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Coordinated control for EV aggregators and power plants in frequency regulation considering time-varying delays

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Listed:
  • Jia, Hongjie
  • Li, Xiaomeng
  • Mu, Yunfei
  • Xu, Chen
  • Jiang, Yilang
  • Yu, Xiaodan
  • Wu, Jianzhong
  • Dong, Chaoyu

Abstract

Nowadays, large scale intermittent renewable energy is being integrated to power systems as a solution for the low-carbon development worldwide. With the increasing penetration of renewable power generation, system frequency stability is becoming more and more serious. To increase the utilization of renewable energy, electric vehicles (EVs) are suggested to participate in load frequency control (LFC) through aggregators due to their vehicle-to-grid (V2G) capability and quick response characteristic, which is denoted as EV-LFC controller in this paper. In order to fully take the advantages of EVs in the LFC, this paper presents a coordinated control strategy between EV-LFC controller and traditional power plants based LFC (PP-LFC) controller for frequency regulation. In this strategy, the EV-LFC has a priority in response than the PP-LFC when the system deviation violates its acceptable range. However, the LFC integrating EVs is with inevitable time delays due to the data and control signal transmission. Meanwhile, the system inertia uncertainty caused by renewable energy in power system may also cause instability problem. For this reason, an improved robust stability criterion is proposed to estimate the asymptotically stable for LFC system considering the inertia uncertainty and time-varying delays simultaneously. Additionally, a PI controller for EV-LFC controller is used to enhance the system frequency stability. Finally, the effect of increasing EVs number on the frequency stability is investigated, which may guide system operator to utilize EVs to the LFC properly. Case studies are carried out based on a simplified Great Britain (GB) power system. Simulation results show that the proposed coordination strategy can not only provide effective frequency regulation, but also reduce the output of traditional power plants, in which the inertia uncertainty and time delays are properly considered.

Suggested Citation

  • Jia, Hongjie & Li, Xiaomeng & Mu, Yunfei & Xu, Chen & Jiang, Yilang & Yu, Xiaodan & Wu, Jianzhong & Dong, Chaoyu, 2018. "Coordinated control for EV aggregators and power plants in frequency regulation considering time-varying delays," Applied Energy, Elsevier, vol. 210(C), pages 1363-1376.
    • Handle: RePEc:eee:appene:v:210:y:2018:i:c:p:1363-1376
    DOI: 10.1016/j.apenergy.2017.05.174
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    6. Badesa, L. & Teng, F. & Strbac, G., 2020. "Pricing inertia and Frequency Response with diverse dynamics in a Mixed-Integer Second-Order Cone Programming formulation," Applied Energy, Elsevier, vol. 260(C).
    7. Rao, Yingqing & Yang, Jun & Xiao, Jinxing & Xu, Bingyan & Liu, Wenjing & Li, Yonghui, 2021. "A frequency control strategy for multimicrogrids with V2G based on the improved robust model predictive control," Energy, Elsevier, vol. 222(C).
    8. Hamad, Basil R. & Al-Durra, Ahmed & Al-Jaafari, Khaled Ali & Zeineldin, Hatem & Mohamed, Yasser Abdel-Rady I. & El-Saadany, Ehab, 2024. "Improving the robustness of distributed secondary control in autonomous microgrids to mitigate the effects of communication delays," Applied Energy, Elsevier, vol. 364(C).
    9. Dong, Chaoyu & Gao, Qingbin & Xiao, Qian & Yu, Xiaodan & Pekař, Libor & Jia, Hongjie, 2018. "Time-delay stability switching boundary determination for DC microgrid clusters with the distributed control framework," Applied Energy, Elsevier, vol. 228(C), pages 189-204.
    10. Alaperä, Ilari & Honkapuro, Samuli & Paananen, Janne, 2018. "Data centers as a source of dynamic flexibility in smart girds," Applied Energy, Elsevier, vol. 229(C), pages 69-79.
    11. Tang, Yi & Li, Feng & Chen, Qian & Li, Mengya & Wang, Qi & Ni, Ming & Chen, Gang, 2018. "Frequency prediction method considering demand response aggregate characteristics and control effects," Applied Energy, Elsevier, vol. 229(C), pages 936-944.
    12. Morteza Nazari-Heris & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks," Sustainability, MDPI, vol. 14(23), pages 1-24, November.
    13. Zhang, Wenjie & Gandhi, Oktoviano & Quan, Hao & Rodríguez-Gallegos, Carlos D. & Srinivasan, Dipti, 2018. "A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination," Applied Energy, Elsevier, vol. 229(C), pages 96-110.
    14. 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.
    15. 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.
    16. Ashraf Khalil & Ang Swee Peng, 2018. "A New Method for Computing the Delay Margin for the Stability of Load Frequency Control Systems," Energies, MDPI, vol. 11(12), pages 1-18, December.
    17. Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Muhammad Omer Khan & Chul-Hwan Kim, 2021. "Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders," Energies, MDPI, vol. 14(2), pages 1-16, January.
    18. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    19. Neofytos Neofytou & Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2019. "Modeling Vehicles to Grid as a Source of Distributed Frequency Regulation in Isolated Grids with Significant RES Penetration," Energies, MDPI, vol. 12(4), pages 1-23, February.
    20. Shang-Guan, Xingchen & He, Yong & Zhang, Chuanke & Jiang, Lin & Spencer, Joseph William & Wu, Min, 2020. "Sampled-data based discrete and fast load frequency control for power systems with wind power," Applied Energy, Elsevier, vol. 259(C).

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