Assessing offshore wind resources: An accessible methodology

Transcription

1 ARTICLE IN PRESS Renewable Energy ] (]]]]) ]]] ]]] Assessing offshore wind resources: An accessible methodology Amardeep Dhanju, Phillip Whitaker, Willett Kempton College of Marine and Earth Studies, University of Delaware, Newark, DE 19716, USA Received 13 October 2006; accepted 7 March 2007 Abstract This paper describes a method for assessing the electric production and value of wind resources, specifically for the offshore environment. Three steps constitute our method. First, we map the available area, delimiting bathymetric areas based on turbine tower technology, then assess competing uses of the ocean to establish exclusion zones. From exclusion zones, bathymetry, and turbine tower water depth limitations, the water sheet area available for wind turbines is calculated. The second step is calculation of power production starting from available area, which determines the location and count of turbines. Then existing wind data are extrapolated to turbine height and, along with the turbine power output curve, they are used to establish the expected electric power production on an hourly basis. The third step calculates market value based on the hourly electric market at the nearest electric grid node. To illustrate these methods, we assess the offshore wind resource of the US state of Delaware. We find year-round average output over 5200 MW, or about four times the average electrical consumption of the state. On local wholesale electricity markets, this would produce just over $2 billion/year in revenue. Because the methods described here do not rely on constructing meteorological towers nor on proprietary software, they are more accessible to a local government, state college, or other organization. For example, these methods can be carried out as an initial assessment of resources, or by a government or public entity as a check on claims by private applicants. r 2007 Elsevier Ltd. All rights reserved. Keywords: Wind power; Wind resource assessment; Offshore wind power; Exclusion zones; Value of wind power 1. Introduction A common first-cut evaluation of wind resource uses mean wind speed, from a nearby weather station or from a wind model. The US Department of Energy and state resource agencies have sponsored maps of mean wind speed for much of the United States, and these maps are useful in locating suitable wind sites with high wind speed and proximity to loads or transmission. This mapping has been expanded offshore in the EU and such has started in the US [1,2]. From mean wind speed and the assumed Weibull distribution, an annual output in MWh can be projected. This annual electrical output times the number of turbines planned for a specific development provides the basis for an initial determination of project return on Corresponding author. Tel.: ; fax: addresses: amar@udel.edu (A. Dhanju), pw@udel.edu (P. Whitaker), willett@udel.edu (W. Kempton). investment. But 2 or more years worth of wind speed data at hub height have been required by investors to secure financing for utility scale wind projects. Investor-grade hub height data require a meteorological tower. For example, in Horn s Rev, Denmark, 3 years worth of data from a meteorological tower were gathered and assessed before construction commenced. For the Cape Wind project in Nantucket sound, a data tower was erected in 2002 and data collected through May of 2005 for investors. Investors doing due diligence are not the only ones interested in the value of the wind resource. For other parties, an approximate calculation of the offshore wind resource and its value may be useful. Stakeholders such as coastal community governments, state regulators, advocates pro or con, and others may wish to evaluate for themselves the size and market value of a wind regime. For these parties, it may be more important to have an accessible and inexpensive method of assessment than to wait for (and pay for) precise on-site data. Also, the /$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi: /

2 2 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] geographical scope of a government, advocate, or university researcher may be much larger than that of a developer, who is concerned with a single project at a single time. Use of existing data makes a large area analysis economically practical, and thus may make these methods also of value to national governments. Therefore, we sought to develop a methodology that is: (1) appropriate for the ocean rather than carried over from land methodologies, (2) using existing data, not requiring new on-site measurements, (3) accessible to a variety of parties without purchase of equipment or proprietary software, (4) applicable to large areas, and yet (5) accurately taking account of fluctuations in wind and pricing throughout the year. Although a next step, with hub-height measurement, would probably be required at any single site to satisfy the requirements of an investor, the methods developed here can provide a basis for public decisions such as policy initiatives, electricity generation planning, advocacy, and, by developers, decisions about initial site location proposals and whether to expend funds for permitting and more precise information gathering. As a test case, the methodology was applied to Delaware to assess its offshore wind power potential. 