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A Stochastic Model Predictive Control Method for Tie-Line Power Smoothing under Uncertainty

Author

Listed:
  • Molin An

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Xueshan Han

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Tianguang Lu

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

Abstract

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method.

Suggested Citation

  • Molin An & Xueshan Han & Tianguang Lu, 2024. "A Stochastic Model Predictive Control Method for Tie-Line Power Smoothing under Uncertainty," Energies, MDPI, vol. 17(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3515-:d:1437300
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    References listed on IDEAS

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    1. Li, Zhengmao & Xu, Yan & Wang, Peng & Xiao, Gaoxi, 2023. "Coordinated preparation and recovery of a post-disaster Multi-energy distribution system considering thermal inertia and diverse uncertainties," Applied Energy, Elsevier, vol. 336(C).
    2. Xiao, Zhao-xia & Guerrero, Josep M. & Shuang, Jia & Sera, Dezso & Schaltz, Erik & Vásquez, Juan C., 2018. "Flat tie-line power scheduling control of grid-connected hybrid microgrids," Applied Energy, Elsevier, vol. 210(C), pages 786-799.
    3. M. C. Campi & S. Garatti, 2011. "A Sampling-and-Discarding Approach to Chance-Constrained Optimization: Feasibility and Optimality," Journal of Optimization Theory and Applications, Springer, vol. 148(2), pages 257-280, February.
    4. Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
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    Cited by:

    1. Raymundo Cordero & Matheus Caramalac & Wisam Ali, 2024. "Generalized Predictive Control with Added Zeros and Poles in Its Augmented Model for Power Electronics Applications," Energies, MDPI, vol. 17(23), pages 1-17, December.

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