohn & Michael C. Caramanis & Fred C. Schweppe, 1984. "Optimal Pricing in Electrical Networks over Space and Time," RAND Journal of Economics, The RAND Corporation, vol. 15(3), pages 360-376, Autumn.
    3. Franke, Thomas & Krems, Josef F., 2013. "Interacting with limited mobility resources: Psychological range levels in electric vehicle use," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 109-122.
    4. Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables," Energy, Elsevier, vol. 120(C), pages 642-651.
    5. Mokryani, Geev & Hu, Yim Fun & Papadopoulos, Panagiotis & Niknam, Taher & Aghaei, Jamshid, 2017. "Deterministic approach for active distribution networks planning with high penetration of wind and solar power," Renewable Energy, Elsevier, vol. 113(C), pages 942-951.
    6. Lueken, Colleen & Carvalho, Pedro M.S. & Apt, Jay, 2012. "Distribution grid reconfiguration reduces power losses and helps integrate renewables," Energy Policy, Elsevier, vol. 48(C), pages 260-273.
    7. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, December.
    8. Hung, Duong Quoc & Mithulananthan, N., 2014. "Loss reduction and loadability enhancement with DG: A dual-index analytical approach," Applied Energy, Elsevier, vol. 115(C), pages 233-241.
    9. Santos, Sérgio F. & Fitiwi, Desta Z. & Cruz, Marco R.M. & Cabrita, Carlos M.P. & Catalão, João P.S., 2017. "Impacts of optimal energy storage deployment and network reconfiguration on renewable integration level in distribution systems," Applied Energy, Elsevier, vol. 185(P1), pages 44-55.
    10. Blesl, Markus & Das, Anjana & Fahl, Ulrich & Remme, Uwe, 2007. "Role of energy efficiency standards in reducing CO2 emissions in Germany: An assessment with TIMES," Energy Policy, Elsevier, vol. 35(2), pages 772-785, February.
    11. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    12. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    13. Hung, Duong Quoc & Mithulananthan, N. & Bansal, R.C., 2014. "An optimal investment planning framework for multiple distributed generation units in industrial distribution systems," Applied Energy, Elsevier, vol. 124(C), pages 62-72.
    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. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    2. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.
    3. Bruno Canizes & João Costa & Diego Bairrão & Zita Vale, 2023. "Local Renewable Energy Communities: Classification and Sizing," Energies, MDPI, vol. 16(5), pages 1-26, March.
    4. Christian Thiel & Andreea Julea & Beatriz Acosta Iborra & Nerea De Miguel Echevarria & Emanuela Peduzzi & Enrico Pisoni & Jonatan J. Gómez Vilchez & Jette Krause, 2019. "Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union," Energies, MDPI, vol. 12(12), pages 1-23, June.

    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. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeshwar Mahanta, 2018. "Impact of Electric Vehicle Charging Station Load on Distribution Network," Energies, MDPI, vol. 11(1), pages 1-25, January.
    2. Santos, Sérgio F. & Fitiwi, Desta Z. & Cruz, Marco R.M. & Cabrita, Carlos M.P. & Catalão, João P.S., 2017. "Impacts of optimal energy storage deployment and network reconfiguration on renewable integration level in distribution systems," Applied Energy, Elsevier, vol. 185(P1), pages 44-55.
    3. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Akif Zia Khan & Dong Ryeol Shin, 2017. "Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective," Energies, MDPI, vol. 10(4), pages 1-47, April.
    4. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    5. Emilio Ghiani & Andrea Giordano & Andrea Nieddu & Luca Rosetti & Fabrizio Pilo, 2019. "Planning of a Smart Local Energy Community: The Case of Berchidda Municipality (Italy)," Energies, MDPI, vol. 12(24), pages 1-14, December.
    6. Muhammad Mahad Malik & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Bader Alharbi & Hamoud Alafnan & Halemah Alshehry, 2023. "Climate Change Impacts Quantification on the Domestic Side of Electrical Grid and Respective Mitigation Strategy across Medium Horizon 2030," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    7. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    8. Bucciarelli, Martina & Paoletti, Simone & Vicino, Antonio, 2018. "Optimal sizing of energy storage systems under uncertain demand and generation," Applied Energy, Elsevier, vol. 225(C), pages 611-621.
    9. Syed Ali Abbas Kazmi & Dong Ryeol Shin, 2017. "DG Placement in Loop Distribution Network with New Voltage Stability Index and Loss Minimization Condition Based Planning Approach under Load Growth," Energies, MDPI, vol. 10(8), pages 1-28, August.
    10. Fu, Xueqian & Chen, Haoyong & Cai, Runqing & Yang, Ping, 2015. "Optimal allocation and adaptive VAR control of PV-DG in distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 173-182.
    11. Syed Ali Abbas Kazmi & Abdul Kashif Janjua & Dong Ryeol Shin, 2018. "Enhanced Voltage Stability Assessment Index Based Planning Approach for Mesh Distribution Systems," Energies, MDPI, vol. 11(5), pages 1-36, May.
    12. Yaghoobi, Jalil & Islam, Monirul & Mithulananthan, Nadarajah, 2018. "Analytical approach to assess the loadability of unbalanced distribution grid with rooftop PV units," Applied Energy, Elsevier, vol. 211(C), pages 358-367.
    13. Sara Deilami & S. M. Muyeen, 2020. "An Insight into Practical Solutions for Electric Vehicle Charging in Smart Grid," Energies, MDPI, vol. 13(7), pages 1-13, March.
    14. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Dong Ryeol Shin, 2017. "Multi-Objective Planning Techniques in Distribution Networks: A Composite Review," Energies, MDPI, vol. 10(2), pages 1-44, February.
    15. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    16. Moore, J. & Woo, C.K. & Horii, B. & Price, S. & Olson, A., 2010. "Estimating the option value of a non-firm electricity tariff," Energy, Elsevier, vol. 35(4), pages 1609-1614.
    17. Vadim Borokhov, 2014. "On the properties of nodal price response matrix in electricity markets," Papers 1404.3678, arXiv.org, revised Jan 2015.
    18. Karsten Neuhoff, 2002. "Optimal congestion treatment for bilateral electricity trading," Working Papers EP05, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    19. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    20. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(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:gam:jeners:v:12:y:2019:i:4:p:686-:d:207692. 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.