Building the Sun4Cast System: Improvements in Solar Power Forecasting

AbstractAs integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Fo...

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

[2]  Hui-Chuan Lin,et al.  Multi-sensor Advection Diffusion nowCast (MADCast) for cloud analysis and short-term prediction , 2014 .

[3]  J. A. Ruiz-Arias,et al.  A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model , 2014 .

[4]  G. Thompson,et al.  A Study of Aerosol Impacts on Clouds and Precipitation Development in a Large Winter Cyclone , 2014 .

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

[6]  J. Apt,et al.  The character of power output from utility‐scale photovoltaic systems , 2008 .

[7]  S. E. Haupt,et al.  Solar Irradiance Nowcasting Case Studies near Sacramento , 2017 .

[8]  L. D. Monache,et al.  An analog ensemble for short-term probabilistic solar power forecast , 2015 .

[9]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[10]  Thomas Auligné,et al.  Multivariate Minimum Residual Method for Cloud Retrieval. Part I: Theoretical Aspects and Simulated Observation Experiments , 2014 .

[11]  J. Lazo.  Economic Value of Research to Improve Solar Power Forecasting , 2017 .

[12]  A. Troccoli,et al.  Interannual variability of solar energy generation in Australia , 2012 .

[13]  S. Miller,et al.  Cloud Advection Schemes for Short-Term Satellite-Based Insolation Forecasts , 2012 .

[14]  Jan Kleissl,et al.  Solar Energy Forecasting and Resource Assessment , 2013 .

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

[16]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

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

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

[19]  Erik Ela,et al.  Impacts of Variability and Uncertainty in Solar Photovoltaic Generation at Multiple Timescales , 2013 .

[20]  Dong Huang,et al.  3D cloud detection and tracking system for solar forecast using multiple sky imagers , 2015 .

[21]  L. Dubus.  Weather and Climate and the Power Sector: Needs, Recent Developments and Challenges , 2014 .

[22]  Sue Ellen Haupt,et al.  Metrics for Evaluation of Solar Energy Forecasts , 2016 .

[23]  J. A. Ruiz-Arias,et al.  Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe , 2013 .

[24]  J. A. Ruiz-Arias,et al.  Proposal of a regressive model for the hourly diffuse solar radiation under all sky conditions , 2010 .

[25]  S. E. Haupt,et al.  A model tree approach to forecasting solar irradiance variability , 2015 .

[26]  Daniela De Benedetto,et al.  Assessment of spatial and temporal within-field soil variability by using geostatistical techniques1 , 2011 .

[27]  J. Lazo.  An Economic Assessment of Hydro-Met Services and Products: A Value Chain Approach , 2017 .

[28]  Donald Chung,et al.  On the Path to SunShot. The Role of Advancements in Solar Photovoltaic Efficiency, Reliability, and Costs , 2016 .

[29]  Sue Ellen Haupt,et al.  NCAR / TN-527 + STR Metrics for evaluation of solar energy forecasts , 2016 .

[30]  Luca Delle Monache,et al.  Probabilistic Weather Prediction with an Analog Ensemble , 2013 .

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

[32]  Aijun Deng,et al.  A shallow convection parameterization for the non-hydrostatic MM5 mesoscale model , 1996 .

[33]  Peter R. Jones,et al.  Implementation and Evaluation , 1995 .

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

[35]  S. E. Haupt,et al.  Regime-Dependent Short-Range Solar Irradiance Forecasting , 2016 .

[36]  S. E. Haupt,et al.  A regime-dependent artificial neural network technique for short-range solar irradiance forecasting , 2016 .

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

[38]  Anthony R. Florita,et al.  Sub-Hourly Impacts of High Solar Penetrations in the Western United States: Preprint , 2012 .

[39]  J. Dudhia,et al.  A Fast All-sky Radiation Model for Solar applications (FARMS): Algorithm and performance evaluation , 2016 .

[40]  Jean-Jacques Morcrette,et al.  Aerosols for Concentrating Solar Electricity Production Forecasts: Requirement Quantification and ECMWF/MACC Aerosol Forecast Assessment , 2012 .

[41]  S. Miller,et al.  Short-term solar irradiance forecasting via satellite/model coupling , 2017, Solar Energy.

[42]  R. Stull,et al.  Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions , 2011 .

[43]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[44]  Thomas Auligné.  Multivariate Minimum Residual Method for Cloud Retrieval. Part II: Real Observations Experiments , 2014 .

[45]  Bri-Mathias Hodge,et al.  A suite of metrics for assessing the performance of solar power forecasting , 2015 .

[46]  Jimy Dudhia,et al.  Surface clear‐sky shortwave radiative closure intercomparisons in the Weather Research and Forecasting model , 2013 .

[47]  J. Kain,et al.  A Shallow-Convection Parameterization for Mesoscale Models. Part I: Submodel Description and Preliminary Applications. , 2003 .