DOE OSTI2011
Renewable resources, such as wind and solar, are providing an increasingly larger percentage of our energy needs. To successfully integrate these intermittent resources into the power grid while maintaining its reliability, we need to better understand the characteristics and predictability of the variability associated with these power generation resources. WindSENSE, a three year project at Lawrence Livermore National Laboratory, considered the problem of scheduling wind energy on the grid from the viewpoint of the control room operator. Our interviews with operators at Bonneville Power Administration (BPA), Southern California Edison (SCE), and California Independent System Operator (CaISO), indicated several challenges to integrating wind power generation into the grid. As the percentage of installed wind power has increased, the variable nature of the generation has become a problem. For example, in the Bonneville Power Administration (BPA) balancing area, the installed wind capacity has increased from 700 MW in 2006-2007 to over 1300 MW in 2008 and more than 2600 MW in 2009. To determine the amount of energy to schedule for the hours ahead, operators typically use 0-6 hour ahead forecasts, along with the actual generation in the previous hours and days. These forecasts are obtained from numerical weather prediction (NWP) simulations or based on recent trends in wind speed in the vicinity of the wind farms. However, as the wind speed can be difficult to predict, especially in a region with complex terrain, the forecasts can be inaccurate. Complicating matters are ramp events, where the generation suddenly increases or decreases by a large amount in a short time (Figure 1, right panel). These events are challenging to predict, and given their short duration, make it difficult to keep the load and the generation balanced. Our conversations with BPA, SCE, and CaISO indicated that control room operators would like (1) more accurate wind power generation forecasts for use in scheduling and (2) additional information that can be exploited when the forecasts do not match the actual generation. To achieve this, WindSENSE had two areas of focus: (1) analysis of historical data for better insights, and (2) observation targeting for improved forecasts. The goal was to provide control room operators with an awareness of wind conditions and energy forecasts so they can make well-informed scheduling decisions, especially in the case of extreme events such as ramps.