Blending distributed photovoltaic and demand load forecasts

Abstract Utilities consider having accurate electric load forecasts to be critical to their day-to-day operations. But the growing penetration of distributed photovoltaic (DPV) solar power production “behind the meter” makes it more difficult to predict load because it is hard to distinguish between increased solar power generation and decreased power consumption at a site with installed DPV. This paper describes the development of a “top down” solar power forecasting system and how it can be integrated with a load forecasting system that incorporates weather information. Both systems depend on accurate weather forecasts, information regarding the utility variables, and daily and seasonal factors. The load forecasting system provides day-ahead forecasts having roughly 2% error, on average, and the solar power forecast is within 5% error. In general, we find that if the load forecasting system incorporates the same weather and solar predictors as the solar power forecasting system, it implicitly accounts for DPV generation during periods of stationary solar power deployment, and a separate DPV forecast may not be necessary. However, for higher penetrations of solar power during times of rapid deployment of additional solar capacity, it may become important to explicitly incorporate the solar power forecasts to avoid degradation of the load forecasts. This approach could allow utilities and independent system operators to better deal with rapidly increasing penetration of DPV generation.

[1]  J. Wolfowitz,et al.  Introduction to the Theory of Statistics. , 1951 .

[2]  Hassan Soltan,et al.  A methodology for Electric Power Load Forecasting , 2011 .

[3]  Amanpreet Kaur,et al.  Net load forecasting for high renewable energy penetration grids , 2016 .

[4]  E. Lorenz,et al.  Overview of Irradiance and Photovoltaic Power Prediction , 2014 .

[5]  Sue Ellen Haupt,et al.  Solar Forecasting: Methods, Challenges, and Performance , 2015, IEEE Power and Energy Magazine.

[6]  S. E. Haupt,et al.  The Role of Unresolved Clouds on Short-Range Global Horizontal Irradiance Predictability , 2016 .

[7]  B. Hodge,et al.  The value of day-ahead solar power forecasting improvement , 2016 .

[8]  H. Pedro,et al.  Benefits of solar forecasting for energy imbalance markets , 2016 .

[9]  Yu Xie,et al.  Building the Sun4Cast System: Improvements in Solar Power Forecasting , 2017 .

[10]  Yongli Wang,et al.  Short-term power load forecasting based on IVL-BP neural network technology , 2012 .

[11]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[12]  J. Ross Quinlan,et al.  Generating Production Rules from Decision Trees , 1987, IJCAI.

[13]  S. E. Haupt,et al.  WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction , 2016 .

[14]  Tao Hong,et al.  Energy Forecasting: Past, Present, and Future , 2013 .

[15]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[16]  John K. Williams,et al.  Using random forests to diagnose aviation turbulence , 2013, Machine Learning.

[17]  Wei-Chiang Hong,et al.  Electric load forecasting by support vector model , 2009 .

[18]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[19]  S. E. Haupt,et al.  A Wind Power Forecasting System to Optimize Grid Integration , 2012, IEEE Transactions on Sustainable Energy.

[20]  Bri-Mathias Hodge,et al.  Recent Trends in Variable Generation Forecasting and Its Value to the Power System , 2015, IEEE Transactions on Sustainable Energy.

[21]  S. E. Haupt,et al.  The Sun4Cast® Solar Power Forecasting System: The Result of the Public-Private-Academic Partnership to Advance Solar Power Forecasting , 2016 .

[22]  Sue Ellen Haupt,et al.  A consensus forecasting approach for improved turbine hub height wind speed predictions , 2011 .

[23]  Sue Ellen Haupt,et al.  Variable Generation Power Forecasting as a Big Data Problem , 2017, IEEE Transactions on Sustainable Energy.

[24]  Moon-Hee Park,et al.  Short-term Load Forecasting Using Artificial Neural Network , 1992 .