New model developments could lead to improved wind power forecasts
Imagine cold air building up inside a valley or other topographic basin, like a cold swimming pool in the atmosphere. Known in meteorology as cold-air pools, or simply cold pools, these events affect wind plant power production in two key ways. Because they are associated with stagnant air and low winds near the ground, cold pools can severely limit wind energy output over periods of hours to days. Then, when cold pools dissolve, known as cold pool “erosion,” increased wind speeds allow wind power production to recover.
“Accurate forecasts of cold pools and other weather events are vital to the success of the wind energy industry because they allow wind plant owners to plan for future energy production and grid operators to improve reliability,” said Robert Arthur, a researcher at Lawrence Livermore National Laboratory who has previously investigated the effect of frontal passages on wind power plants.
However, cold pools are notoriously difficult to predict. They have challenged weather forecasters for decades, even prior to the advent of modern wind energy, because of the problems they cause in other sectors. For example, cold pools can trap pollution, leading to poor air quality in urban areas. They are also associated with fog and freezing rain, creating risks for air and ground transportation.
Weather models have difficulty forecasting the cold, near-ground temperatures found within cold pools. Because of this, models show high winds from aloft penetrating closer to the surface than they really do during cold pool events. This model error leads to an overprediction of wind speed in the wind turbine rotor region (roughly 50–150 meters above ground for a wind turbine with a hub height of 100 meters). A related issue causes cold pools to erode too early in models, causing the models to incorrectly predict the timing of the increase in wind speed following the event.
“These errors are subtle but can have a large impact on both the magnitude and timing of wind power predictions,” Arthur said.
To address this forecasting need, Arthur and collaborators at Lawrence Livermore National Laboratory and the National Oceanic and Atmospheric Administration (NOAA) implemented a method that better predicts large temperature changes in regions of steep terrain, where cold pools generally form. In an article published in the journal Monthly Weather Review, the improved temperature prediction method was shown to reduce wind speed errors by as much as 20% when compared to a standard modeling approach.
Development and testing of the method were completed as part of the Second Wind Forecast Improvement Project (WFIP2), a large, collaborative effort to improve weather models for use in the wind energy industry. Part of DOE’s Atmosphere to Electrons initiative, WFIP2 also included an extensive campaign to gather meteorological observations in the Columbia River Basin of the U.S. Pacific Northwest. Arthur and his colleagues focused on a January 2017 cold pool event, observed during WFIP2, to validate their method. The team continues to work actively on this case study, examining important cold pool features, such as turbulence and low-level clouds.
“Improvement in cold pool forecasting, particularly cold pool erosion, was one of the big successes of WFIP2,” said lead investigator Will Shaw of PNNL.
In fact, many previous WFIP2 model updates have been incorporated into NOAA’s High Resolution Rapid Refresh model, a weather forecast model that is used throughout the wind energy industry. According to Dave Turner, a researcher at NOAA and head of its Atmospheric Science for Renewable Energy Program, the latest operational version of the model does a markedly better job forecasting the evolution of cold pools than its predecessors.
Arthur’s new modeling method for steep terrain combines with other model developments in wind energy R&D to improve wind resource forecasting and, therefore, the reliability of energy production from such forecasts.
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