Project Name: Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operations (SUMMER-GO)
Funding Opportunity: Solar Forecasting 2
SETO Subprogram: Systems Integration
Location: Golden, CO
SETO Award Amount: $1,698,608
Awardee Cost Share: $331,930
Principal Investigator: Bri-Mathias Hodge
This project brings together a team of experts in solar power forecasting and electric power system operations to develop an innovative suite of software tools for creating and incorporating probabilistic solar power forecasts into power system operations. The research team will test the integration of probabilistic solar forecasts into the Electric Reliability Council of Texas (ERCOT) real-time operation environment over time in order to increase economic efficiency and improve system reliability. To do this, they will automate the process that determines the appropriate amount of reserves needed and use probabilities of forecasted conditions to calculate when to dispatch generation from power plants.
APPROACH
This project will increase grid reliability and lower the costs of operating the electric grid when there are high penetration levels of solar. The team will achieve these goals by creating probabilistic solar power forecasts that are useful for grid operations. Since solar power generation is affected by cloud cover—which is difficult to predict with certainty—accurate forecasting can allow more solar generation to be contributed to the grid without increasing the cost of power or decreasing its reliability. At the end of the project, the operator of the transmission grid in Texas, ERCOT, will be able to use the probabilistic solar power forecasts in its real-time operations to make better decisions about the amount of power reserves it needs to hold and which power plants it needs to dispatch.
INNOVATIONS
The researchers in this project will develop algorithms that will enable reserve power—power capacity that can be dispatched in response to changing demand or solar power generation—to be calculated based on the state of the grid and the meteorological conditions. In addition, the researchers will develop a method to calculate the best strategies for dispatching power plants by taking into account risks to the grid and the uncertainty of a solar forecast. This work comprises not only the design of these algorithms, but also their validation and implementation in ERCOT’s test system, which mimics the utility’s real-time operational energy management system. The team anticipates that by adopting these algorithms to calculate reserve generation for backup power, there will be a simultaneous increase in the overall reliability of the system and a decrease in the amount of reserves needed to provide flexibility to the system.