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Final Report on California Regional Wind Energy Forecasting Project
Author: H. S. Chin
Publisher:
Published: 2005
Total Pages: 17
ISBN-13:
DOWNLOAD EBOOK →Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather forecasting has been recognized as an important factor to further improve the wind energy forecast for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather forecasting capability to develop an improved regional wind energy forecasting system. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and direction, using its wind tunnel facility at the windmill farm at the Altamont Pass. The main objective of LLNL's involvement is to provide UC-Davis with improved wind forecasts to drive the parameterization scheme of turbine power curves developed from the wind tunnel facility. Another objective of LLNL's effort is to support the windmill farm operation with real-time wind forecasts for the effective energy management. The forecast skill in capturing the situation to meet the cut-in and cutout speed of given turbines would help reduce the operation cost in low and strong wind scenarios, respectively. The main focus of this report is to evaluate the wind forecast errors of LLNL's three-dimensional real-time weather forecast model at the location with the complex terrain. The assessment of weather forecast accuracy would help quantify the source of wind energy forecast errors from the atmospheric forecast model and/or wind-tunnel module for further improvement in the wind energy forecasting system.
Network and Communication Technology Innovations for Web and IT Advancement
Author: Alkhatib, Ghazi I.
Publisher: IGI Global
Published: 2012-10-31
Total Pages: 455
ISBN-13: 1466621583
DOWNLOAD EBOOK →With the steady stream of new web based information technologies being introduced to organizations, the need for network and communication technologies to provide an easy integration of knowledge and information sharing is essential. Network and Communication Technology Innovations for Web and IT Advancement presents studies on trends, developments, and methods on information technology advancements through network and communication technology. This collection brings together integrated approaches for communication technology and usage for web and IT advancements.
Wind Energy Forecasting of a Wind Turbine Farm in the Altamont Pass Through Wind Tunnel Modeling
Emerging Capabilities and Applications of Artificial Higher Order Neural Networks
Author: Zhang, Ming
Publisher: IGI Global
Published: 2021-02-05
Total Pages: 540
ISBN-13: 1799835650
DOWNLOAD EBOOK →Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields. Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.
Operational Forecasting Based on a Modified Weather Research and Forecasting Model
Author:
Publisher:
Published: 2010
Total Pages: 8
ISBN-13:
DOWNLOAD EBOOK →Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.
Review of Wind Energy Forecasting Methods for Modeling Ramping Events
Author:
Publisher:
Published: 2011
Total Pages: 77
ISBN-13:
DOWNLOAD EBOOK →Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF. WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.
Improving Short-term Wind Power Forecasting Through Measurements and Modeling of the Tehachapi Wind Resource Area
Dissertation Abstracts International
Integration of Wind Generation Forecasts Into Power Systems Operation
Author: Phillip De Mello
Publisher:
Published: 2012
Total Pages:
ISBN-13: 9781267970299
DOWNLOAD EBOOK →Increasing wind power generation has lead to an increase in concern over the uncertainty and variability of wind generation as it interacts with the powers system. This study compares previous efforts to study wind integration and presents a new methodology to enhance evaluation of wind power integration and quantify wind forecast impacts. The methodology involves a three step production cost model simulation to simulate day ahead, hour ahead, and real time power systems operations using hourly, fifteen minute, and five minute time steps. A full nodal network model is used in all simulations to ensure consistency between the different steps of the simulations. Wind generation forecast are analyzed and modeled in the simulations, to determine their effect on operations. The methodology is used to analyze several case studies of the California power system. The case studies model California using additional wind generation to meet a 20% renewable energy requirement. The model simulates the four major wind regions in California. The Tehachapi wind region is modeled with 4.7 GW of additional wind generation capacity, for a total of 7.6 GW wind capacity in the system. The results show that wind forecasts can have significant impacts on the physical and financial aspects of the power systems. The wind forecast errors impact the unit commitments, generator dispatches, and load following ability. Forecast errors cause day ahead thermal generation commitments to vary by 1.2 GW of capacity. The generator revenue and load costs see impacts of 3% and 1%, respectively, for most common forecast errors.