2. Available area mapping This first part of our resource analysis delimits the areas available for resource development, based on depth limitations and competing ocean uses that define exclusion zones. Spatial data from navigation charts and the NOAA ENC direct website [3] were used to plot the bathymetry and some exclusion zones Bathymetry The bathymetric depths included in our analysis are based on a technology analysis of turbine tower technology. We assume that all machines will be 3 MW or greater. Towers for the machines are today based on monopile design a single tube of steel, approximately 5 m in diameter, either on a foundation or driven into the sea floor. The first such machines installed, the GE 3.6 s, were recommended by the vendor to be installed in no more than 20 m of water depth. Current project engineering utilizes monopole towers up to 30 m of water depth. A different tower design, using an offshore-oil-derived jacket structure, has recently been installed in 45 m of water [4] and the design has been validated for 50 m depth [5]. Therefore, in our area mapping we distinguish 20 m as current practice, 30 m as current project planning, and 50 m as prototyped technology. Due to translations from units used in soundings (in ft or m, depending on the map) to the industry-benchmark tower depths (in m), the intervals we use are bounded at depths of 0, 18.2, 27.4 and 50 m. Fig. 1 illustrates the nearer shore area, showing bathymetric gradients for the area to 20 m and most of the area to 30 m. This is included in the area in the Delaware Bay and 60 km out in the Atlantic Ocean. The white areas of river and of ocean to the North of Delaware are not in the bathymetric data we are using. We do not extend the map further East because the full 50 m area would almost double the map size and would loose resolution on the exclusion zones, which are predominantly near shore Exclusion analysis As with land sites, there are competing demands for the water sheet, but oceanic competing uses are very different from terrestrial ones. Some competing demands clearly exclude offshore wind turbine placement, such as designated shipping lanes, and the analyst can definitively exclude them from wind power development. Others such as distance needed to minimize viewshed impact from beaches or residences are decided by political processes, differ in different regions (e.g. Massachusetts versus Texas) and cannot be conclusively determined by the analyst in advance. The exclusion analysis of Delaware examines half of the Delaware Bay constituting Delaware state waters, and the offshore areas extending directly out from the entire 40 km (25 mile) Atlantic shoreline of Delaware. Exclusions derived from navigation maps were as follows: all designated shipping lanes, documented chemical dumps (an acid waste dump site), an area marked residual danger from mines, and a designated restricted area (military) at the mouth of the Delaware Bay. Also excluded were all current sand borrow areas used for beach re-nourishment. Currently, the Army Corps of Engineers has designated five beach nourishment areas off the Delaware coast [6], covering km 2. On the basis of a regional report augmented with consultation with a local ornithologist, the known major avian migratory pathways were also mapped as exclusion zones [7]. These flyways extend out 1 km from the shore; and across the Delaware Bay as a 5 km wide corridor running from Cape Henlopen, Delaware to Cape May, New Jersey. Exclusion for visual impact is based on expressed values such as keeping the ocean horizon pristine, without visible human structures, and the distinct value of preserving familiar viewscapes as local residents have known them [8,9]. Although the weighting residents give these values depend on both local culture and on residents contextual understanding of the project (e.g. a project seen as a government-financed boondoggle may be reported as more visually intrusive than one seen as an environmentally critical technology), for this analysis, we assumed that minimizing apparent turbine size would be of greatest value within the viewshed of high-tourism coastal communities, and that in other inhabited coastal areas, the other values of wind development might be seen as more important in balance. Thus, we centered the visual exclusion analysis on the highest volume tourist town,

