IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v180y2016icp155-168.html
   My bibliography  Save this article

Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks

Author

Listed:
  • García-Villalobos, J.
  • Zamora, I.
  • Knezović, K.
  • Marinelli, M.

Abstract

The massive introduction of plug-in electric vehicles (PEVs) into low voltage (LV) distribution networks will lead to several problems, such as: increase of energy losses, decrease of distribution transformer lifetime, lines and transformer overload issues, voltage drops and unbalances. In this context, this paper proposes a new multi-objective optimization algorithm in order to reduce the mentioned problems. At the same time, users’ interests in terms of charging cost and privacy have been taken into account. The proposed multi-objective optimization is based on minimizing the load variance and charging costs by using the weighted sum method and fuzzy control. The use of vehicle to grid (V2G) concept and load forecast uncertainties have been also considered. Furthermore, an innovative method for mitigating voltage unbalances has been developed. The effectiveness of this methodology has been tested using real data of a LV distribution network, located in Borup (Denmark). Simulation results show that this approach can reduce both energy losses and charging costs as well as it allows a high PEV penetration rates (PEV-PR).

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:180:y:2016:i:c:p:155-168
    DOI: 10.1016/j.apenergy.2016.07.110
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916310406
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2016.07.110?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. 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.
    2. Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
    3. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2015. "Value of flexible electric vehicles in providing spinning reserve services," Applied Energy, Elsevier, vol. 157(C), pages 60-74.
    4. Bishop, Justin D.K. & Axon, Colin J. & Bonilla, David & Tran, Martino & Banister, David & McCulloch, Malcolm D., 2013. "Evaluating the impact of V2G services on the degradation of batteries in PHEV and EV," Applied Energy, Elsevier, vol. 111(C), pages 206-218.
    5. 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.
    6. Yan Zhou & Michael Wang & Han Hao & Larry Johnson & Hewu Wang & Han Hao, 2015. "Plug-in electric vehicle market penetration and incentives: a global review," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(5), pages 777-795, June.
    7. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
    8. 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.
    9. 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.
    10. Neaimeh, Myriam & Wardle, Robin & Jenkins, Andrew M. & Yi, Jialiang & Hill, Graeme & Lyons, Padraig F. & Hübner, Yvonne & Blythe, Phil T. & Taylor, Phil C., 2015. "A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts," Applied Energy, Elsevier, vol. 157(C), pages 688-698.
    11. Foley, Aoife & Tyther, Barry & Calnan, Patrick & Ó Gallachóir, Brian, 2013. "Impacts of Electric Vehicle charging under electricity market operations," Applied Energy, Elsevier, vol. 101(C), pages 93-102.
    12. García-Villalobos, J. & Zamora, I. & San Martín, J.I. & Asensio, F.J. & Aperribay, V., 2014. "Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 717-731.
    13. Munkhammar, Joakim & Rydén, Jesper & Widén, Joakim, 2014. "Characterizing probability density distributions for household electricity load profiles from high-resolution electricity use data," Applied Energy, Elsevier, vol. 135(C), pages 382-390.
    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. Xu, Jian & Wang, Jing & Liao, Siyang & Sun, Yuanzhang & Ke, Deping & Li, Xiong & Liu, Ji & Jiang, Yibo & Wei, Congying & Tang, Bowen, 2018. "Stochastic multi-objective optimization of photovoltaics integrated three-phase distribution network based on dynamic scenarios," Applied Energy, Elsevier, vol. 231(C), pages 985-996.
    2. Gonzalo Bustos-Turu & Koen H. van Dam & Salvador Acha & Nilay Shah, 2023. "An Agent-Based Decision Support Framework for a Prospective Analysis of Transport and Heat Electrification in Urban Areas," Energies, MDPI, vol. 16(17), pages 1-26, August.
    3. Yin, Linfei & Wang, Tao & Zheng, Baomin, 2021. "Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems," Energy, Elsevier, vol. 216(C).
    4. Zheng, Yanchong & Niu, Songyan & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2019. "Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 424-439.
    5. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    6. 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).
    7. Helmus, J.R. & Spoelstra, J.C. & Refa, N. & Lees, M. & van den Hoed, R., 2018. "Assessment of public charging infrastructure push and pull rollout strategies: The case of the Netherlands," Energy Policy, Elsevier, vol. 121(C), pages 35-47.
    8. Shang, Yitong & Liu, Man & Shao, Ziyun & Jian, Linni, 2020. "Internet of smart charging points with photovoltaic Integration: A high-efficiency scheme enabling optimal dispatching between electric vehicles and power grids," Applied Energy, Elsevier, vol. 278(C).
    9. Konstantinos Kotsalos & Ismael Miranda & Jose Luis Dominguez-Garcia & Helder Leite & Nuno Silva & Nikos Hatziargyriou, 2020. "Exploiting OLTC and BESS Operation Coordinated with Active Network Management in LV Networks," Sustainability, MDPI, vol. 12(8), pages 1-25, April.
    10. Bernd Thormann & Thomas Kienberger, 2022. "Estimation of Grid Reinforcement Costs Triggered by Future Grid Customers: Influence of the Quantification Method (Scaling vs. Large-Scale Simulation) and Coincidence Factors (Single vs. Multiple Appl," Energies, MDPI, vol. 15(4), pages 1-26, February.
    11. Morsy Nour & José Pablo Chaves-Ávila & Gaber Magdy & Álvaro Sánchez-Miralles, 2020. "Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems," Energies, MDPI, vol. 13(18), pages 1-34, September.
    12. 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.
    13. Mirakhorli, Amin & Dong, Bing, 2018. "Model predictive control for building loads connected with a residential distribution grid," Applied Energy, Elsevier, vol. 230(C), pages 627-642.
    14. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
    15. Breen, M. & Murphy, M.D. & Upton, J., 2019. "Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms," Applied Energy, Elsevier, vol. 242(C), pages 1697-1711.
    16. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    17. Carpinelli, G. & Mottola, F. & Proto, D. & Varilone, P., 2017. "Minimizing unbalances in low-voltage microgrids: Optimal scheduling of distributed resources," Applied Energy, Elsevier, vol. 191(C), pages 170-182.
    18. Shahid Hussain & Mohamed A. Ahmed & Ki-Beom Lee & Young-Chon Kim, 2020. "Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot," Energies, MDPI, vol. 13(12), pages 1-27, June.
    19. Wu, Di & Radhakrishnan, Nikitha & Huang, Sen, 2019. "A hierarchical charging control of plug-in electric vehicles with simple flexibility model," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    20. 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.
    21. Das, Ridoy & Wang, Yue & Putrus, Ghanim & Kotter, Richard & Marzband, Mousa & Herteleer, Bert & Warmerdam, Jos, 2020. "Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services," Applied Energy, Elsevier, vol. 257(C).
    22. Lin, Haiyang & Liu, Yiling & Sun, Qie & Xiong, Rui & Li, Hailong & Wennersten, Ronald, 2018. "The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation," Applied Energy, Elsevier, vol. 230(C), pages 189-206.
    23. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    24. 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.

