A Spatial-Economic Analysis of Wind Power by Jeremy Tchou, Undergraduate at Harvard College
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Wind Energy in the United States: A Spatial-Economic Analysis of Wind Power By Jeremy Tchou, Undergraduate at Harvard College Abstract Wind is seen as an important component of future clean renewable technologies. Last year, wind energy capacity in the United States grew by 45%. With the explosion of wind development across the country, it is important to know the extent of available wind power in the United States, not only physically, but also economically. By combining available wind speed data between 1987 and 2006 with relevant cost restrictions, an economic map of the United States is created to determine the availability of cost-competitive wind power. Economic variables include electricity price, distance to the transmission grid and local roads, land slope, and population density. Forested areas are removed from potential areas of development. The model confirms that wind power in the central plains of the United States has the most potential to provide energy to the electric grid, and it successfully predicts the location of wind farm development currently underway. The model also highlights Missouri, South Dakota, Nebraska, Indiana, and Montana as being undeveloped states in terms of wind energy potential. The author finds that there is more than ample economically available wind energy to supply the entire electricity needs of the United States; however, this availability is largely dependent on the status of the federal incentive program, the Production Tax Credit. Introduction Within the borders of the United States, there are regions that have unusually large wind energy potential, and electricity generators have taken advantage of these resources. Wind energy has seen incredible growth in the past ten years. In 2007, wind energy grew by 45% in the United States (American Wind Energy Association 2008a). Data on US wind profiles are readily available for the United States. However, wind farm investments take into account much more than the local wind power production profile. Project developers analyze other economic factors before building a wind farm at a particular site. Such factors include location of transmission grids, population density, slope, power clearing prices, property conditions, and location of roads. Currently, there are no public sources that assemble the variables into a usable, malleable database. The WinDS model produced by the National Renewable Energy Laboratory is the most analogous model available. The model predicts future wind production in the United States integrating constraints such as electricity prices, transmission lines, and storage (National Renewable Energy Laboratory 2007). However, the NREL model focuses mainly on transmission and leaves out other model constraints which are included in the model described here. Additionally, information about results from the WinDS model is limited. As far as the author knows there is no previous study which combines all relevant economic information into a spatial map. The results of this study are important for wind farm development, transmission planning, and for policy initiatives concerning renewable energy in the United States. Wind Power Information The wind power data comes from the GEOS-4 model used by NASA’s Global Modeling and Assimilation Office (Bloom, Silva, Dee 2005). The GEOS-4 is organized around meteorological observations from the Goddard Earth Observing System and is available with global 6-hour temporal resolution and 2 x 2.5 degrees spatial resolution. The wind profiles were converted into wind power data using the GE 2.5MW turbine as a model for the power curve.1 The model incorporated wind data over 20 years, the expected lifetimes of wind power plants. Wind profile maps of the United States are readily available in the public domain. These maps, however, show a static environment representing an average wind power for a particular region. Yearly wind power can vary significantly at particular sites. In locating optimal wind turbine sites, it is important to take into consideration such variability. According to a study done in Southport, England, the highs and lows of yearly wind power output at one site can vary substantially (Godfrey 2007). 1 Many thanks for the extensive data work done by Lu Xi, graduate student in the School of Engineering and Applied Sciences, Harvard University. The 1-2 year site monitoring done by most project developments may not be sufficient to accurately predict energy production over the next 20 years. Although sites may not have such drastic power variability as cited in the aforementioned example, it is important to account for yearly fluctuations in wind. Figure 1 and 2 depict yearly changes in wind power. In figure 2 wind power can change up to 33% from year to year. Figure 1 averages yearly variability over the lifetime of the wind power plant. Using the available static maps in the public domain and extrapolating them to 20 years could potentially over or under state wind power production. To account for this problem, wind power data over 20 years is used in the model. Figure 1 Average Year-to-Year Variability in Wind Power. Figure 2 Variability in Yearly Wind Power between 1998 and Using wind data observations from one year in order to 1999. Total power output between given years can vary predict power output over 20 years can lead to substantial substantially, up to 33% in this example. This variability over or under production errors. 20 years of observations necessitated the use of 20 years of observations to reduce were used to reduce predicted power output error. power output error. Data Interpolation The original wind data information starts as point data like the ones seen in figure 3. Extrapolating information between each of the points is an important process in forming the spatial economic map. In Archer and Jacobson (2005) wind power is estimated with an inverse distance weighted procedure. A similar distance weighted interpolation method, called natural neighbors (NN), is used for the economic model to smooth the points into a continuous surface. NN interpolates data between points by using thiessen polygons. Figure 3 Natural Neighbor Interpolation. Global point data was interpolated into a smooth surface using a Natural Neighbor, area distance weighted interpolation method. A smooth surface was needed for comparison with other variables. Finances behind Wind Farm Construction Wind farm development is a capital-intensive process with high initial fixed costs. Most costs occur during the construction phase. Revenue, however, is spread out over the subsequent years. The main financial model used for profitability analysis is net present value (NPV). The NPV model discounts to the present all future cash flows. The model gives a single present dollar value indicating the profitability of a certain project. Although the finance model will not be described in detail, it incorporates but is not limited to factors such as electricity prices, operations & maintenance, land lease costs, depreciation, and the Production Tax Credit. The figures below shows a list of several of the steps used in the model as well as two examples from the toolbox. Total Revenue after operating and maintenance costs using a discount factor and variable market prices Figure 4 Toolbox List of models used Figure 5 Total Revenue Model calculates total revenue after operating and maintenance for the economic analysis. costs. A discount factor is used as well as a variable market price. Figure 6 Depreciation Calculates the value of depreciation for a wind farm. This depreciation value can be used for tax deductions. Data Results Wind power data was processed through several different economic layers. Each layer was modified to spatially represent the costs associated with the specific parameter. Four different results were calculated with the model data. First, two categories were used for price modeling. The first category, national electricity markets, uses a single national average price of wholesale electricity. The second category, differentiated electricity markets, uses different electricity prices across the US. Each category was split into two maps, one map for all model costs except for transmission and excluded areas (non-limited) and one map that included both of these variables (limited). i. National Electricity Markets Non-Limited (No Transmission Costs and no Removal of Forested Areas) Using one national electricity price allows for accurate comparison of economic wind resources across regions. As seen in the map below, the central US has some of the windiest areas in the country. The southeastern states lack the large wind resources that many of the other states have. When implementing state renewable portfolio standards and even national renewable energy goals, policy makers should take into account the spatial distribution of wind resources. Since the transmission modeling did not incorporate all of the costs associated with connecting and transmitting wind energy, it is helpful to see the map without transmission costs included. Maps like this are useful for Figure 7 Non-Limited Profitable Areas with a National Market. This map government agencies and private companies uses a constant electricity price. It does not include transmission costs nor looking to develop power transmission lines to does it remove forested areas. It is useful to compare wind power profitability without these restrictions in order to better assess future access wind resources. If agencies know the transmission line expansions. locations of power demand, a non-limited map shows the closest locations for extracting wind energy. Limited (Transmission Costs and Removal of Forested Areas) Removing forested areas and including transmission connection costs significantly influences the results of the analysis. Large areas on both coasts are absent from the map. While forests account for much of the disappearance of wind farms in the East, transmission line costs are a greater factor in the West. This is more representative of the situation facing wind developers today. Results are on the right. Figure 8 Limited Profitable Areas with a National Market.