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Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models

Author

Listed:
  • Jinling Lu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Bo Wang

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China)

  • Hui Ren

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Daqian Zhao

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Fei Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA)

  • Miadreza Shafie-khah

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal)

  • João P. S. Catalão

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal
    INESC TEC and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal)

Abstract

To address the static voltage stability issue and suppress the voltage fluctuation caused by the increasing integration of wind farms and solar photovoltaic (PV) power plants, a two-tier reactive power and voltage control strategy based on ARMA power forecasting models for wind and solar plants is proposed in this paper. Firstly, ARMA models are established to forecast the output of wind farms and solar PV plants. Secondly, the discrete equipment is pre-regulated based on the single-step prediction information from ARMA forecasting models according to the optimization result. Thirdly, a multi-objective optimization model is presented and solved by particle swarm optimization (PSO) according to the measured data and the proposed static voltage stability index. Finally, the IEEE14 bus system including a wind farm and solar PV plant is utilized to test the effectiveness of the proposed strategy. The results show that the proposed strategy can suppress voltage fluctuation and improve the static voltage stability under the condition of high penetration of renewables including wind and solar power.

Suggested Citation

  • Jinling Lu & Bo Wang & Hui Ren & Daqian Zhao & Fei Wang & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1518-:d:113867
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    References listed on IDEAS

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    1. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    2. Yujing Sun & Fei Wang & Bo Wang & Qifang Chen & N.A. Engerer & Zengqiang Mi, 2016. "Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems," Energies, MDPI, vol. 10(1), pages 1-20, December.
    3. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    4. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    5. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
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    Cited by:

    1. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    2. Piotr Kacejko & Paweł Pijarski, 2021. "Optimal Voltage Control in MV Network with Distributed Generation," Energies, MDPI, vol. 14(2), pages 1-19, January.
    3. Dash, Deepak Ranjan & Dash, P.K. & Bisoi, Ranjeeta, 2021. "Short term solar power forecasting using hybrid minimum variance expanded RVFLN and Sine-Cosine Levy Flight PSO algorithm," Renewable Energy, Elsevier, vol. 174(C), pages 513-537.

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