scholarly journals The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs

2015 ◽  
Vol 96 (10) ◽  
pp. 1699-1718 ◽  
Author(s):  
James Wilczak ◽  
Cathy Finley ◽  
Jeff Freedman ◽  
Joel Cline ◽  
Laura Bianco ◽  
...  
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Prediction Models ◽  
Weather Prediction ◽  
Substantial Reduction ◽  
Energy Resource ◽  
The United States ◽  
Department Of Energy ◽  
Improvement Project ◽  
Forecast Models

Abstract The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

scholarly journals LEVELIZED COST OF ENERGY (LCOE) ANALYSIS OF HEXCRETE WIND TOWERS

2018 ◽  
Author(s):  
Ali Nahvi
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Wind Power ◽  
The United States ◽  
Wind Power Generation ◽  
Department Of Energy ◽  
Energy Output ◽  
Gis Mapping ◽  
Levelized Cost ◽  
Cost Of Energy

Wind power generation has witnessed a dramatic growth in the 21st century. The Department of Energy (DOE) had a vision for wind energy that it would change into an extensively greater part of overall power generation in the U.S. by 2050. As specified by the DOE, wind power generation has grown by trifold from 2008 to 2013. This study presents a constructible, financially feasible alternative wind tower design to the 80 m steel tower platform which has the potential to decrease the overall Levelized cost of energy (LCOE). A hexagonal concrete wind tower solution is evaluated to facilitate the fabrication of a taller wind turbine generator to harvest more powerful, stable, and frequent wind resources for elevating wind energy production to cut down the overall LCOE. Subject matter experts from the industry were benefitted from to develop a process and estimate the cost and schedule of development and assembly of this process. To mitigate uncertainties and quantify risks, a sensitivity analysis was carried out on cost and schedule estimates. Also, estimating LCOE of wind towers is a primary requirement for efficient assimilation of wind power generation in the electricity market. In the state of Iowa, wind power is rapidly becoming a significant electricity generator. Unpredictable outputs and different options for deploying wind towers are one of the major problems of power system operators. Good estimation tools are important and will be needed to integrate wind energy into the economic power plant. The other objective of this research is to propose a GIS-based map to visualize LCOE of different wind tower construction options in various locations. Therefore, wind speed GIS mapping by using weather information will be crucial. Calculation of energy output by applying wind gradient formula to wind speeds energy are performed. The research concludes of Hexcrete towers can be achieved by use of the 120m and 140 m Hexcrete tower platform on certain wind sites in the United States.

Forecasting the Wind to Reach Significant Penetration Levels of Wind Energy

2011 ◽  
Vol 92 (9) ◽  
pp. 1159-1171 ◽  
Author(s):  
Melinda Marquis ◽  
Jim Wilczak ◽  
Mark Ahlstrom ◽  
Justin Sharp ◽  
Andrew Stern ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Science ◽  
Time Frame ◽  
Department Of Energy ◽  
Power Prediction ◽  
Financial Benefit ◽  
The Government

Advances in atmospheric science are critical to increased deployment of variable renewable energy (VRE) sources. For VRE sources, such as wind and solar, to reach high penetration levels in the nation's electric grid, electric system operators and VRE operators need better atmospheric observations, models, and forecasts. Improved meteorological observations through a deep layer of the atmosphere are needed for assimilation into numerical weather prediction (NWP) models. The need for improved operational NWP forecasts that can be used as inputs to power prediction models in the 0–36-h time frame is particularly urgent and more accurate predictions of rapid changes in VRE generation (ramp events) in the very short range (0–6 h) are crucial. We describe several recent studies that investigate the feasibility of generating 20% or more of the nation's electricity from weather-dependent VRE. Next, we describe key advances in atmospheric science needed for effective development of wind energy and approaches to achieving these improvements. The financial benefit to the nation of improved wind forecasts is potentially in the billions of dollars per year. Obtaining the necessary meteorological and climatological observations and predictions is a major undertaking, requiring collaboration from the government, private, and academic sectors. We describe a field project that will begin in 2011 to improve short-term wind forecasts, which demonstrates such a collaboration, and which falls under a recent memorandum of understanding between the Office of Energy Efficiency and Renewable Energy at the Department of Energy and the Department of Commerce/National Oceanic and Atmospheric Administration.

