Researchers at Lawrence Livermore National Laboratory are hoping to provide wind energy operators detailed information so as to maximise the efficiency of wind farms and the distribution of the power generated therein.
With increased wind energy being poured into the power grid, there is a need to ensure that ramp events – when conditions can change the flow of energy by over 1,000 megawatts (MW) of power – can be predicted and managed.
“We’re trying to forecast wind energy at any given time,” said Chandrika Kamath, the LLNL lead on the project. “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.”
Funded by the Department of Energy’s Office of Energy Efficiency and Renewable Energy, and dubbed WindSENSE, the project is hoping to provide wind energy operators with better wind forecasts so they can manage the load balance.
Kamath used data-mining techniques to determine if weather conditions in regions with wind farms could be effective indicators of days when ramp events are likely to occur. She focused on two separate regions — the Tehachapi Pass in Southern California and the Columbia Basin region on the Oregon-Washington border.
“Our work identified important weather variables associated with ramp events,” Kamath said. “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.”
Need for better forecasts are growing in time with the increase in wind farm output. Wind farms in the Tehachapi Pass currently produce 700 MW of power but are expected to be producing 3,000 MW soon. In 2007, wind farms in the Columbia Basin were producing 700 MW of power, but by 2009 that figure had increased to 3,000 MW.
“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,” said John Zack, director of forecasting of AWS Truepower. “These new software tools have the potential to help forecast providers and users make informed decisions and maximize their weather sensor deployment investment.”
Part of the WindSENSE research was focused on determining the best type of sensors to place in the best location, given that both factors can vary greatly and can create the forecast errors in extreme ramp events.
“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, ” Chandrika said. “Our work is leading to a better understanding of the characteristics and the predictability of the variability associated with wind generation resources.”
Source: Lawrence Livermore National Laboratory
Wind turbines photo by Andrew Clark, via Lawrence Livermore National Laboratory