Learn about the application of time-series solar irradiance data for project siting, planning and operations.

Forecasting High-Penetration Distributed PV

The intermittent nature of PV requires utilities and independent system operators to adapt their planning, scheduling and operating strategies to maintain grid reliability and get the most value out of installed PV resources. These papers describe projects that use advanced methodologies to forecast high-penetration PV fleet output, and integrate forecasts into grid system operations.

• Extending Fleet Forecasting Capability into the Probabilistic Realm (June 2018 – presentation)
• EPIC Solar Forecasting Task 3 Final Report: Grid-Connected and Embedded PV Fleet Forecasting Accuracy (March 2018)
• EPIC Solar Forecasting Task 2 Final Report: Data Forecasting Accuracy (March 2018)
• Advancing the Science of Behind-the-Meter PV Forecasting (February 2018 – presentation)
• Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations (February 2016)
• CSI RD&D Solicitation 3: Integrating PV into Utility Planning and Operation Tools (April 2014)
• Experience in California with Behind-the-Meter PV Forecasts (Feb. 2014 – presentation)
• Behind-the-Meter PV Fleet Forecasting: Results for 130,000 PV Systems in California (April 2013 – presentation)
• Forecasting Output for 130,000 PV Systems in California (Feb. 2013 – presentation)
• Integrating PV into Utility Planning and Operation Tools (Feb. 2013 – presentation)
• Behind-the-Meter PV Fleet Forecasting (Oct. 2012 – presentation)
• Solar Monitoring, Forecasting and Variability Assessment at SMUD (April 2012)

PV Power Output Variability Methodologies

These papers present methodologies for cost-effectively predicting the power output variability of high-penetration PV fleets.

• Spatial and Temporal Variability of Solar Energy (July 2016)
• CSI RD&D Solicitation 1: Advanced Modeling and Verification for High Penetration PV (April 2012)
• Determining Storage Reserves for Regulating Solar Variability (Nov. 2011)
• Modeling PV Fleet Output Variability (May 2011)
• PV Power Output Variability: Calculation of Correlation Coefficients Using Satellite Insolation Data (March 2011)
• Short-term Irradiance Variability: Station Pair Correlation as a Function of Distance (March 2011)
• Parameterization of Site-specific Short-term Irradiance Variability (Nov. 2010)
• PV Power Output Variability: Correlation Coefficients (Nov. 2010)
• Evaluating PV Fleet Output Variability (Dec. 2009 – presentation)
• Quantifying PV Power Output Variability (May 2009)

Accuracy of Satellite-Derived Irradiance Data, Irradiance Forecasts and PV Performance Models

This research provides government researchers, utilities, independent system operators and developers information on PV power output models and methods of calculating and quantifying the accuracy of satellite-derived solar irradiance data for specific applications.

• Improved model of solar resource variability based on regional aggregation and climate zones (Jun 2018)
• Detecting Calibration Drift at Ground Truth Stations: A Demonstration of Satellite Irradiance Models’ Accuracy (July 2017)
• Importance of Input Data and Uncertainty Associated with Tuning Satellite to Ground Solar Irradiation (June 2016)
• Reducing Solar Project Uncertainty with an Optimized Resource Assessment Tuning Methodology (June 2014)
• Day Ahead Irradiance Forecast Variability Characterization Using Satellite Data  (June 2016)
• Solar Energy Forecast Validation for Extended Areas & Economic Impact of Forecast Accuracy (June 2016)
• On the Ability of Ground Based Global Horizontal Irradiance Measurements to Reduce Error in Satellite Derived Plane of Array Irradiance data for Fixed Tilt Photovoltaic Power Plants (June 2016)
• Satellite-to-Irradiance Modeling – A New Version of the SUNY Model (May 2015)
• CEC PIER Project: Demonstration and Validation of PV Output Variability Modeling Approach (July 2013)
• Operational Improvements in the Performance of the SUNY Satellite-to-Solar Irradiance Model Using Satellite Infrared Channels (June 2013)
• Solar Monitoring, Forecasting and Variability Assessment at SMUD (April 2012)
• Reporting of Irradiance Model Relative Errors (March 2012)
• Predicting Short-term Variability of High Penetration PV (March 2012)
• Evaluating Irradiance Accuracy Using California ISO Data: Lessons Learned (Feb. 2012 – presentation)
• Validation of Short and Medium Term Operational Solar Radiation Forecasts in the U.S. (Aug. 2010)
• Validation of PV Performance Models Using Satellite-based Irradiance Measurements: A Case Study (May 2010)
• Field Validation of the Clean Power Estimator’s Obstruction Analysis Algorithm (Feb. 2004)
• Validation of a Simplified PV Simulation Engine (June 2003)

Determining the Value of PV Using Solar Prediction and Modeling

Solar developers and utility planners are often faced with the task of determining the economic value of one or more PV systems. These research offers insights into the value assessment process.

• Solar Energy Forecast Validation for Extended Areas & Economic Impact of Forecast Accuracy (June 2016)
• Off-Shore Wind and Grid-connected PV: High Penetration Peak Shaving for New York City (March 2012)
• Valuing a Portfolio of PV Investments (Oct. 2010 – presentation)
• Weather Adjusted Performance Guarantees (May 2010)
• Shining on the Big Apple: Satisfying New York City’s Peak Electrical Needs with PV (Nov. 2008 – presentation)