• Energy Research

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

Abstract. With the planned construction of vast offshore wind farms along the US East Coast, identifying and understanding key coastal processes, such as sea breezes, has become a critical need for the sustainability and development of US offshore wind energy. In this study, a new two-step identification method is proposed to detect and characterize three types of sea breezes (pure, corkscrew and backdoor) over the US northeastern coast from a year-long WRF (Weather Research and Forecasting) simulation. The results suggest that the proposed detection method can identify the three different types of sea breezes in the model simulation. Key sea breeze features, such as the calm zone associated with pure sea breezes and coastal jets associated with corkscrew sea breezes, are evident in the sea breeze composite imagery. In addition, the simulated sea breeze events indicate a seasonal transition from pure to corkscrew sea breeze between March and August as the land–sea thermal contrast increases. Furthermore, the location and extension of the sea breeze front are different for each type of sea breeze, suggesting that the coastal impact of sea breeze varies with sea breeze type. From the wind energy perspective, the power production associated with a 10 MW offshore wind turbine would be approximately 3 to 4 times larger during a corkscrew sea breeze event than the other two types of sea breezes. This highlights the importance of identifying the correct type of sea breeze in numerical weather/wind energy forecasting.

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. The effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

The U.S. Department of Energy's (DOE) National Renewable Energy Laboratory (NREL), under an interagency agreement with the Bureau of Ocean Energy Management (BOEM), is providing technical assistance to identify and delineate leasing areas for offshore wind energy development within the Atlantic Coast Wind Energy Areas (WEAs) established by BOEM. This report focuses on NREL's development of three delineated leasing area options for the Massachusetts (MA) WEA and the technical evaluation of these leasing areas. The overarching objective of this study is to develop a logical process by which the MA WEA can be subdivided into non-overlapping leasing areas for BOEM's use in developing an auction process in a renewable energy lease sale. NREL worked with BOEM to identify an appropriate number of leasing areas and proposed three delineation alternatives within the MA WEA based on the boundaries announced in May 2012. A primary output of the interagency agreement is this report, which documents the methodology, including key variables and assumptions, by which the leasing areas were identified and delineated.

The value of day-ahead solar power forecasting improvements was analyzed by simulating the operation of the Independent System Operator – New England (ISO-NE) power system under a range of scenarios with varying solar power penetrations and solar power forecasting improvements. The results showed how the integration of solar power decreased operational electricity generation costs, by decreasing fuel and variable operation and maintenance costs, while decreasing start and shutdown costs of fossil fueled conventional generators. Solar power forecasting improvements changed the impacts that the uncertainty of solar power has on bulk power system operations; electricity generation from the fast start and lower efficiency power plants, ramping of all generators, start and shutdown costs, and solar power curtailment were all reduced. These impacts led to a reduction in overall operational electricity generation costs in the system that translates into an annual economic value for improving solar power forecasting.

Abstract. The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models for the use case of wind energy development and operation. Several coupling methods and techniques for generating turbulence at the microscale that is subgrid to the mesoscale have been evaluated for a variety of cases. Case studies included flat terrain, complex terrain, and offshore environments. Methods were developed to bridge the terra incognita, that scale from about 100 m through the depth of the boundary layer. The team used wind-relevant metrics and archived code, case information, and assessment tools and are making those widely available. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy.

Abstract. Mountains can modify the weather downstream of the terrain. In particular, when stably stratified air ascends a mountain barrier, buoyancy perturbations develop. These perturbations can trigger mountain waves downstream of the mountains that can reach deep into the atmospheric boundary layer where wind turbines operate. Several such cases of mountain waves occurred during the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River basin in the lee of the Cascade Range bounding the states of Washington and Oregon in the Pacific Northwest of the United States. Signals from the mountain waves appear in boundary layer sodar and lidar observations as well as in nacelle wind speeds and power observations from wind plants. Weather Research and Forecasting (WRF) model simulations also produce mountain waves and are compared to satellite, lidar, and sodar observations. Simulated mountain wave wavelengths and wave propagation speeds (group velocities) are analyzed using the fast Fourier transform. We found that not all mountain waves exhibit the same speed and conclude that the speed of propagation, magnitudes of wind speeds, or wavelengths are important parameters for forecasters to recognize the risk for mountain waves and associated large drops or surges in power. When analyzing wind farm power output and nacelle wind speeds, we found that even small oscillations in wind speed caused by mountain waves can induce oscillations between full-rated power of a wind farm and half of the power output, depending on the position of the mountain wave's crests and troughs. For the wind plant analyzed in this paper, mountain-wave-induced fluctuations translate to approximately 11 % of the total wind farm output being influenced by mountain waves. Oscillations in measured wind speeds agree well with WRF simulations in timing and magnitude. We conclude that mountain waves can impact wind turbine and wind farm power output and, therefore, should be considered in complex terrain when designing, building, and forecasting for wind farms.