3 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] 3 Fig. 1. Study area with buoy sites and bathymetry. Rehoboth Beach, Delaware. (This is an analytical simplification, not a policy recommendation.) For the visual exclusion, two distances were examined; to aid in visualization, both are described in terms of the apparent height of a turbine tower in comparison to an object held at the end of a person s outstretched arm. The first level is that the 80 m high turbine towers should not appear to a standing adult to be larger than half the height of the thumb on an outstretched arm (assuming a 2 m person, 75 cm arm length, and 3 cm for half a thumb). The more conservative criterion is that the tower should not appear larger than the height of 4 dimes stacked, or the thickness of a wedding band (4 mm). Given these figures and assuming an 80 m tower height, simple trigonometry yields an exclusion radius of 2 and 15 km, respectively. The curvature of the Earth was not considered in this calculation, due to its small effect at these distances. (For the more distant 4 mm exclusion, we calculate that the curvature of the earth further reduces the needed distance from shore by 1.2 km over 15 km, an effect reducing the visual exclusion distance less than 8% and ignored here.) The parameters used here are for our initial estimates of areas, not recommendations for siting decisions. For example, the determination of exclusion areas for viewshed are a matter of public debate and political decision making, not determinable by the isolated analyst. Also, our analysis of bird flyways is not based on detailed ornithology. Thus, actual decisions about placement of turbines, if any, would be made as detailed studies are done and the decisionmaking process proceeds in local discussion and hearings. Fig. 2 depicts all the exclusion zones analyzed and also marks the available area in Delaware Bay and off the Atlantic shoreline. The black line running through the Delaware Bay and out into the Atlantic is the Northern

4 4 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] Fig. 2. Exclusion zones assumed for this analysis. boundary of our analysis; the bottom of the map, corresponding to the Southern Delaware border, is the southern boundary of our analysis. Within the Bay the black line is the legal limit of Delaware waters, but the continuation of the black line into the Atlantic Ocean is an arbitrary latitude line, not corresponding to a legal distinction, to delimit our analysis of offshore Delaware. The analysis also does not distinguish state waters in the Atlantic (out to 3 nautical miles, 5.6 km) from Federal waters [10]. The exclusion areas shown visually in Fig. 2 are quantitatively summarized in Table 1. Note from Fig. 2 that exclusion areas extensively overlap. Thus the exclusion area numbers cannot be numerically summed. The table can be used to examine the areas that were excluded for any one use. For example, the bird flyways we identified would exclude 103 of 1485 km 2 or 7% of the m waters, they exclude 24 of 849 km 2 or 3% of the m waters, and none in the m waters. By comparison, shipping lanes require greater exclusion areas than any other use, and this is true at every depth interval considered here. The overlapping of exclusions can be seen in our evaluation of the change from no visual exclusion to 2 km, then to 15 km visual exclusion. The last two rows of Table 1 show the sizes of the visual exclusions at differing depths; the effects are largest near shore. Table 2 shows their interaction with other exclusions. Specifically, Table 2 gives the total area before considering any exclusions, the area available after removing all other exclusions but no visual exclusion, and the area available with two increasing distances of visual exclusion. That is, Table 2 shows the effect of adding visual exclusion on top of other exclusions. Due to both overlapping exclusion areas and the large amount of total area available, even the 15 km visual exclusion would have only a modest effect on the total area

5 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] 5 Table 1 Areas of each individual exclusion, not considering overlap (areas in km 2 ) m m m Total Total area analyzed Bird exclusion Military area and explosive waste dumps Beach renourishment borrow area Chemical dump area Spoil ground Designated shipping lane Visual exclusion of 2 km (apparent half a thumb, 3 cm) Visual exclusion of 15 km (apparent height of four dimes or a wedding band, 4 mm) Table 2 Comparison of three alternative assumptions of visual exclusion, showing ocean area in total and area after removing each exclusion (areas in km 2 ) m m m Total Total area (no exclusions) Available area, with no visual exclusion Available area, with 2 km visual exclusion Available area, with 15 km visual exclusion available. The effect is more pronounced in the 0 20 m depths of greatest current attention, and would differ in areas of different offshore geometry (e.g. the much publicized Cape Wind proposal in Nantucket Sound is surrounded on three sides by human viewsheds and sandwiched between shipping lanes, so the effect of increasing viewshed exclusion would be more dramatic there). Considering all exclusions, the total area out to 50 m depth that is available for turbines in the waters off Delaware is 2890 km 2. Of this, 504 km 2 is in Delaware Bay and 2386 km 2 off Delaware s Atlantic coast. 