IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i1p195-d195878.html
   My bibliography  Save this article

A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method

Author

Listed:
  • Xin Li

    (School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Xiaodi Zhang

    (State Grid Beijing Electric Power Company, Beijing 100031, China
    School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Yuling Fan

    (College of Informatics, Huazhong Agricultural University, Wuhan 430070, China)

Abstract

To reduce the fluctuation of renewable energy (RE) supply and improve the economic efficiency of the power grid, the energy local area network (ELAN), which is a subnetwork of the energy internet (EI), plays an important role in specific regions. Electric vehicles (EVs), as virtual energy storage (VES) in ELANs, are helpful to decrease the fluctuations of RE supply. However, how to use EVs in ELANs is a complex issue, considering the uncertainties of EVs’ charging demand, the forecast data errors of RE sources, etc. In this paper, a typical ELAN structure is established, taking into account RE sources, load response system, and a distributed energy storage (DES) system including EVs. A two-step optimization framework for ELAN scheduling problem is proposed. A global optimization model based on forecast data is built to maximize the income of ELAN, and an online local optimization model is introduced to minimize the correction cost utilizing prior knowledge. Finally, the proposed two-step optimization framework is applied to a series of real-world ELAN scheduling problems. The results show that DES system with EVs can reduce the volatility of RE supply evidently, and the proposed method is able to maximize the income of the ELAN efficiently.

Suggested Citation

  • Xin Li & Xiaodi Zhang & Yuling Fan, 2019. "A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method," Energies, MDPI, vol. 12(1), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:195-:d:195878
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/1/195/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/1/195/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Evgeny Nefedov & Seppo Sierla & Valeriy Vyatkin, 2018. "Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings," Energies, MDPI, vol. 11(8), pages 1-18, August.
    2. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    3. Weis, Allison & Jaramillo, Paulina & Michalek, Jeremy, 2014. "Estimating the potential of controlled plug-in hybrid electric vehicle charging to reduce operational and capacity expansion costs for electric power systems with high wind penetration," Applied Energy, Elsevier, vol. 115(C), pages 190-204.
    4. Destek, Mehmet Akif & Aslan, Alper, 2017. "Renewable and non-renewable energy consumption and economic growth in emerging economies: Evidence from bootstrap panel causality," Renewable Energy, Elsevier, vol. 111(C), pages 757-763.
    5. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    6. Abedini, Mohammad & Moradi, Mohammad H. & Hosseinian, S. Mahdi, 2016. "Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm," Renewable Energy, Elsevier, vol. 90(C), pages 430-439.
    7. Riccardo Iacobucci & Benjamin McLellan & Tetsuo Tezuka, 2018. "The Synergies of Shared Autonomous Electric Vehicles with Renewable Energy in a Virtual Power Plant and Microgrid," Energies, MDPI, vol. 11(8), pages 1-20, August.
    8. Kavousi-Fard, Abdollah & Abunasri, Alireza & Zare, Alireza & Hoseinzadeh, Rasool, 2014. "Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids," Energy, Elsevier, vol. 78(C), pages 904-915.
    9. Tang, Ruoli & Li, Xin & Lai, Jingang, 2018. "A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization," Applied Energy, Elsevier, vol. 228(C), pages 254-264.
    10. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    11. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Cohen, Miri Weiss & Reis, Agnaldo J.R. & Silva, Sidelmo M. & Souza, Marcone J.F. & Fleming, Peter J. & Guimarães, Frederico G., 2016. "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, Elsevier, vol. 89(C), pages 730-742.
    12. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
    13. Hao Bai & Shihong Miao & Pipei Zhang & Zhan Bai, 2015. "Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System," Energies, MDPI, vol. 8(2), pages 1-26, February.
    14. Tang, Ruoli & Wu, Zhou & Li, Xin, 2018. "Optimal operation of photovoltaic/battery/diesel/cold-ironing hybrid energy system for maritime application," Energy, Elsevier, vol. 162(C), pages 697-714.
    15. Peng, Chao & Zou, Jianxiao & Lian, Lian & Li, Liying, 2017. "An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits," Applied Energy, Elsevier, vol. 190(C), pages 591-599.
    16. Haitao Liu & Yu Ji & Huaidong Zhuang & Hongbin Wu, 2015. "Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid," Energies, MDPI, vol. 8(5), pages 1-20, May.
    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. 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).

