Grantee Research Project Results
Final Report: Catching the Wind: A Low Cost Method for Wind Power Site Assessment
EPA Grant Number: SU833530Title: Catching the Wind: A Low Cost Method for Wind Power Site Assessment
Investigators: Jacobson, Arne E. , Allen, Andrea , Bracken, Cameron , Edward, Charles , Sheppard, Colin , Benzonelli, Heidi , Apple, James , Bohn, Juliette , Radecsky, Kristen , Johnstone, Peter , Deshmukh, Ranjit
Institution: Humboldt State University
EPA Project Officer: Page, Angela
Phase: I
Project Period: September 30, 2007 through May 30, 2008
Project Amount: $10,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet (2007) RFA Text | Recipients Lists
Research Category: Pollution Prevention/Sustainable Development , P3 Challenge Area - Air Quality , P3 Awards , Sustainable and Healthy Communities
Objective:
The use of wind power can reduce demand for fossil fuels, decrease local air and water pollution, and reduce greenhouse gas emissions. Humboldt County has an appreciable wind resource and great potential for wind development on both the small and large scale. For large wind farms, site assessment is a small portion of the overall cost, however, the small to mid-sized wind turbine market is hindered by a lack of accurate, low cost wind resource data. Current practices require at least one year of measured wind data or the use of expensive software to estimate a site’s resource. Our Phase I response to this challenge was to purchase wind monitoring equipment, which can be loaned to local community members at low cost. We developed and evaluated several measurecorrelate- predict methods that will reduce the length of time needed to make accurate wind resource assessments. Finally, we produced an open source application, the Statistical Wind Energy Estimation Tool (SWEET), which provides an interface for the correlation and prediction methods.
Our Phase II proposal builds upon our successes from Phase I and responds to the challenges we encountered. We propose the implementation of a community-based anemometer loan program, using a mobile wind monitoring station. We will use the traditional tilt-up tower installed in Phase I as a local source of high quality, long term wind data. We will further develop SWEET into a multiuser, publicly available, open source tool for wind site assessment.
Summary/Accomplishments (Outputs/Outcomes):
Our Phase I successes involve the installation of a wind monitoring station in Humboldt County, the evaluation of four different measure-correlate-predict methods for wind site assessment, and the creation of SWEET, an open source software package implementing the prediction methods.
Tower Installation
After ten months of planning, site preparation, and construction, the Renewable Energy Student Union (RESU) successfully installed an 80ft wind monitoring station on a ridge east of Humboldt Bay. This site is in a region with a high potential for wind power development. Based on the data collected at the site, as much as 4,230 lbs CO2/year could be displaced if the landowners choose to install wind turbines.
From the installation, we learned that the building codes in Humboldt County are rigorously defined and strictly enforced. To satisfy these requirements, we paid for a full structural analysis of our traditional tilt-up tower, as well as 5 cubic yards of concrete to anchor the tower. Ultimately, the labor and cost of materials and services necessary for permitting our tower amounted to 250 person hours and $2700, which is $2000 more than we anticipated.
Comparison of Measure-Correlate-Predict Methods
Despite the difficulties we encountered installing the monitoring tower, we achieved notable success in our implementation of a software tool to perform the correlation and prediction. Four statistical correlation methods were utilized and their predictive capabilities were evaluated using a variety of data sets. These methods are referred to as the Variance Ratio Method, Mortimer’s Method, the Multi-model, and Artificial Neural Network. The four methods were also compared against a “no correlation” control scenario.
Overall, the Variance Ratio method produced the most reliable predictions, with a standard error of 2%, in the estimation of energy production at a given site. It has the additional benefit of being simple to use. The Multi-Model also performed well, but the model is much more complex and time intensive to use. Mortimer’s method systematically overestimated average wind speed and the Neural Network was the least consistent of any of the methods.
We were able to make predictions that substantially improved upon the no correlation method, using “well behaved” data from buoys on the Pacific Coast and monitoring stations in North Dakota. These data sets are considered “well behaved” because they come from high quality monitoring stations in locations with little obstruction from topography or terrain roughness, namely, the Pacific Ocean and the great plains of North Dakota. The performance of the statistical methods demonstrates that our methodology is valuable in assessing wind power potential based on a small period of monitoring.
However, due to the relatively low quality sources of data in Humboldt County, the correlate and predict methods did not improve upon the no correlation method. We believe a long term source of high quality data is therefore needed to apply the methods locally.
Software Tool
We have created a web-based software application, SWEET, which uses statistical methods to predict long term wind behavior at a potential wind site, based on historical data from nearby weather stations. Currently, SWEET is a prototype application based on open source programming languages.
Conclusions:
The cost and labor involved with permitting a traditional wind monitoring tower is substantial. Fortunately, this challenge can be overcome. In Humboldt County, a tower designed for mobility would qualify as a temporary mobile tower installation and would be exempt from building and planning permits. Such a mobile wind monitoring station would eliminate the cost of the permit itself, the need to commission a structural engineer, the need to use concrete anchors in certain soils, as well as the time and labor necessary to accomplish the aforementioned tasks. Because the sources of long-term wind data in Humboldt County are not sufficient for employing the studied statistical methods, we recommend the establishment of a permanent source of high quality terrestrial wind data. The tower we installed in Kneeland, CA is a likely candidate for such a station.
SWEET has the potential to shorten the monitoring time necessary for anyone collecting sitespecific wind speed data located near a weather station. The tool will be accessible via the Web and will initially be free to use. To prepare the tool for public use the following features should be developed: 1) multi-user capability, 2) improved usability, 3) further verification of the methods, and 4) documentation.
Proposed Phase II Objectives and Strategies
Our challenge for Phase II is to implement a community-based anemometer loan program, as well as to further develop our open-source predictive software application. The anemometer loan program will promote wind development in Humboldt County and the software will promote development worldwide.
Based on our Phase I experience, we have identified four Phase II priorities:
- Acquire a mobile wind monitoring station
- Establish a long term source of high quality terrestrial wind data in Humboldt County
- Develop a community-based anemometer loan program
- Develop SWEET into a full featured, multi-user, publicly available tool
A tower designed for mobility eliminates the need for a building permit and significantly reduces the cost and labor necessary to monitor at a new site. We estimate that the net life cycle benefit of using a mobile tower versus a traditional tower is $23,000.
In order to use the statistical methods implemented in SWEET along with our mobile wind monitoring tower, we propose to use our recently installed monitoring station in Kneeland, CA as a long term source of high quality data.
Along with our community partner, the Redwood Alliance, we will help implement a communitybased anemometer loan program. This program will manage the outreach and selection process, and will organize tasks involved with scheduling, tower installation, and maintenance.
Finally, investment in professional software development is necessary for SWEET to achieve its full potential as a time-saving predictive tool that is applicable worldwide.
The potential benefits of our proposed project are substantial. We estimate that each homeowner served by our program who installs wind power will benefit from $70,000 in energy savings over the assumed 30-year lifetime of their turbine. The community as a whole will save $1.9 million if the program endures for 15 years. Lastly, the worldwide net benefit of our program is $42,000 when a monetary value is placed on offsetting CO2 emissions.
Supplemental Keywords:
wind power, wind energy, wind turbine, wind resource assessment, wind data, wind monitoring, measure-correlate-predict, data correlation, Mortimer’s method, variance ratio, artificial neural network, Humboldt County, Pacific Northwest, renewable energy, renewable energy education, anemometer loan program,Relevant Websites:
http://resu.humboldt.edu/~resu/sweet Exit
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.