IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v163y2021icp1385-1396.html
   My bibliography  Save this article

A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles

Author

Listed:
  • Wu, Chuanshen
  • Gao, Shan
  • Liu, Yu
  • Song, Tiancheng E.
  • Han, Haiteng

Abstract

Managing uncertainty is key to enhancing robustness in microgrids. This study focuses on the uncertainties in aggregated electric vehicles (EVs) and establishes a two-layer model predictive control (MPC) strategy for charging EVs with a microgrid. The uncertainty in sampling results of EV connection times to the microgrid and the uncertainty in sampling results of state of charge (SOC) of each EV at the time of the initial connection are both taken into account through multi-uncertainty sampling. In order of consider the impact of extreme scenarios, as feedback from arriving EVs are combined with multi-uncertainty sampling for higher accuracy of sampling results during rolling optimization. Simulation results show that, compared to conventional strategies, the proposed strategy has better performance in reducing forecasting error and regulating the charging and discharging of aggregated EVs with uncertainties.

Suggested Citation

  • Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:1385-1396
    DOI: 10.1016/j.renene.2020.08.137
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120313823
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.08.137?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. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    2. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    3. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    4. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    5. 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.
    6. Zhang, Xian & Chan, Ka Wing & Wang, Huaizhi & Zhou, Bin & Wang, Guibin & Qiu, Jing, 2020. "Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties," Applied Energy, Elsevier, vol. 262(C).
    7. Shojaabadi, Saeed & Abapour, Saeed & Abapour, Mehdi & Nahavandi, Ali, 2016. "Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties," Renewable Energy, Elsevier, vol. 99(C), pages 237-252.
    8. Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2016. "Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration," Applied Energy, Elsevier, vol. 179(C), pages 338-349.
    9. Caralis, George & Christakopoulos, Theofanis & Karellas, Sotirios & Gao, Zhiqiu, 2019. "Analysis of energy storage systems to exploit wind energy curtailment in Crete," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 122-139.
    10. Liu, Ye & Wu, Xiaogang & Du, Jiuyu & Song, Ziyou & Wu, Guoliang, 2020. "Optimal sizing of a wind-energy storage system considering battery life," Renewable Energy, Elsevier, vol. 147(P1), pages 2470-2483.
    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. Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.
    2. 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).
    3. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    4. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
    5. Bastida-Molina, Paula & Hurtado-Pérez, Elías & Moros Gómez, María Cristina & Vargas-Salgado, Carlos, 2021. "Multicriteria power generation planning and experimental verification of hybrid renewable energy systems for fast electric vehicle charging stations," Renewable Energy, Elsevier, vol. 179(C), pages 737-755.
    6. Fatin Ishraque, Md. & Shezan, Sk. A. & Ali, M.M. & Rashid, M.M., 2021. "Optimization of load dispatch strategies for an islanded microgrid connected with renewable energy sources," Applied Energy, Elsevier, vol. 292(C).
    7. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    8. Qiang, Guofeng & Tang, Shu & Hao, Jianli & Di Sarno, Luigi & Wu, Guangdong & Ren, Shaoxing, 2023. "Building automation systems for energy and comfort management in green buildings: A critical review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    9. Huo, Yuchong & Bouffard, François & Joós, Géza, 2022. "Integrating learning and explicit model predictive control for unit commitment in microgrids," Applied Energy, Elsevier, vol. 306(PA).
    10. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    11. Faris Adnan Padhilah & Kyeong-Hwa Kim, 2021. "A Centralized Power Flow Control Scheme of EV-Connected DC Microgrid to Satisfy Multi-Objective Problems under Several Constraints," Sustainability, MDPI, vol. 13(16), pages 1-37, August.
    12. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Optimal energy management of integrated energy systems for strategic participation in competitive electricity markets," Energy, Elsevier, vol. 278(PA).
    13. Miguel Matos & João Almeida & Pedro Gonçalves & Fabiano Baldo & Fernando José Braz & Paulo C. Bartolomeu, 2024. "A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities," Energies, MDPI, vol. 17(3), pages 1-25, January.

    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. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    2. Chaudhary, Priyanka & Rizwan, M., 2018. "Energy management supporting high penetration of solar photovoltaic generation for smart grid using solar forecasts and pumped hydro storage system," Renewable Energy, Elsevier, vol. 118(C), pages 928-946.
    3. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    4. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    5. Kyritsis, A. & Voglitsis, D. & Papanikolaou, N. & Tselepis, S. & Christodoulou, C. & Gonos, I. & Kalogirou, S.A., 2017. "Evolution of PV systems in Greece and review of applicable solutions for higher penetration levels," Renewable Energy, Elsevier, vol. 109(C), pages 487-499.
    6. Ho-Sung Ryu & Mun-Kyeom Kim, 2020. "Combined Economic Emission Dispatch with Environment-Based Demand Response Using WU-ABC Algorithm," Energies, MDPI, vol. 13(23), pages 1-20, December.
    7. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    8. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    9. 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.
    10. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    11. Kirchhoff, Hannes & Strunz, Kai, 2019. "Key drivers for successful development of peer-to-peer microgrids for swarm electrification," Applied Energy, Elsevier, vol. 244(C), pages 46-62.
    12. Parwal, Arvind & Fregelius, Martin & Temiz, Irinia & Göteman, Malin & Oliveira, Janaina G. de & Boström, Cecilia & Leijon, Mats, 2018. "Energy management for a grid-connected wave energy park through a hybrid energy storage system," Applied Energy, Elsevier, vol. 231(C), pages 399-411.
    13. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    14. Psarros, Georgios N. & Papathanassiou, Stavros A., 2023. "Generation scheduling in island systems with variable renewable energy sources: A literature review," Renewable Energy, Elsevier, vol. 205(C), pages 1105-1124.
    15. Ma, Li & Wang, Lingfeng & Liu, Zhaoxi, 2021. "Multi-level trading community formation and hybrid trading network construction in local energy market," Applied Energy, Elsevier, vol. 285(C).
    16. Gayo-Abeleira, Miguel & Santos, Carlos & Javier Rodríguez Sánchez, Francisco & Martín, Pedro & Antonio Jiménez, José & Santiso, Enrique, 2022. "Aperiodic two-layer energy management system for community microgrids based on blockchain strategy," Applied Energy, Elsevier, vol. 324(C).
    17. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    18. Ahl, A. & Yarime, M. & Goto, M. & Chopra, Shauhrat S. & Kumar, Nallapaneni Manoj. & Tanaka, K. & Sagawa, D., 2020. "Exploring blockchain for the energy transition: Opportunities and challenges based on a case study in Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    19. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    20. Maarja Meitern, 2022. "Does Access to Regulative Exemption Reduce Barriers for Energy Communities? A Dutch Case Study," Sustainability, MDPI, vol. 14(9), pages 1-13, May.

    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:renene:v:163:y:2021:i:c:p:1385-1396. 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.journals.elsevier.com/renewable-energy .

    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.