Wind Resource Assessment Using a Mesoscale Model: The Effect of Horizontal Resolution

2012 ◽  
Author(s):  
S. Jafari ◽  
T. Sommer ◽  
N. Chokani ◽  
R. S. Abhari
Keyword(s):  
Wind Energy ◽  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Energy Resource ◽  
Resource Assessment ◽  
Land Area ◽  
Wind Resource Assessment ◽  
Wind Resource ◽  
Horizontal Grid

Prospecting for wind farm sites and pre-development studies of wind energy projects require knowledge of the wind energy resource over large areas (that is, areas of the order of 10’000 km2 and greater). One approach to detail this wind resource is the use of mesoscale numerical weather prediction models. In this paper, the mesoscale Weather Research and Forecasting (WRF) model is used to examine the effect of horizontal grid resolution on the fidelity of the predictions of the wind resource. The simulations are made for three test cases, Switzerland (land area 39’770 km2), Iowa (land area 145,743 km2) and Oregon (land area 248’647 km2), representing a range of terrain types, from complex terrain to flat terrain, over the period from 2006–2010. On the basis of comparisons to the data from meteorological masts and tall communication towers, guidelines are given for the horizontal grid required in the use of mesoscale models of large area wind resource assessment, especially over complex terrain.

scholarly journals The Second Wind Forecast Improvement Project (WFIP2): Observational Field Campaign

2019 ◽  
Vol 100 (9) ◽  
pp. 1701-1723 ◽  
Author(s):  
James M. Wilczak ◽  
Mark Stoelinga ◽  
Larry K. Berg ◽  
Justin Sharp ◽  
Caroline Draxl ◽  
...  
Keyword(s):  
Wind Energy ◽  
Pacific Northwest ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Grid Cell ◽  
Department Of Energy ◽  
Improvement Project ◽  
Field Campaign ◽  
Energy Applications ◽  
Nwp Model

AbstractThe Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.

scholarly journals The Second Wind Forecast Improvement Project (WFIP2): General Overview

2019 ◽  
Vol 100 (9) ◽  
pp. 1687-1699 ◽  
Author(s):  
William J. Shaw ◽  
Larry K. Berg ◽  
Joel Cline ◽  
Caroline Draxl ◽  
Irina Djalalova ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Integrated Approach ◽  
Department Of Energy ◽  
Scale Model ◽  
Improvement Project ◽  
Mesoscale Models ◽  
Operational Forecasts ◽  
Forecast Models

AbstractIn 2015 the U.S. Department of Energy (DOE) initiated a 4-yr study, the Second Wind Forecast Improvement Project (WFIP2), to improve the representation of boundary layer physics and related processes in mesoscale models for better treatment of scales applicable to wind and wind power forecasts. This goal challenges numerical weather prediction (NWP) models in complex terrain in large part because of inherent assumptions underlying their boundary layer parameterizations. The WFIP2 effort involved the wind industry, universities, the National Oceanographic and Atmospheric Administration (NOAA), and the DOE’s national laboratories in an integrated observational and modeling study. Observations spanned 18 months to assure a full annual cycle of continuously recorded observations from remote sensing and in situ measurement systems. The study area comprised the Columbia basin of eastern Washington and Oregon, containing more than 6 GW of installed wind capacity. Nests of observational systems captured important atmospheric scales from mesoscale to NWP subgrid scale. Model improvements targeted NOAA’s High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to National Weather Service (NWS) operational forecast models, and these modifications have already yielded quantitative improvements for the short-term operational forecasts. This paper describes the general WFIP2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP2 observational science, modeling challenges and solutions, incorporation of forecasting uncertainty into decision support tools for the wind industry, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

Methods for Improvement of Drift Forecast Models

2010 ◽  
Author(s):  
Christophe Maisondieu ◽  
O̸yvind Breivik ◽  
Jens-Christian Roth ◽  
Arthur A. Allen ◽  
Bertrand Forest ◽  
...  
Keyword(s):  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
The United States ◽  
Search And Rescue ◽  
Environmental Data ◽  
Fishing Vessels ◽  
Forecast Models ◽  
Definition Of

