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Impact of onsite solar generation on system load demand forecast

机译：现场太阳能发电对系统负荷需求预测的影响

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摘要

Net energy metering tariffs have encouraged the growth of solar PV in the distribution grid. The additional variability associated with weather-dependent renewable energy creates new challenges for power system operators that must maintain and operate ancillary services to balance the grid. To deal with these issues power operators mostly rely on demand load forecasts. Electric load forecast has been used in power industry for a long time and there are several well established load forecasting models. But the performance of these models for future scenario of high renewable energy penetration is unclear. In this work, the impact of onsite solar power generation on the demand load forecast is analyzed for a community that meets between 10% and 15% of its annual power demand and 3-54% of its daily power demand from a solar power plant. Short-Term Load Forecasts (STLF) using persistence, machine learning and regression-based forecasting models are presented for two cases: (1) high solar penetration and (2) no penetration. Results show that for 1-h and 15-min forecasts the accuracy of the models drops by 9% and 3% with high solar penetration. Statistical analysis of the forecast errors demonstrate that the error distribution is best characterized as a t-distribution for the high penetration scenario. Analysis of the error distribution as a function of daily solar penetration for different levels of variability revealed that the solar power variability drives the forecast error magnitude whereas increasing penetration level has a much smaller contribution. This work concludes that the demand forecast error distribution for a community with an onsite solar generation can be directly characterized based on the local solar irra-diance variability.
机译：净能源计量关税鼓励了配电网中太阳能光伏的增长。与天气相关的可再生能源相关的额外可变性为必须维护和运营辅助服务以平衡电网的电力系统运营商带来了新的挑战。为了解决这些问题，电力运营商主要依靠需求负荷预测。电力负荷预测已在电力行业中使用了很长时间，并且有几种完善的负荷预测模型。但是，这些模型在未来可再生能源普及率较高的情况下的性能尚不清楚。在这项工作中，针对一个太阳能电站满足其年度电力需求的10％至15％和每日电力需求的3-54％的社区，分析了现场太阳能发电对需求负荷预测的影响。针对以下两种情况，提出了使用持久性，机器学习和基于回归的预测模型的短期负荷预测（STLF）：( 1）高太阳穿透率和（2）无穿透率。结果表明，在1h和15min的预测下，高太阳穿透率会使模型的准确性分别下降9％和3％。对预测误差的统计分析表明，对于高渗透率场景，误差分布最能表征为t分布。根据不同的可变性水平将误差分布作为每日太阳穿透力的函数进行分析，结果表明，太阳能功率的可变性会驱动预测的误差量级，而增加的穿透率的贡献则小得多。这项工作得出的结论是，可以根据当地的太阳辐照度变化直接表征具有现场太阳能发电的社区的需求预测误差分布。

著录项

  • 来源
    《Energy Conversion & Management》 |2013年第11期|701-709|共9页
  • 作者

    Amanpreet Kaur; Hugo T.C. Pedro; Carlos F.M. Coimbra;

  • 作者单位

    Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Energy Research and Center of Excellence in Renewable Resource Integration, University of California, San Diego, La Jolla, CA 92093, USA;

    Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Energy Research and Center of Excellence in Renewable Resource Integration, University of California, San Diego, La Jolla, CA 92093, USA;

    Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Energy Research and Center of Excellence in Renewable Resource Integration, University of California, San Diego, La Jolla, CA 92093, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Load forecasting; Solar forecasting; High solar penetration; Error distribution; Photovoltaic farms;

    机译：负荷预测;太阳预报;太阳穿透力高;错误分布;光伏农场;

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