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Online Area Load Modeling in Power Systems Using Enhanced Reinforcement Learning

Author

Listed:
  • Xiaoya Shang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Zhigang Li

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Tianyao Ji

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • P. Z. Wu

    (Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences, Shenzhen 518055, China)

  • Qinghua Wu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

Abstract

The accuracy of load modeling directly influences power system operation and control. Previous modeling studies have mainly concentrated on the loads connected to a single boundary bus, without thoroughly considering the static load characteristics of the voltage. To remedy this oversight, this paper proposes an accurate modeling approach for area loads with multiple boundary buses and ZIP loads (a combination of constant-impedance, constant-current and constant-power loads) based on Ward equivalence. Furthermore, to satisfy the requirements for real-time monitoring, the model parameters are identified in an online manner using an enhanced reinforcement learning (ERL) algorithm. Parallel tables of value functions are implemented in the ERL algorithm to improve its tracking performance. Three simulation cases are addressed, the first involving a single ZIP load and the second and third involving area loads in the IEEE 57-bus system and in a real 1209-bus power system in China, respectively. The results demonstrate that the ERL algorithm outperforms an existing reinforcement learning algorithm and the improved least-squares method in terms of convergence and the ability to track both step-changing and time-varying loads. Additionally, the results obtained on test cases confirm that the proposed area load model is more accurate than a previously introduced model.

Suggested Citation

  • Xiaoya Shang & Zhigang Li & Tianyao Ji & P. Z. Wu & Qinghua Wu, 2017. "Online Area Load Modeling in Power Systems Using Enhanced Reinforcement Learning," Energies, MDPI, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1852-:d:118571
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    References listed on IDEAS

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    1. Taeho Kim & Monika Ivantysynova, 2017. "Active Vibration Control of Swash Plate-Type Axial Piston Machines with Two-Weight Notch Least Mean Square/Filtered-x Least Mean Square (LMS/FxLMS) Filters," Energies, MDPI, vol. 10(5), pages 1-16, May.
    2. Huiru Zhao & Yuwei Wang & Sen Guo & Mingrui Zhao & Chao Zhang, 2016. "Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling," Energies, MDPI, vol. 9(9), pages 1-20, September.
    3. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    4. Qian Zhang & Kin Keung Lai & Dongxiao Niu & Qiang Wang & Xuebin Zhang, 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power," Energies, MDPI, vol. 5(9), pages 1-18, September.
    5. Xiangwei Guo & Longyun Kang & Yuan Yao & Zhizhen Huang & Wenbiao Li, 2016. "Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm," Energies, MDPI, vol. 9(2), pages 1-16, February.
    6. Chuanjia Han & Bo Yang & Tao Bao & Tao Yu & Xiaoshun Zhang, 2017. "Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer," Energies, MDPI, vol. 10(5), pages 1-24, May.
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