3. Power production To calculate the power that could be produced by the offshore wind resource, we first calculate the number of turbines that would fit within the area, then analyze the wind regime to calculate the power output of each turbine. The total power production is then the number of turbines times the power production per turbine Turbine count In a given area, the number of turbines that can be placed is constrained by the inter-turbine spacing. In laying out wind power sites on land or over water, the choice of inter-turbine spacing represents a trade-off between more power per unit area (closer spacing) versus more power per machine (wider spacing). The textbook treatment for land turbines is to keep inter-turbine wake losses below 10% by keeping the downstream spacing no less than 10 rotor diameters and cross-wind spacing five diameters [11]. For the GE 3.6 s with a 104 m blade, this array spacing corresponds to 0.54 km 2 per turbine. Using this spacing factor, total number of GE 3.6 s turbines that might be placed offshore Delaware within the available area would be 5351 with a maximum installed capacity (also called nameplate capacity) of 19,264 MW. Of these, 933 could fit in Delaware Bay and 4418 in the Atlantic Ocean. The next section of the paper examines wind data, and calculates the potential power production from offshore wind turbines Use of measured, point wind data rather than modeled areas One characteristic of the offshore wind resource that simplifies resource evaluation is that it is similar over large areas. In terrestrial wind mapping, especially in hilly or mountain terrain, the wind speed will vary dramatically as one moves horizontally a few kilometers or even hundreds of meters (e.g. on a ridge top compared to wooded valleys to either side). Over the ocean, there are local effects near the shore and within estuaries, but on the open ocean much larger areas have fairly uniform wind speeds. The wide areas of similar wind speed can be seen for the region covered here in maps based on buoy data [12,13]. On land, the most widely used wind mapping technique, mesoscale mapping using proprietary software, is based on existing wind data from sparse weather stations. This is not used directly, but rather national weather service models of large-scale air movements are used as input of weather over large areas, and the mesoscale model combines this with

6 6 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] much more dense topographic data, to infer wind speed at high resolution. Over the ocean, the uniform topography means that models using topography will provide little additional resolution. On the other hand, given the more uniform wind field over the open ocean, an anemometer measurement will be applicable for wider areas around the measurement point. For this reason, we will make an assumption that greatly simplifies the wind resource assessment process and renders it more accessible for parties with modest resources the nearest buoy s actual wind measurements will be used rather than a model output, with a simple extrapolation method to convert from wind speed at buoy height to wind speed at turbine hub height Selection of buoy Table 3 gives the meteorological stations on land along the Delaware Bay (labeled Ports ) and the NOAA buoys in the area offshore Delaware. They are also shown in the map of Fig. 1. Buoy is close to shore and near a large shallow area away from the largest tourist area, thus it may be a possible near-term wind farm site. The buoy is located between a coastal meteorological station and an ocean buoy in Fig. 1, so for that reason also is a reasonable pick for our goal of making a simple approximate estimate for the area. (Table 3 shows wind speeds derived from a more complex model than that employed here [14], as that was run on all these buoys.) Procedurally, meteorological data for the past several years were downloaded and cleaned to ensure a complete year of data existed (8760 hourly entries). These wind speeds were recorded in 10 min intervals, but were collapsed to hourly in order to simplify data processing, and to better match the hourly PJM market data. NOAA buoy is located only 34 km ESE of Indian River Inlet, Delaware (See Fig. 1). Examination of the recorded data showed the year 2003 with the most nearly whole data set. NOAA wind data for offshore buoys are originally recorded as an average of each 10 min period. The buoys perform under hostile conditions and it is not unusual to find gaps where readings are missing or null. In order to complete the data set we retrieved the historical averages for each month and inserted the value for the appropriate month in the place of the missing data. The result was a data set of near-surface wind speed readings hourly for a year Extrapolation of wind speed to hub height The recorded anemometer readings for buoy were taken at 5 m elevation. This needed to be converted to speeds at 80 m. There are a number of methods to extrapolate buoy data to hub height and all of them have weaknesses. A comprehensive discussion of the alternatives is available from recent work by Archer and Jacobson (2005) [15]. For this exercise, a simplified log law conversion was used, as given in Formula 1. R ¼ u 2 ¼ log ðz 2=Z 0 Þ u 1 log ðz 1 =Z 0 Þ, (1) where R ¼ wind velocity at desired hub height, in this case a generic 80 m was used; u 1 ¼ wind velocity at the lower height; u 2 ¼ wind velocity at desired hub height of 80m; Z 0 ¼ the surface roughness, ocean surface roughness varies from a maximum of 4 mm to a minimum of mm. A moderate value of mm was assumed in all calculations. The formula is in m so the notation is m and Z 1 ¼ lower height in m, Z 2 ¼ upper height in m. The simplest way to calculate turbine power from wind speed is to assume an idealized machine. The formulae is from [11]: P w ¼ r D U 3. (2) For the GE 3.6 s in 8.2 m/s average wind speed, P w ¼ð1:225 kg=m Þ m 3 8:2m=s ¼ 3246:10 kw: (3) That is, an ideal wind turbine, in the 8.2 m/s wind speeds, would on average generate kw. But a much more accurate way is to base the calculation on actual machines rather than ideal ones. To do this, we use a best-fit polynomial to the power output curve in the product specifications for the GE 3.6 s turbine. The fit of the curve used had an R 2 value of The formula derived could be improved as it results in extremely small Table 3 Bay shore and NOAA Buoys; from these, was selected for use Location ID Anemometer height (m) Mean wind speed at hub height [14] (m/s) Latitude (N) Longitude (W) PORTS ID LEWES 12.2 n.a PORTS ID DELAWARE CITY 6.4 n.a PORTS ID BRANDYWINE SHOALS NOAA ID NOAA ID NOAA ID

