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Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems

Author

Listed:
  • Yalin Liang

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yuyao He

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yun Niu

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

Regarding the microgrid with large-scale electric vehicle (EV) energy storage systems working at the vehicle-to-grid (V2G) mode, uncertain factors (e.g., the number of EVs feeding the microgrid shifts frequently) make the system unfixed, leading to the fact that it is difficult to precisely determine the real-time droop coefficients of the system, thereby degrading the performance of the traditional inverter control strategies that rely on the droop coefficients. To solve the problem, this paper proposes an errorless-control-targeted double control loop (DCL) technique based on robust MPC to control the microgrid with EV energy storage systems without using droop coefficients. Firstly, the structure of the DCL method is developed, with each component in the structure detailed. Compared to the traditional control strategies, the novel one regards the frequency, voltage, and currents as the control objectives instead of active/inactive power. It deserves to be mentioned that the frequency and voltage are regulated by proportional-integral controllers, while the currents are regulated by the finite control set model predictive control (FCS-MPC) method. Secondly, the impacts of system parameter uncertainties on the prediction accuracy of the FCS-MPC controller are analyzed clearly, illustrating that it is necessary to develop effective techniques to enhance the robustness of the controller. Thirdly, sliding mode observers (SMO) based on a novel hyperbolic function are constructed to detect the real-time disturbances, which can be used to generate voltage compensations by using automatic disturbance regulators. Then, the voltage compensations are adopted to establish a modified predicting plant model (PPM) used for the FCS-MPC controller. By using the proposed SMO-based disturbance detection and compensation techniques, the MPC controller gains a strong robustness against parameter uncertainties. Finally, a simulation is conducted on a microgrid system to verify the effectiveness of the proposed techniques, and the obtained results are compared with the traditional virtual synchronous machine (VSG) strategy relying on droop coefficients.

Suggested Citation

  • Yalin Liang & Yuyao He & Yun Niu, 2022. "Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems," Energies, MDPI, vol. 15(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1398-:d:749629
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    References listed on IDEAS

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    1. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
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    4. Nan Jin & Chao Pan & Yanyan Li & Shiyang Hu & Jie Fang, 2020. "Model Predictive Control for Virtual Synchronous Generator with Improved Vector Selection and Reconstructed Current," Energies, MDPI, vol. 13(20), pages 1-16, October.
    5. Seghir Benhalima & Rezkallah Miloud & Ambrish Chandra, 2018. "Real-Time Implementation of Robust Control Strategies Based on Sliding Mode Control for Standalone Microgrids Supplying Non-Linear Loads," Energies, MDPI, vol. 11(10), pages 1-18, September.
    6. Youssef Hennane & Abdelmajid Berdai & Jean-Philippe Martin & Serge Pierfederici & Farid Meibody-Tabar, 2021. "New Decentralized Control of Mesh AC Microgrids: Study, Stability, and Robustness Analysis," Sustainability, MDPI, vol. 13(4), pages 1-25, February.
    7. Yalin Liang & Yuyao He & Yun Niu, 2020. "Microgrid Frequency Fluctuation Attenuation Using Improved Fuzzy Adaptive Damping-Based VSG Considering Dynamics and Allowable Deviation," Energies, MDPI, vol. 13(18), pages 1-23, September.
    8. Francesco Simmini & Tommaso Caldognetto & Mattia Bruschetta & Enrico Mion & Ruggero Carli, 2021. "Model Predictive Control for Efficient Management of Energy Resources in Smart Buildings," Energies, MDPI, vol. 14(18), pages 1-19, September.
    9. Antonio Camacho & Miguel Castilla & Franco Canziani & Carlos Moreira & Paulo Coelho & Mario Gomes & Pedro E. Mercado, 2017. "Performance Comparison of Grid-Faulty Control Schemes for Inverter-Based Industrial Microgrids," Energies, MDPI, vol. 10(12), pages 1-25, December.
    10. Prudhvi Kumar Gorijeevaram Reddy & Sattianadan Dasarathan & Vijayakumar Krishnasamy, 2021. "Investigation of Adaptive Droop Control Applied to Low-Voltage DC Microgrid," Energies, MDPI, vol. 14(17), pages 1-20, August.
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    Cited by:

    1. Mahmoud F. Elmorshedy & Umashankar Subramaniam & Jagabar Sathik Mohamed Ali & Dhafer Almakhles, 2023. "Energy Management of Hybrid DC Microgrid with Different Levels of DC Bus Voltage for Various Load Types," Energies, MDPI, vol. 16(14), pages 1-32, July.
    2. Yan Yang & Yeqin Wang & Weixing Zhang & Zhenghao Li & Rui Liang, 2022. "Design of Adaptive Fuzzy Sliding-Mode Control for High-Performance Islanded Inverter in Micro-Grid," Energies, MDPI, vol. 15(23), pages 1-25, December.

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