IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1852-d118571.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1852/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/11/1852/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lavička, Hynek & Kracík, Jiří, 2020. "Fluctuation analysis of electric power loads in Europe: Correlation multifractality vs. Distribution function multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    3. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    4. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    5. Benedikt Finnah, 2022. "Optimal bidding functions for renewable energies in sequential electricity markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 1-27, March.
    6. Yajing Gao & Xiaojie Zhou & Jiafeng Ren & Zheng Zhao & Fushen Xue, 2018. "Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game," Energies, MDPI, vol. 11(5), pages 1-19, April.
    7. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    8. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    9. repec:dui:wpaper:1504 is not listed on IDEAS
    10. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    11. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    12. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    13. Wilkinson, Sam & Maticka, Martin J. & Liu, Yue & John, Michele, 2021. "The duck curve in a drying pond: The impact of rooftop PV on the Western Australian electricity market transition," Utilities Policy, Elsevier, vol. 71(C).
    14. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    15. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    16. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    17. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    18. Arim Jin & Dahan Lee & Jong-Bae Park & Jae Hyung Roh, 2023. "Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation," Energies, MDPI, vol. 16(7), pages 1-19, April.
    19. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    20. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    21. Germeshausen, Robert & Wölfing, Nikolas, 2019. "How marginal is lignite? Two simple approaches to determine price-setting technologies in power markets," ZEW Discussion Papers 19-031, ZEW - Leibniz Centre for European Economic Research.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1852-:d:118571. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.