7 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] 7 negative values at initial startup speed for the turbine. These small negative values are assumed to be of little significance and no attempt has been made to compensate for their presence. In the middle section, m/s, the polynomial that maps wind speed at hub height (u 2 ) in m/s to output electrical power (P) inkwis P ¼ 2:2428 u 3 2 þ 82:057 u :93 u 2 þ 890:84. (4) The 3.6 s begins production at wind speeds of 3.5 m/s and follows the output curve to its maximum nameplate capacity of 3600 kw at 14 m/s. From 14 until 27 m/s, it produces a constant 3600 kw. Above 27 m/s, safety cutoffs engage and electricity output drops to zero. This was accounted for by configuring the formula to set 0 output for wind speeds below 3.4; to plot values between 3.5 and 14 with the derived polynomial; to set output at 3600 kw between 15 and 27; and to set all values above 27 as zero. 4. Market value Delaware power is sold into the PJM system. PJM originally stood for Pennsylvania, Jersey, Maryland, but it has expanded to now manage a regional electrical grid that stretches over 17 states. Any offshore wind from Delaware s Atlantic coast would feed into the most proximate node, South Delaware, with connection at Indian River. All of the PJM market data are publicly available [16]. PJM uses a pricing system called locational marginal pricing (LMP) where the price is set by the seller s actual location within the transmission grid. This allows for effective market signaling but complicates the data analysis somewhat because it also results in wide variability in actual bid/sell price of electricity within PJM. The PJM data are aggregated into reports that include hourly averages over a collection of specific pricing points. In this case, Delaware is divided into a number of these aggregated LMP points. As mentioned, because of its close proximity to the Delaware offshore areas, the one of concern to us is the South Delaware Power Limited aggregate Strategies for selling wind farm output into energy markets One realistic way to estimate the value of wind electricity is to examine existing markets for electricity and see how wind would sell into them. In doing so, it is important to reflect the two characteristics of wind, its variable output and its zero fuel cost, so as to accurately compare it. We outline three basic approaches. The first method is to change the dispatchability of the wind generator, so as to make it have characteristics similar to thermal plants. Specifically, that would mean either over-sizing the wind facility or providing large storage, so as to make a near-perfect load match. Operationally, that would mean charging the storage or spilling wind when there is more than needed, and discharging storage when there is more need for power than wind can provide. (A similar approach would be to have a backup fossil generator the same size as the proposed wind facility.) This may be required in isolated electrical systems, such as an island, or it may be economic in cases of distant wind using storage with generation to reduce transmission costs, [17] or if storage is inexpensive because it is being purchased for other reasons [18 20]. But in the electrical grids of most countries, there are many other generators, already set up to respond to load fluctuations, and storage or backup typically are not needed at moderate wind power penetrations. This is true for PJM. The second option is to rely on markets for firming power, and to not provide steady power via technical means dedicated to the wind generation. This can be achieved through a payment by the wind operator to another power provider for firming power. In this case, the wind provider would incur some financial penalty, but then avoids the need to build storage or backup power facilities. Instead, wind energy facility developers and operators sign a long-term contract that covers their needed return on investment. The lack of fuel costs means that the energy purchasing entity has the ability to forecast production against demand for a hedge against fuel price volatility. For the wind developer with this commitment in hand, obtaining capital follows the routine business model. To value wind power by this method, we would consider recent power purchase agreement prices, and subtract a recent average price for firming power. The third method is to sell the output of a wind farm on the spot energy market, and let buyers set the value of wind on an energy basis. In the power bidding