    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. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    2. Gaizka Saldaña & Jose Ignacio San Martin & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation," Energies, MDPI, vol. 12(12), pages 1-37, June.
    3. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2016. "Low carbon technologies as providers of operational flexibility in future power systems," Applied Energy, Elsevier, vol. 168(C), pages 724-738.
    4. 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.
    5. Jian, Linni & Zheng, Yanchong & Shao, Ziyun, 2017. "High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles," Applied Energy, Elsevier, vol. 186(P1), pages 46-55.
    6. Sarabi, Siyamak & Davigny, Arnaud & Courtecuisse, Vincent & Riffonneau, Yann & Robyns, Benoît, 2016. "Potential of vehicle-to-grid ancillary services considering the uncertainties in plug-in electric vehicle availability and service/localization limitations in distribution grids," Applied Energy, Elsevier, vol. 171(C), pages 523-540.
    7. Cesar Diaz-Londono & Luigi Colangelo & Fredy Ruiz & Diego Patino & Carlo Novara & Gianfranco Chicco, 2019. "Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station," Energies, MDPI, vol. 12(20), pages 1-29, October.
    8. Raveendran, Visal & Alvarez-Bel, Carlos & Nair, Manjula G., 2020. "Assessing the ancillary service potential of electric vehicles to support renewable energy integration in touristic islands: A case study from Balearic island of Menorca," Renewable Energy, Elsevier, vol. 161(C), pages 495-509.
    9. Gonzalez Venegas, Felipe & Petit, Marc & Perez, Yannick, 2021. "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    10. García-Villalobos, J. & Zamora, I. & San Martín, J.I. & Asensio, F.J. & Aperribay, V., 2014. "Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 717-731.
    11. 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.
    12. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    13. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    14. Ji, Zhenya & Huang, Xueliang, 2018. "Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 710-727.
    15. Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, October.
    16. 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.
    17. Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
    18. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    19. Will, Christian & Lehmann, Nico & Baumgartner, Nora & Feurer, Sven & Jochem, Patrick & Fichtner, Wolf, 2022. "Consumer understanding and evaluation of carbon-neutral electric vehicle charging services," Applied Energy, Elsevier, vol. 313(C).
    20. Zhao, Yang & Noori, Mehdi & Tatari, Omer, 2016. "Vehicle to Grid regulation services of electric delivery trucks: Economic and environmental benefit analysis," Applied Energy, Elsevier, vol. 170(C), pages 161-175.

    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:180:y:2016:i:c:p:155-168. 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.