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Relaxed deep learning for real-time economic generation dispatch and control with unified time scale

Author

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  • Yin, Linfei
  • Yu, Tao
  • Zhang, Xiaoshun
  • Yang, Bo

Abstract

To solve the collaboration problem of multi-time scale economic dispatch and generation control in power system, i.e., long term time scale optimization, short term time scale optimization, and real-time control, a real-time economic generation dispatch and control (REG) framework, which as a unified time scale framework, is designed in this paper. With a relaxed operator employed into deep neural network (DNN), relaxed deep learning (RDL) is proposed for the REG framework, which towards an alternative to conventional generation control framework, which combines unit commitment (UC), economic dispatch (ED), automatic generation control (AGC), and generation command dispatch (GCD). Compared with 1200 combined conventional generation control algorithms in two simulations, i.e., IEEE 10-generator 39-bus New-England power system and Hainan 8-generator power grid (China), RDL obtains the optimal control performance with smaller frequency deviation, smaller area control error, smaller total cost, and smaller number of reverse regulation. Although the RDL needs a relative long computation time in the pre-training, the simulation results verify the effectiveness and feasibility of the proposed RDL for REG framework.

Suggested Citation

  • Yin, Linfei & Yu, Tao & Zhang, Xiaoshun & Yang, Bo, 2018. "Relaxed deep learning for real-time economic generation dispatch and control with unified time scale," Energy, Elsevier, vol. 149(C), pages 11-23.
    • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:11-23
    DOI: 10.1016/j.energy.2018.01.165
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    References listed on IDEAS

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    Cited by:

    1. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    2. Yin, Linfei & Zhang, Bin, 2023. "Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems," Applied Energy, Elsevier, vol. 330(PA).
    3. Yin, Linfei & Zhao, Lulin, 2021. "Rejectable deep differential dynamic programming for real-time integrated generation dispatch and control of micro-grids," Energy, Elsevier, vol. 225(C).
    4. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).
    5. Shin, Hansol & Kim, Tae Hyun & Kim, Hyoungtae & Lee, Sungwoo & Kim, Wook, 2019. "Environmental shutdown of coal-fired generators for greenhouse gas reduction: A case study of South Korea," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Zhao, Lulin & Yin, Linfei, 2024. "Knowledge-shareable adaptive deep dynamic programming for hierarchical generation control of distributed high-percentage renewable energy systems," Renewable Energy, Elsevier, vol. 228(C).
    7. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    8. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).

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