DOE funds $12 million in solar-generation modeling research

You could be forgiven if you’re having a hard time discerning what the U.S. Department of Energy ‘s (DOE) real attitude toward the solar industry is.

On the one hand, you have Secretary of Energy Rick Perry’s order to the Federal Energy Regulatory Commission to create a rule that would bail out uneconomic coal and nuclear plants, putting solar on the back burner if not undermining its economic advantages.

On the other hand, they provided $46 million in July to continue the Sunshot Initiative and, yesterday, announced $12 million in grants to eight projects dedicated to predicting solar generation levels more accurately.

So … your guess is as good as ours (not that we’re complaining about the funding they’re providing the industry, mind you).

The goal of the latest funding round , according to the department’s release, is to give utilities the most advanced tools to manage the variability of solar generation more effectively and, as always, improve grid reliability.

“These projects will address a critical gap in our research, which is knowing precisely how much solar electricity to expect at any given hour on any given day,” Perry said. “These tools are becoming more important as the solar industry continues to grow and will work to ensure that solar contributes to the reliability, affordability, and resilience of our nation’s electric [sic] grid.”

The DOE believes its investment will create $2.6 million in private sector funding.

Awardees

University of Arizona

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.

Pacific Northwest National Laboratory

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.

University of California San Diego

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.

National Renewable Energy Laboratory

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.

Brookhaven National Laboratory

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.

Electric Power Research Institute, Inc.

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.

National Renewable Energy Laboratory

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.

Johns Hopkins University

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.