The U.S., along with many foreign nations, is turning increasingly to the power of the wind to meet the rapidly growing demand for clean energy. National security in its fullest meaning must inevitably include energy and environmental security. We must reduce our dependence on imported fossil fuels while ensuring plentiful clean energy with renewable sources. The wind, however, is an intermittent resource that is challenging to predict, sometimes varying significantly from minute to minute. What’s more, complex atmospheric factors, such as turbulence, and topographical features, such as hills, modify the wind speed and direction and hence the power that can be extracted by wind turbines. Turbulence also plays an important role in the reliability and life span of turbine components.
As the article Predicting Wind Power with Greater Accuracy describes, Lawrence Livermore scientists and engineers have launched a broad effort to enhance the accuracy of wind power predictions and thereby strengthen America’s supply of clean energy. Reducing the uncertainty in wind power forecasts is essential for optimizing power production from wind farms and sustaining the impressive growth of wind energy production in the U.S.
Improving the accuracy of existing wind power models will enable wind farm operators to supply more of the available power to a utility on any given day, improving a utility’s capacity factor (the ratio of actual to maximum potential output). Better forecasting and lower uncertainty of wind farm power production also provide an economic benefit to operators, the utilities, and consumers by lowering the cost of energy and enhancing operating profits.
For their part, electric power grid operators need more accurate estimates of power production from wind farms to better match supply with projected customer demand. Every day, utilities depend on a mix of electrical energy sources: traditional baseload (natural gas, nuclear, coal, and geothermal); renewable (primarily wind and solar); storage (hydropower); and standby natural gas plants. With greater certainty of how much power they will receive from wind farms for that day, grid operators will have a better picture for how much they need to tap other energy sources.
To enhance the predictive accuracy of wind power forecasts, Laboratory researchers combine fieldwork, computer simulation, and data analysis. With high-performance computing, a Livermore core competency, we are modeling wind flow and all its perturbations, including turbulence. Our capability in this area has benefited from decades of experience operating the National Atmospheric Release Advisory Center. This facility, which helped pioneer atmospheric modeling, combines meteorological forecasts and atmospheric dispersion models to predict the probable spread of hazardous material released into the atmosphere and its flow over complex terrain. Wind farms, especially those in the western U.S., are often situated in complex terrains, and models must account for how topography affects the wind.
The simulation challenge can be extraordinarily complicated. For example, simulating the fluctuating power production of an entire wind farm comprising more than 100 turbines requires use of Livermore’s massively parallel supercomputers. In addition, the resolved length scales in wind simulations can range from millimeters in the rotor-blade boundary layer to 100 kilometers for large atmospheric weather patterns.
Because we need to validate our simulations, field teams use lidars and other meteorological instruments to collect atmospheric data and measure wind profiles and turbulence blowing into wind farms. We compare those data with the power produced from the turbines during that same time interval to refine power curve models supplied by turbine manufacturers. Refining the power curves can help us more accurately predict power output for a given set of atmospheric conditions.
In our effort to reduce the uncertainty of wind forecasting, we have leveraged expertise originally developed in our nuclear weapons program, which made significant advances in so-called uncertainty quantification, or UQ. By applying UQ, we have identified and narrowed the uncertainties associated with collected field data and with results from various simulation codes.
Wind power is only one component of Livermore’s renewable energy portfolio. Our geothermal energy research includes studying where to develop additional reservoirs and how to optimize those reservoirs. We’re also investigating new materials for enhanced photovoltaic solar cells and developing better storage technology for hydrogen-powered vehicles. Finally, as more solar and wind resources are added to the grid, we’re working with the California Public Utilities Commission to integrate intermittent renewable energy into the electric power grid and help the state prepare for more complex load-balancing situations.
With a full complement of projects, we’re working hard to enhance the nation’s energy security with a robust mix of renewable and sustainable energy options.