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.

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 The Power to Change: A Brief Survey of The Wind Power’s Technological and Societal Potential, Barriers to Use, and Ways Forward

2019 ◽  
Vol 1 (1) ◽  
pp. p11
Author(s):  
Helen Kopnina
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Wind Power ◽  
Energy Supply ◽  
Fossil Fuels ◽  
Energy Transition ◽  
Low Carbon ◽  
Close Relationship ◽  
The Government ◽  
Carbon Dependency

With the effects of climate change linked to the use of fossil fuels, as well as the prospect of their eventual depletion, becoming more noticeable, political establishment and society appear ready to switch towards using renewable energy. Solar power and wind power are considered to be the most significant source of global low-carbon energy supply. Wind energy continues to expand as it becomes cheaper and more technologically advanced. Yet, despite these expectations and developments, fossil fuels still comprise nine-tenths of the global commercial energy supply. In this article, the history, technology, and politics involved in the production and barriers to acceptance of wind energy will be explored. The central question is why, despite the problems associated with the use of fossil fuels, carbon dependency has not yet given way to the more ecologically benign forms of energy. Having briefly surveyed some literature on the role of political and corporate stakeholders, as well as theories relating to sociological and psychological factors responsible for the grassroots’ resistance (“not in my backyard” or NIMBYs) to renewable energy, the findings indicate that motivation for opposition to wind power varies. While the grassroots resistance is often fueled by the mistrust of the government, the governments’ reason for resisting renewable energy can be explained by their history of a close relationship with the industrial partners. This article develops an argument that understanding of various motivations for resistance at different stakeholder levels opens up space for better strategies for a successful energy transition.

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 Can Reanalysis Products Outperform Mesoscale Numerical Weather Prediction Models in Modeling the Wind Resource in Simple Terrain?

2021 ◽  
Author(s):  
Vincent Pronk ◽  
Nicola Bodini ◽  
Mike Optis ◽  
Julie K. Lundquist ◽  
Patrick Moriarty ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Stability ◽  
Negative Bias ◽  
Numerical Weather ◽  
Wind Resource ◽  
Mesoscale Numerical Weather Prediction

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at site in Oklahoma, United States, and in a U.S. Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~ −1 m s−1 ) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a slight negative bias (~ −0.5 m s−1 ) offshore and a slight positive bias (~ +0.5 m s−1) at the land-based site. Surprisingly, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED’s higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle both onshore and offshore.

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 Predictability of Rotating Stratified Turbulence

2009 ◽  
Vol 66 (5) ◽  
pp. 1384-1400 ◽  
Author(s):  
K. Ngan ◽  
P. Bartello ◽  
D. N. Straub
Keyword(s):  
Prediction Models ◽  
Isotropic Turbulence ◽  
Weather Prediction ◽  
Boussinesq Equations ◽  
Atmospheric Science ◽  
Stratified Turbulence ◽  
Homogeneous Isotropic Turbulence ◽  
Theoretical Understanding ◽  
Numerical Weather Prediction Models ◽  
Classical Picture

Abstract Although predictability represents one of the fundamental problems in atmospheric science, gaps in our knowledge remain. Theoretical understanding of the inverse error cascade is limited mostly to homogeneous, isotropic turbulence, whereas numerical simulations have focused on highly complex numerical weather prediction models. These results cannot be easily reconciled. This paper describes selected aspects of the predictability behavior of rotating stratified turbulence. The objective is to determine how the predictability varies with scale when the dynamics are more realistic than the idealized models that underlie the classical picture of predictability and yet are free of the parameterizations that complicate interpretation of NWP models. Using a numerical model of the nonhydrostatic Boussinesq equations, it is shown that the predictability decay, as diagnosed by the relative error, is slower for subsynoptic flow. The dependence on the deformation radius, differences between balanced and unbalanced modes, and implications for NWP models are discussed.

scholarly journals An Ensemble Model of Wind Speed Forecasting Based on Variational Mode Decomposition and Bare-Bones Fireworks Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jicheng Quan ◽  
Li Shang
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Feature Reduction ◽  
Partial Least Square ◽  
Ensemble Prediction ◽  
Wind Speed Forecasting ◽  
Proposed Model

Wind energy is one of the fastest growing renewable energy sources. Wind speed forecasting is essential to enhance the utilization of wind energy. Various prediction models have been developed to improve the prediction accuracy of wind speed. However, wind speed time series has nonlinearity, fluctuation, and intermittence, which makes the prediction difficult. Existing prediction models ignore data decomposition and feature reduction and suffer from the deficiency of individual models. This paper proposes a novel ensemble prediction model, which integrates data preprocessing, feature selection, parameter optimization, three intelligent prediction models, and an ensemble strategy. To improve prediction performance, a highly efficient optimization algorithm is applied to determine the individual models’ optimal parameters. Furthermore, partial least square regression is used to calculate combination weight. Additionally, two 10 min datasets from the National Renewable Energy Laboratory (NREL) are employed for one-step-ahead prediction. Among the involved models, the proposed model can obtain the best prediction accuracy. The experimental results indicate that the mean absolute percent errors of the proposed model are 7.97% and 9.99%, which are lower than the comparison methods. Pearson’s test reveals that the proposed approach can have the strongest association between the actual data and the prediction results.