Over the past decades, various operational drift forecast models were developed for trajectory prediction of objects lost at sea for search and rescue operations. Most of these models are now based on a stochastic, Monte Carlo definition of the object’s initial position and its time-evolving search area through computation of an ensemble of equally probable trajectories (Breivik [1]). Uncertainties in environmental forcing, mainly surface currents and wind, as well as the uncertainties inherent in the simplified computation of leeway speed and direction relative to the wind are also accounted for through this ensemble-based approach. Accuracy of the drift forecast obviously depends to a large extent on the quality of the environmental forecast data provided by numerical weather prediction models and ocean models, but it also depends on the level of uncertainty associated with the estimation of the drift properties (leeway) of the objects themselves. The present work mostly focuses on this second aspect of the problem. Drift properties of objects can be described by means of their downwind and crosswind leeway coefficients, according to the definition of leeway as stated by Allen [2, 3]. Assessment of the leeway coefficients is based on a direct method, which requires measurements acquired during field tests. Such field experiments basically entail deploying one or more objects at sea and simultaneously recording the environmental parameters (namely wind speed and motion of the object relative to the ambient water masses, i.e., its leeway) as well as the object’s position while adrift for periods ranging from several hours to several days. Using this method, a large database providing leeway coefficients for more than sixty object classes ranging from medical waste to a person-in-water to small fishing vessels was compiled over the years by the United States Coast Guard (Allen [2]). More recently additional trials were conducted, which allowed evaluation of new objects, including 20-ft shipping containers. We present in this paper the methods and analysis procedures for field determination of leeway coefficients of typical search-and-rescue objects. As an example we present the case study of a 20-ft container and discuss results obtained from a drift forecast model assessing sensitivity of such a model to the quality of environmental data as well as uncertainty levels of some reference parameters.

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals The National Earth System Prediction Capability: Coordinating the Giant

2017 ◽  
Vol 98 (2) ◽  
pp. 239-252 ◽  
Author(s):  
Jessie C. Carman ◽  
Daniel P. Eleuterio ◽  
Timothy C. Gallaudet ◽  
Gerald L. Geernaert ◽  
Patrick A. Harr ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Department Of Energy ◽  
Earth System ◽  
Numerical Weather ◽  
Interagency Coordination ◽  
Research Technology ◽  
Prediction Capability ◽  
The U.S

Abstract The United States has had three operational numerical weather prediction centers since the Joint Numerical Weather Prediction Unit was closed in 1958. This led to separate paths for U.S. numerical weather prediction, research, technology, and operations, resulting in multiple community calls for better coordination. Since 2006, the three operational organizations—the U.S. Air Force, the U.S. Navy, and the National Weather Service—and, more recently, the Department of Energy, the National Aeronautics and Space Administration, the National Science Foundation, and the National Oceanic and Atmospheric Administration/Office of Oceanic and Atmospheric Research, have been working to increase coordination. This increasingly successful effort has resulted in the establishment of a National Earth System Prediction Capability (National ESPC) office with responsibility to further interagency coordination and collaboration. It has also resulted in sharing of data through an operational global ensemble, common software standards, and model components among the agencies. This article discusses the drivers, the progress, and the future of interagency collaboration.

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

scholarly journals The Incentives of Wind Power Production and Integration in US Policy

2014 ◽  
Vol 21 ◽  
pp. 78
Author(s):  
Samuel S. Webster
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Department Of Energy ◽  
Tax Credit ◽  
Wind Energy Generation ◽  
Production Tax Credit ◽  
The Us ◽  
Production Tax ◽  
The Impact ◽  
Wind Power Development

This paper analyzes the impact of the federal Production Tax Credit on the development of wind energy in the US. Following an analysis of the incentives these policies produce for wind energy generation and integration, this paper finds that, although the Production Tax Credit has proven effective at promoting some level of wind power development, the effectiveness of the Production Tax Credit varies by region and by itself is unlikely to achieve the deep levels of wind power penetration desired by some policymakers and the U.S. Department of Energy.

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