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Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources

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  • Yin, Linfei
  • Lu, Yuejiang

Abstract

The article establishes a three-state energy (TSE) model for flexible energy sources (FESs) connected to smart grids. The article proposes a unified time-scale (UTS) coordinated primary voltage control framework and a UTS coordinated primary voltage controller for voltage control of smart grids containing a high proportion of FESs. To mitigate uncoordinated voltage, the proposed control framework integrates traditional secondary and primary voltage control into a UTS. The article proposes an expandable deep width learning (EDWL) for the proposed controller. The proposed controller applies the EDWL for predictive control; the proposed controller outputs the reactive power reference value of each TSE unit in smart grids with the real-time voltages of smart grids pilot buses. The proposed algorithm is numerically simulated with the proportional-integral-derivative (PID) algorithm and deep neural networks (DNNs) in IEEE 118-bus, 300-bus, 1354-bus, and 2383-bus systems. The simulation results show that the proposed framework and controller can quickly and accurately control the grid voltage, and verify the feasibility and effectiveness of the proposed approach. The integral of squared error control performance index is 0.47% smaller than the PID and 0.06% smaller than DNNs.

Suggested Citation

  • Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006861
    DOI: 10.1016/j.energy.2021.120437
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    1. 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).
    2. Wang, Qi & Yang, Li & Huang, Kang, 2022. "Fast prediction and sensitivity analysis of gas turbine cooling performance using supervised learning approaches," Energy, Elsevier, vol. 246(C).

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