scholarly journals DESERT SOLAR POWER INDIA: A SYSTEMATIC STUDY OF POTENTIAL DEVELOPMENT OF SOLAR POWER IN THE INDIAN DESERT

2021 ◽  
Vol 9 (10) ◽  
pp. 1411-1414
Author(s):  
Kmalesh Kumar ◽  
◽  
Seema Malhotra Baxi ◽  
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Wind Energy ◽  
Solar Power ◽  
Clean Energy ◽  
The State ◽  
Underground Explosion ◽  
Barren Land ◽  
Generating Capacity ◽  
The Government

The deserts of Rajasthan have long been known for their spare beauty and their intense sunshine. Now that sun is being turned into a surge of solar power expansion that may one day power not just Rajasthan but a wide swath of India with clean energy. Rajasthan, with its 300 days a year of sunshine and relatively cheap desert land, has set a goal even more ambitious than Indias. In this years state budget, the newly formed state government announced it hoped to install 25,000 megawatts of solar energy in the state within the next five years, and infrastructure to transmit that power to the national grid. Rajasthan is no newcomer to renewable energy. Since the 1990s, the state has been home to a range of wind energy projects, with about 2,800 megawatts of wind capacity now installed, out of an estimated potential capacity of 5,000 megawatts. Altogether wind power in Rajasthan accounts for about 13 percent of Indias wind energy production. But Rajasthans Great Indian Thar Desert, the test site for Indias first underground explosion of a nuclear weapon 15 years ago, may now help make India a solar power as well. The desert set in Rajasthans largest district Jaisalmer, near the border with Pakistan, it is a place of sand dunes and shrub thickets – but also, increasingly, solar installations that could help change the character of Indias energy development. India committed at the U.N. Framework Convention on Climate Change negotiations in Copenhagen in 2009 to reduce its climate-changing emissions, per unit of GDP, by 20 to 25 percent by 2020, compared to 2005 levels. The country is currently the worlds seventh largest emitter of global warming pollution and the fifth biggest producer of emissions from burning fossil fuels. Sixty-eight percent of those emissions from fossil fuel use come from creating energy for the worlds second most populous country, according to Indias energy ministry. Today the country has 2.28 million megawatts of power generating capacity, and about 12.4 percent of that comes from renewable energy. Of the 2,632 megawatts of solar power now installed in India, Rajasthan so far has only 730 megawatts, putting it in second place behind the state of Gujarat, with 916 megawatts, according to Indias Ministry of New and Renewable Energy. But Rajasthan, Indias largest state and 60 percent covered by sunny desert, is now attracting the worlds interest as a solar hotspot. Around 1 lakh (100,000) square kilometers of barren land is available in the northwest arid belt of the state at cheaper rates that could be utilized for large scale solar projects. The government is formulating the policy to harness the enormous solar potential of the region to meet the countrys growing energy requirements. Besides large solar installations, the government is studying the possibility of grid-connected rooftop solar photovoltaic units for households. The Solar Energy Corporation of India estimates that 130 million homes could potentially be equipped with the units, creating 25,000 megawatts of generating capacity. said Alok, Rajasthans Energy Secretary.

A Wind Energy Ramp Tool and Metric for Measuring the Skill of Numerical Weather Prediction Models

2016 ◽  
Vol 31 (4) ◽  
pp. 1137-1156 ◽  
Author(s):  
Laura Bianco ◽  
Irina V. Djalalova ◽  
James M. Wilczak ◽  
Joel Cline ◽  
Stan Calvert ◽  
...  
Keyword(s):  
Wind Energy ◽  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Model ◽  
Skill Score ◽  
Electric Load ◽  
Ramp Events ◽  
Skill Improvement ◽  
Grid Operation ◽  
Numerical Weather Prediction Models

Abstract A wind energy Ramp Tool and Metric (RT&M) has been developed out of recognition that during significant ramp events (large changes in wind power over short periods of time ) it is more difficult to balance the electric load with power production than during quiescent periods between ramp events. A ramp-specific metric is needed because standard metrics do not give special consideration to ramp events and hence may not provide an appropriate measure of model skill or skill improvement. This RT&M has three components. The first identifies ramp events in the power time series. The second matches in time forecast and observed ramps. The third determines a skill score of the forecast model. This is calculated from a utility operator’s perspective, incorporates phase and duration errors in time as well as power amplitude errors, and recognizes that up and down ramps have different impacts on grid operation. The RT&M integrates skill over a matrix of ramp events of varying amplitudes and durations.

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