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Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories

Author

Listed:
  • Yajing Gao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Jing Zhu

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Huaxin Cheng

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Fushen Xue

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Qing Xie

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Peng Li

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

With the increasing permeability of photovoltaic (PV) power production, the uncertainties and randomness of PV power have played a critical role in the operation and dispatch of the power grid and amplified the abandon rate of PV power. Consequently, the accuracy of PV power forecast urgently needs to be improved. Based on the amplitude and fluctuation characteristics of the PV power forecast error, a short-term PV output forecast method that considers the error calibration is proposed. Firstly, typical climate categories are defined to classify the historical PV power data. On the one hand, due to the non-negligible diversity of error amplitudes in different categories, the probability density distributions of relative error (RE) are generated for each category. Distribution fitting is performed to simulate probability density function (PDF) curves, and the RE samples are drawn from the fitted curves to obtain the sampling values of the RE. On the other hand, based on the fluctuation characteristic of RE, the recent RE data are utilized to analyze the error fluctuation conditions of the forecast points so as to obtain the compensation values of the RE. The compensation values are adopted to sequence the sampling values by choosing the sampling values closest to the compensation ones to be the fitted values of the RE. On this basis, the fitted values of the RE are employed to correct the forecast values of PV power and improve the forecast accuracy.

Suggested Citation

  • Yajing Gao & Jing Zhu & Huaxin Cheng & Fushen Xue & Qing Xie & Peng Li, 2016. "Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories," Energies, MDPI, vol. 9(7), pages 1-15, July.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:523-:d:73577
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    References listed on IDEAS

    as
    1. Yajing Gao & Huaxin Cheng & Jing Zhu & Haifeng Liang & Peng Li, 2016. "The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather," Sustainability, MDPI, vol. 8(1), pages 1-22, January.
    2. 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.
    3. Bridier, Laurent & Hernández-Torres, David & David, Mathieu & Lauret, Phillipe, 2016. "A heuristic approach for optimal sizing of ESS coupled with intermittent renewable sources systems," Renewable Energy, Elsevier, vol. 91(C), pages 155-165.
    4. Thomas Huld & Ana M. Gracia Amillo, 2015. "Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum," Energies, MDPI, vol. 8(6), pages 1-23, June.
    5. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
    6. Sharma, Vishal & Yang, Dazhi & Walsh, Wilfred & Reindl, Thomas, 2016. "Short term solar irradiance forecasting using a mixed wavelet neural network," Renewable Energy, Elsevier, vol. 90(C), pages 481-492.
    7. Nantiwat Pholdee & Sujin Bureerat, 2015. "An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(10), pages 1780-1789, July.
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    Cited by:

    1. Yajing Gao & Yanping Sun & Xiaodan Wang & Feifan Chen & Ali Ehsan & Hongmei Li & Hong Li, 2017. "Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique," Energies, MDPI, vol. 10(12), pages 1-20, December.
    2. Weiliang Liu & Changliang Liu & Yongjun Lin & Liangyu Ma & Feng Xiong & Jintuo Li, 2018. "Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather," Energies, MDPI, vol. 11(3), pages 1-22, February.
    3. Yajing Gao & Fushen Xue & Wenhai Yang & Yanping Sun & Yongjian Sun & Haifeng Liang & Peng Li, 2017. "A Three-Part Electricity Price Mechanism for Photovoltaic-Battery Energy Storage Power Plants Considering the Power Quality and Ancillary Service," Energies, MDPI, vol. 10(9), pages 1-21, August.
    4. Yajing Gao & Wenhai Yang & Jing Zhu & Jiafeng Ren & Peng Li, 2017. "Evaluating the Effect of Distributed Generation on Power Supply Capacity in Active Distribution System Based on Sensitivity Analysis," Energies, MDPI, vol. 10(10), pages 1-14, September.

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