The world is continuing to use more wind energy, but its capriciousness can make it burdensome to equalize the load and generation.
However, a project done by researchers working together for the Lawrence Livermore National Laboratory and AWS Truepower may rectify this situation by warning control room workers of upcoming wind conditions and anticipated energy usage to help them make more accurate schedule arrangements. This is of particular importance in events such as steep increases or drops in the wind’s speed, called a ramp event, within a short period of time which will result in a proportionate incline or decline of a wind turbine’s electricity production.
Dr. Chandrika Kamath, the team’s project leader for the Lawrence Livermore National Laboratory, states that, “We’re trying to forecast wind energy at any given time. One of our goals is to help the people in the control room at the utilities determine when ramp events may occur and how that will affect the power generation from a particular wind farm.”
The United States Department of Energy’s Office of Energy Efficiency and Renewable Energy has nicknamed this endeavor WindSENSE.
In order to better comprehend the nature of these ramp events, Dr. Kamath has resorted to data mining in the hopes of establishing whether the wind conditions in areas with wind farms could effectively predict the days where there will be a high likelihood of a ramp event. Data on weather and wind energy from two separate regions was being used.: the Columbia Basin on the border of Washington state and Oregon, as well as southern California’s Tehachapi Pass.
Dr. Kamath states that, “Our work identified important weather variables associated with ramp events. This information could be used by the schedulers to reduce the number of data streams they need to monitor when they schedule wind energy on the power grid.”
She went on to emphasize the importance of having the most accurate wind speed predictions possible as wind energy continues to contribute more electricity to the world’s demanding power grids.
In the Tehachapi Pass of southern California, wind turbines generate 700 MW (megawatts) of electricity and will shortly be generating as much as 3,000 MW. The Columbia Basin’s wind farms generated 700MW in 2007, but were generating a shocking 3,000 MW by 2009. It is essential for wind speed predictions to be as precise as possible, particularly when ramp events may alter power generation by as much as 1,000 MW in as little as a single hour.
John Zack, AWS Truepower’s Director of Forecasting, stays that, “The observation targeting research conducted as part of the WindSENSE project resulted in the development and testing of algorithms to provide guidance on where to gather data to improve wind forecast performance. These new software tools have the potential to help forecast providers and users make informed decisions and maximize their weather sensor deployment investment.”
Utility companies use wind generation predictions based upon computer simulations which are fueled by observational information that have been programmed into the simulator’s time progression. Data regarding particular variables in some places are better than others at decreasing the number of prediction miscalculations in the event of extremes, where the even takes place and the forecast period.
Determining which places and sensor types would have the greatest benefit for both extreme event and short term wind forecasts was a large part of WindSENSE’s endeavor.
Dr. Kamath said, “We’re trying to reduce the barriers to integrating wind energy on the grid by analyzing historical data and identifying the new data we should collect so we can improve the decision making by the control room operators. Our work is leading to a better understanding of the characteristics and the predictability of the variability associated with wind generation resources.”