Note: this post was originally published on CleanTechnica
By Silvio Marcacci
Breakthrough renewable energy forecasting technologies may be two years away from revolutionizing the efficiency of wind and solar generation on America’s grid.
The National Center for Atmospheric Research (NCAR) is adding to its already impressive list of renewable energy innovations with a new two-year plan to develop custom forecasting systems for wind energy and solar power.
NCAR scientists and engineers will develop technology to improve wind power output by predicting sudden changes in wind speed, help wind farm operators avoid curtailment during icy conditions, and predict the amount of energy generated by small-scale solar energy installations.
Pushing Past State Of The Art To Probabilistic Forecasts
This new batch of renewable energy forecasting systems will be deployed at Xcel Energy regional control centers in Denver, Minneapolis, and Amarillo to help the utility increase renewable energy output to the grid while reducing costs and ensuring a reliable power supply.
“This is pushing the state-of-the-art still further,” said Sue Ellen Haupt, NCAR program director. “Every improvement to the forecasts results in additional savings.”
The new phase of renewable energy forecasting technology will provide “probabilistic forecasts,” meaning utility managers will be able to make decisions based on high-accuracy predictions of certain weather conditions at a wind farm on the next day. Forecasts will focus on wind “ramp” events, ice and extreme temperatures, and distributed solar.
Anticipating Wind Ramp Events
Of the three, predicting ramp events could mean the most for overall generation. Ramp events refer to sudden and significant changes in wind conditions over the span of a few hours due to passing weather fronts or atmospheric events. NCAR’s Variational Doppler Radar Analysis System (VDRAS) will combine radar data with computer simulations to create accurate forecasts for specific wind farms and reduce intermittency.
VDRAS forecasts will allow utilities to accurately estimate how much electricity wind farms can generate, and ramp other baseload generation up or down according to overall demand on the grid. “We are able to power the system using wind more often, and aggressively, than we have in the past,” said Gabriel Romero of Xcel.
Preventing Cold-Weather Effects On Turbines
While ramp events may mean the most for generation, predicting extreme weather conditions could do the most to prevent sudden wind farm shut downs. NCAR is partnering with Pennsylvania State University to develop a 48-hour forecasting system to predict extreme temperature swings as well as the impact of freezing rain and fog on turbines.
Both of these weather conditions can force wind farms to stop operation, and can potentially damage turbine blades. NCAR is applying similar computer models and specialized algorithms as the ones used to keep aircraft safe from potentially lethal in-flight icing conditions.
Predicting Small-Scale Solar Output
If you thought solar was being left out of the forecasting mix, don’t worry – NCAR is also developing a forecasting system to help utilities anticipate the output of small-scale solar installations. The system will predict when customers with rooftop solar will supply their own demand, when they’ll be contributing excess electricity to the grid, and when they’ll need to pull power from utility sources.
This new solar forecasting system will augment NCAR research already underway to forecast sunlight conditions and predict power at 15-minute increments for large-scale solar installations. The existing research will use an array of observational tools to predict cloud cover at solar facilities and the effect different types of clouds will have on generation.
Renewable Energy Forecasting Equals Economic Benefits
All of NCAR’s efforts will ultimately benefit consumers as much as they will benefit the climate. Since electricity can’t yet be stored in large quantities, every time solar or wind system generation suddenly decreases, utilities are forced to turn on coal or natural gas facilities.
In addition to higher emissions, these generation units may be more expensive than renewables, and if they are unavailable due to maintenance or can’t be turned on in time to meet demand, the utility is forced to buy power on the expensive electricity spot market.
NCAR’s approach to renewable energy forecasting has already been proven to save millions. A wind forecasting system it developed for Xcel in 2010 saved utility customers over $6 million that year by developing 35% more accurate forecasts for wind farm output.
“By creating more detailed and accurate forecasts…we can produce a major return on investment,” said Thomas Bogdan, President of the University Corporation for Atmospheric Research. “This type of cutting-edge research helps make renewable energy more cost competitive.”
, distributed generation
, Energy Storage
, Feature writing
, Gabriel Romero
, National Center for Atmospheric Research
, natural gas
, Pennsylvania State University
, Probabilistic forecasts
, Rooftop solar
, solar panel efficiency
, Solar PV
, Sue Ellen Haupt
, Thomas Bogdan
, University Corporation for Atmospheric Research
, Variational Doppler Radar Analysis System
, Wind ramp event
, Xcel Energy