The Solar Forecasting 2 funding program builds on the Improving Solar Forecasting Accuracy funding program to support projects that generate tools and knowledge to enable grid operators to better forecast how much solar energy will be added to the grid. These efforts will improve the management of solar power’s variability and uncertainty, enabling its more reliable and cost-effective integration onto the grid. This funding program supports the Energy Department’s broader Grid Modernization Initiative, a crosscutting effort that helps to better integrate all sources of electricity, improve the security of our nation's grid, solve challenges of energy storage and distributed generation, and provide a critical platform for U.S. competitiveness and innovation in a global energy economy. The Department of Energy announced selections for Solar Forecasting 2 on December 19, 2017. Read the announcement.
Projects funded under Solar Forecasting 2 fall under three topic areas:
- In the first topic, one project is developing a test framework to benchmark solar irradiance and solar power forecasting models, as well as a transparent set of rules and metrics that will allow industry and academia to compare different models and evaluate performance. This test framework will be used to validate the models developed by other awardees.
- In the second topic, projects are developing predictive irradiance models that significantly improve on existing capabilities and reduce errors associated with large-scale cloud movement.
- In the third topic, projects are researching solutions that integrate solar power generation models with energy management systems to enhance grid operation. Each project partners with a utility or system operator to test the solution under real-life conditions.
These projects seek to improve solar irradiance and power prediction, which includes quantifying the impacts from clouds and other weather conditions while untangling customer load from PV generation on the distribution system, where visibility is often limited to the net load (coupling load and PV generation). Specifically, these projects target the improvement of solar resource predictions that occur in two time frames: 24 to 48 hours in advance for day-ahead planning, and from one to six hours in advance for real-time grid operation. Forecasts in these two particular time windows have been particularly challenging for existing solar forecasting models. These targeted performance improvements will generate tools and knowledge that will enable utilities to more accurately plan for generation reserves and real-time load balancing.
-- Award and cost share amounts are subject to change pending negotiations –
Project Title: Open Source Evaluation Framework for Solar Forecasting
Location: Tucson, AZ
SETO Award Amount: $999,808
Awardee Cost Share: $261,414
Project Description: This project develops an open-source framework that enables evaluations of irradiance, solar power, and net-load forecasts. Team members have previously collaborated on forecasting trials for utilities, developed operational solar and wind forecasts, and led projects using the open-source PVLib simulation and performance tool. The goal is to make the open-source evaluation framework more easily available for forecast providers, utilities, balancing authorities and fleet operators for non-biased forecast model assessment.
Project Title: Development of the Next Weather Research and Forecasting Model – Improving Solar Forecasts
Location: Richland, WA
SETO Award Amount: $1,214,872
Awardee Cost Share: $150,306
Project Description: This project is developing the next generation of solar resource capabilities integrated into the weather research and forecasting (WRF) model to include enhancements for intra-day and day-ahead forecasts of solar irradiance. The new or improved treatments include absorptive aerosol, cloud microphysics, subgrid variability in irradiance, and application of uncertainty quantification techniques.
Project Title: Hybrid Adaptive Input Model Objective Selection Ensemble Forecasts for Intra-Day and Day-Ahead Global Horizon Irradiance, Direct Normal Irradiance, and Ramps
Location: San Diego, CA
SETO Award Amount: $1,316,203
Awardee Cost Share: $162,500
Project Description: This project develops a Hybrid Adaptive Input Model Objective Selection ensemble model to improve solar irradiance and cloud cover forecasts. Major components of this ensemble include a holistic optimization framework and ingestion of new-generation cloud cover products. The goal is to increase the state-of-the-art predictive capabilities for solar generation from their present values of 10 percent to 30 percent (with a stretch goal of 50 percent) consistently for both global horizon solar irradiance and direct normal irradiance.
Project Title: Probabilistic Cloud Optimized Day-Ahead Forecasting System Based on Weather Research and Forecasting Solar System
Location: Golden, CO
SETO Award Amount: $1,720,806
Awardee Cost Share: $212,482
Project Description: This project develops a publicly available ensemble-based solar capability for the weather research and forecasting (WRF) model that will serve as a baseline operational solar irradiance forecasting model. The team will use an adjoint analysis technique to adjust the most important variables and calibrate the WRF solar system ensemble to provide accurate estimates of forecast uncertainties. This resulting system will increase the accuracies of intra-day and day-ahead probabilistic solar forecasts that can be used in grid operations.
Project Title: Advancing the Weather Research and Forecasting Solar Model to Improve Solar Irradiance Forecast in Cloudy Environments
Location: Upton, NY
SETO Award Amount: $1,600,000
Awardee Cost Share: $214,195
Project Description: This project is developing solar-specific improvements to the weather research and forecasting model for improving prediction of solar irradiance in cloudy environments. Specific areas of improvements are cloud microphysics, radiative transfer, and innovative analysis packages.
Project Title: Probabilistic Forecasts and Operational Tools to Improve Solar Integration
Location: Knoxville, TN
SETO Award Amount: $1,800,000
Awardee Cost Share: $759,008
Project Description: This project is developing improved probabilistic solar and net load forecasts for three separate utility case studies, each with different operating procedures. The team is using advanced tools to research and develop methods for each utility to manage uncertainty in a reliable and economic manner in daily operations. In addition, they will validate these methods by integrating forecasts and decision making functions into a scheduling management platform to verify the use of probabilistic forecasts to reduce integration costs.
Project Title: Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operations
Location: Golden, CO
SETO Award Amount: $1,698,933
Awardee Cost Share: $331,930
Project Description: The project is designing novel algorithms to create probabilistic solar power forecasts and automate their integration into power system operations. Adaptive reserves will dynamically adjust reserve levels conditional on meteorological and power system states. Risk-parity dispatch will be developed to produce optimal dispatch strategies by cost-weighting solar generation scenarios on forecast uncertainty. This project will test the integration of probabilistic solar forecasts into the Electric Reliability Council of Texas’ real-time operation environment through automated reserve and dispatch tools that can increase economic efficiency and improve system reliability.
Project Title: Coordinated Ramping Product and Regulation Reserve Procurements in California Independent System Operator and Midcontinent Independent System Operator Using Multi-Scale Probabilistic Solar Power Forecasts
Location: Baltimore, MD
SETO Award Amount: $1,738,630
Awardee Cost Share: $482,953
Project Description: This project is advancing the state-of-the-art in solar forecasting technologies by developing short-term and day-ahead probabilistic solar power prediction capabilities. The proposed technology will be based on the big-data-driven, transformative IBM Watt-Sun platform, which will be driven by parallel computation-based scalable and fast data curation technology and multi-expert machine learning based model blending. The integration of validated probabilistic solar forecasts into the scheduling operations of both the Midcontinent and California Independent System Operators will be tested, via efficient and dynamic procurement of ramp product and regulation. Integration of advanced visualization of ramping events and associated alerts into their energy management systems and control room operations will also be researched and validated.
Learn more about SETO's other systems integration funding programs.