    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. Bingke Yan & Bo Wang & Lin Zhu & Hesen Liu & Yilu Liu & Xingpei Ji & Dichen Liu, 2015. "A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet," Energies, MDPI, vol. 8(11), pages 1-24, November.
    2. Xing, Hui & Spence, Stephen & Chen, Hua, 2020. "A comprehensive review on countermeasures for CO2 emissions from ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    3. He Yin & Hai Lan & Ying-Yi Hong & Zhuangwei Wang & Peng Cheng & Dan Li & Dong Guo, 2023. "A Comprehensive Review of Shipboard Power Systems with New Energy Sources," Energies, MDPI, vol. 16(5), pages 1-44, February.
    4. Tang, Ruoli & An, Qing & Xu, Fan & Zhang, Xiaodi & Li, Xin & Lai, Jingang & Dong, Zhengcheng, 2020. "Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method," Energy, Elsevier, vol. 211(C).
    5. Tang, Ruoli & Zhang, Shihan & Zhang, Shangyu & Lai, Jingang & Zhang, Yan, 2023. "Semi-online parameter identification methodology for maritime power lithium batteries," Applied Energy, Elsevier, vol. 339(C).
    6. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2018. "Microgrids energy management systems: A critical review on methods, solutions, and prospects," Applied Energy, Elsevier, vol. 222(C), pages 1033-1055.
    7. Tang, Ruoli & Lin, Qiao & Zhou, Jinxiang & Zhang, Shangyu & Lai, Jingang & Li, Xin & Dong, Zhengcheng, 2020. "Suppression strategy of short-term and long-term environmental disturbances for maritime photovoltaic system," Applied Energy, Elsevier, vol. 259(C).
    8. Yin, He & Wu, Jinghu & Zhang, Gang & Lan, Hai & Hong, Ying-Yi & Li, Dan, 2024. "Variable time-scale power scheduling of a River-Sea going renewable energy ship considering coupling variations in all-electric propulsion," Applied Energy, Elsevier, vol. 374(C).
    9. 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.
    10. Jin, Xiaolong & Wu, Jianzhong & Mu, Yunfei & Wang, Mingshen & Xu, Xiandong & Jia, Hongjie, 2017. "Hierarchical microgrid energy management in an office building," Applied Energy, Elsevier, vol. 208(C), pages 480-494.
    11. Helindu Cumaratunga & Masaki Imanaka & Muneaki Kurimoto & Shigeyuki Sugimoto & Takeyoshi Kato, 2021. "Proposal of Priority Schemes for Controlling Electric Vehicle Charging and Discharging in a Workplace Power System with High Penetration of Photovoltaic Systems," Energies, MDPI, vol. 14(22), pages 1-23, November.
    12. Tang, Ruoli & Zhang, Shangyu & Zhang, Shihan & Zhang, Yan & Lai, Jingang, 2023. "Parameter identification for lithium batteries: Model variable-coupling analysis and a novel cooperatively coevolving identification algorithm," Energy, Elsevier, vol. 263(PB).
    13. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    14. Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Chul-Hwan Kim, 2018. "A Bi-Level EV Aggregator Coordination Scheme for Load Variance Minimization with Renewable Energy Penetration Adaptability," Energies, MDPI, vol. 11(10), pages 1-28, October.
    15. Guo, Tianyu & Li, Peng & Wang, Zixuan & Shi, Ruyu & Han, Zhonghe & Xia, Hui & Li, Jianyi, 2021. "Integrated modelling and optimal operation analysis of multienergy systems based on Stackelberg game theory," Energy, Elsevier, vol. 236(C).
    16. 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.
    17. Villanthenkodath, Muhammed Ashiq & Mahalik, Mantu Kumar, 2021. "Does economic growth respond to electricity consumption asymmetrically in Bangladesh? The implication for environmental sustainability," Energy, Elsevier, vol. 233(C).
    18. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    19. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    20. Rodríguez-Gallegos, Carlos D. & Vinayagam, Lokesh & Gandhi, Oktoviano & Yagli, Gokhan Mert & Alvarez-Alvarado, Manuel S. & Srinivasan, Dipti & Reindl, Thomas & Panda, S.K., 2021. "Novel forecast-based dispatch strategy optimization for PV hybrid systems in real time," Energy, Elsevier, vol. 222(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:1:p:195-:d:195878. 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.