气候变化研究进展 ›› 2011, Vol. 7 ›› Issue (2): 136-142.

• 气候系统变化 • 上一篇    下一篇

太阳能光伏发电量预报方法的发展

李芬1,陈正洪1,成驰1,段善旭2   

  1. 1. 湖北省气象科技服务中心
    2. 华中科技大学电气与电子工程学院
  • 收稿日期:2010-09-03 修回日期:2010-11-30 出版日期:2011-03-30 发布日期:2011-04-07
  • 通讯作者: 李芬 E-mail:beckyhust@163.com
  • 基金资助:
    GYHY201006036;QY-Z-201010;自然科学基金

Review on Forecast Methods for Photovoltaic Power Generation

  • Received:2010-09-03 Revised:2010-11-30 Online:2011-03-30 Published:2011-04-07

摘要: 太阳能光伏发电技术成为当今世界可再生能源发电领域的一个研究热点。在未来,我国大规模的并网光伏发电系统将持续快速发展,但目前我国对太阳能光伏发电量预报方法的研究还很薄弱,几乎没有可满足实际太阳能光伏发电量预报需求的方法和系统。太阳能光伏发电量预报,主要是通过太阳总辐射的准确预报,结合光伏电站历史发电量数据分析,进而得到光伏发电量预报。通过对国内外太阳能光伏发电量预报方法的介绍和分类,以及对国际上太阳能光伏发电量预报系统建设的介绍,希望对我国太阳能光伏发电量预报系统发展起到一定的促进和推动作用。

关键词: 光伏发电, 效率模型, 气象要素, 太阳总辐射, 预报, 动力-统计方法, photovoltaic power generation system, efficiency model, meteorological factors, total solar radiation, forecasting, statistic-dynamic method

Abstract: Solar photovoltaic technology is becoming one of the hot issues in the field of renewable energy generation. In future, China’s large-scale grid-connected photovoltaic power generation system will be continuously in rapid development. But, so far, the exploring of photovoltaic power generation forecasting is still weak, and there are few methods available to meet the practical needs of photovoltaic power generation prediction in China. Photovoltaic power generation prediction means to accurately predict solar irradiances at first, and then in combination with the analysis of the historic power generation data of photovoltaic power station, to further forecast photovoltaic power. In this paper, we briefly introduce and classify several types of photovoltaic power generation forecasting models, such as the simulation-prediction method based on global solar radiation prediction and photovoltaic simulator, the physical prediction method based on global solar radiation prediction and photoelectric conversion efficiency model, the statistic-dynamic method based on the meteorological data and photoelectric power generation data processing and numerical weather prediction. Meanwhile, we also simply introduce photovoltaic power generation forecasting platform’s construction abroad.

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