IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v304y2021ics0306261921011983.html
   My bibliography  Save this article

Tri-stage optimal dispatch for a microgrid in the presence of uncertainties introduced by EVs and PV

Author

Listed:
  • Jiao, Feixiang
  • Ji, Chengda
  • Zou, Yuan
  • Zhang, Xudong

Abstract

This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced.

Suggested Citation

  • Jiao, Feixiang & Ji, Chengda & Zou, Yuan & Zhang, Xudong, 2021. "Tri-stage optimal dispatch for a microgrid in the presence of uncertainties introduced by EVs and PV," Applied Energy, Elsevier, vol. 304(C).
  • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011983
    DOI: 10.1016/j.apenergy.2021.117881
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921011983
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.117881?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. Zhang, Yan & Fu, Lijun & Zhu, Wanlu & Bao, Xianqiang & Liu, Cang, 2018. "Robust model predictive control for optimal energy management of island microgrids with uncertainties," Energy, Elsevier, vol. 164(C), pages 1229-1241.
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. Du, Yan & Wang, Zhiwei & Liu, Guangyi & Chen, Xi & Yuan, Haoyu & Wei, Yanli & Li, Fangxing, 2018. "A cooperative game approach for coordinating multi-microgrid operation within distribution systems," Applied Energy, Elsevier, vol. 222(C), pages 383-395.
    4. Wenig, Jürgen & Sodenkamp, Mariya & Staake, Thorsten, 2019. "Battery versus infrastructure: Tradeoffs between battery capacity and charging infrastructure for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    5. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    6. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Feng, Nanping, 2020. "A robust optimization approach for optimal load dispatch of community energy hub," Applied Energy, Elsevier, vol. 259(C).
    7. Mads Almassalkhi & Sarnaduti Brahma & Nawaf Nazir & Hamid Ossareh & Pavan Racherla & Soumya Kundu & Sai Pushpak Nandanoori & Thiagarajan Ramachandran & Ankit Singhal & Dennice Gayme & Chengda Ji & Enr, 2020. "Hierarchical, Grid-Aware, and Economically Optimal Coordination of Distributed Energy Resources in Realistic Distribution Systems," Energies, MDPI, vol. 13(23), pages 1-40, December.
    8. Hou, Hui & Xue, Mengya & Xu, Yan & Xiao, Zhenfeng & Deng, Xiangtian & Xu, Tao & Liu, Peng & Cui, Rongjian, 2020. "Multi-objective economic dispatch of a microgrid considering electric vehicle and transferable load," Applied Energy, Elsevier, vol. 262(C).
    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. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    2. Tao Lv & Yuehong Lu & Yijie Zhou & Xuemei Liu & Changlong Wang & Yang Zhang & Zhijia Huang & Yanhong Sun, 2022. "Optimal Control of Energy Systems in Net-Zero Energy Buildings Considering Dynamic Costs: A Case Study of Zero Carbon Building in Hong Kong," Sustainability, MDPI, vol. 14(6), pages 1-25, March.

    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. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    2. Gan, Wei & Yan, Mingyu & Yao, Wei & Wen, Jinyu, 2021. "Peer to peer transactive energy for multiple energy hub with the penetration of high-level renewable energy," Applied Energy, Elsevier, vol. 295(C).
    3. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    4. Han Su & Qian Zhang & Wanying Wang & Xiaoan Tang, 2021. "A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    5. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    6. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    7. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    8. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    9. Michel Noussan & Matteo Jarre, 2021. "Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region," Energies, MDPI, vol. 14(21), pages 1-19, November.
    10. Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
    11. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    12. Navid Shirzadi & Hadise Rasoulian & Fuzhan Nasiri & Ursula Eicker, 2022. "Resilience Enhancement of an Urban Microgrid during Off-Grid Mode Operation Using Critical Load Indicators," Energies, MDPI, vol. 15(20), pages 1-15, October.
    13. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    14. Peixiao Fan & Jia Hu & Song Ke & Yuxin Wen & Shaobo Yang & Jun Yang, 2022. "A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    15. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    16. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.
    17. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    18. Zhongqi Deng & Peng Tian, 2020. "Are China's subsidies for electric vehicles effective?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 475-489, June.
    19. Han, Zhixin & Fang, Debin & Yang, Peiwen & Lei, Leyao, 2023. "Cooperative mechanisms for multi-energy complementarity in the electricity spot market," Energy Economics, Elsevier, vol. 127(PB).
    20. Secinaro, Silvana & Calandra, Davide & Lanzalonga, Federico & Ferraris, Alberto, 2022. "Electric vehicles’ consumer behaviours: Mapping the field and providing a research agenda," Journal of Business Research, Elsevier, vol. 150(C), pages 399-416.

    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:304:y:2021:i:c:s0306261921011983. 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.