market used in many liberalized markets, power providers bid at a slight margin above their operating costs, hoping their bid is below the highest bid. Wind can bid at or near zero, because if the wind is sold at a low price, their revenue is still above their costs. Fossil bidders are unlikely to bid so low and risk revenue below their fuel costs. Wind, not experiencing this constraint, has the ability to bid to sell at near its fuel cost zero. But the purchase price is set at the last (highest) price, thus the wind bidder wins yet the price is set above their marginal cost. Thus, we can use the market clearing price on current markets to estimate the value of wind power. To value the wind resource, we used the third strategy, the hourly spot market price. This method gives an accurate estimate of the current value of the power, but it does not reflect the fact that if enough power is added to this market, it might lower the bid price in the hourly markets. Nor does it reflect the value of renewable energy certificates or other policies to incentivize clean power Market value on hourly market The results are summarized in Table 4. The actual wind, wind turbine output and price data used in all calculations

8 8 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] Table 4 Monthly averages at buoy and wholesale price at the So Del node for a single 3.6 MW turbine. (Calculations are made on hourly data, so these aggregated monthly column averages cannot be precisely multiplied.) Wind speed at 5 m (m/s) Wind speed at 80 m (m/s) Average GE 3.6 s output (MW) Monthly output (MWh) Price at SoDe ($/ MWh) Monthly revenue ($) January February March April May June July August September October November December Total 10,481 $453,336 Table 5 Delaware Atlantic Wind Power Resource within 50 m depth, as calculated in this study Area (km 2 ) Max turbines (Count) Installed capacity (MW) Average power production (MW) Delaware Bay Not evaluated Atlantic Ocean were hourly; however, since a year of hourly data would include 8760 rows, the results of those calculations are presented in Table 4 as monthly averages. Thus, some figures do not multiply precisely across the Table. For example, January s production (1571 MWh) times the average January price of $49/MWh would yield $76,980 in revenue. But Table 4 shows that the figure, when calculated hourly, is $85,298. This is because, when calculated hourly, some of the hours with higher wind in January also had higher electricity prices. Using the number of turbines calculated earlier, we estimate potential annual electricity production and the value of that electricity for the entire Delaware ocean resource. In the Atlantic, the prior section estimated that 4418 turbines of the size of the GE 3.6 s could be accommodated, within 0 50 m depths and after all exclusions analyzed previously. As shown in Table 4, we calculate that a turbine placed at buoy would produce the power and revenue as shown in the bottom total row of the table. Because the wind resource is fairly uniform over large areas, we assume as an approximation that all the turbines in this area of the Atlantic would achieve this same power output. Thus, for the Atlantic waters (not including Delaware Bay) we estimate total power of 4418 turbines 10,481 MWh ¼ 46,305,058 MWh/year, or a year-round average output of 5286 MW, as shown in Table 5. For revenue, the total would be 4418 $453,366 ¼ $2,003,000,000/year or just over $2 billion/year in revenue. If we consider only the current commercial technology limit of 30 m depth 7% 6% 4% Monthly Earnings 10% 7% 6% 2% 4% 9% 18% 11% 16% Graph 1. Earnings for months 1 12 based on production times price. ( m depth in Table 2), only 2181 turbines can fit, with an average aggregate power output of 2609 MW and revenue of $989 million/year. Turbines in the Delaware Bay would be in addition to either of these Atlantic resource figures, as shown by turbine counts in Table 4. To compare the wind resource with current fossil fuel power plants, Delaware s 2002 generation capacity is 3390 MW (Source: and average consumption is 1300 MW. Due to the coastal areas higher summertime population and air conditioning load, we expected wholesale electricity to be more expensive in summer than winter. On the contrary, possibly due to use of electric home heating and higher natural gas prices, wholesale electric prices did not vary so much and in fact were highest during two winter months. Thus, higher summertime prices did not

9 ARTICLE IN PRESS A. Dhanju et al. / Renewable Energy ] (]]]]) ]]] ]]] 9 Table 6 Delaware offshore Wind Power Resource to 50 m depth, using exclusion from this study and capacity factors from [14] Area (km 2 ) Max turbines (Count) Installed capacity (MW) Capacity factor Average power production (MW) Delaware Bay Atlantic Ocean , Total , compensate for lower wind speeds and less MWh production during the summer months. On the other hand, we found that high prices during the winter months, combined with the greater winds, produce substantially more revenue than we would have predicted. This is shown in Graph 1. Note that months 1 (January at 18%), and 2 (February 16%) produce the most revenue of the year. As a check, we use a different way of characterizing the wind resource, capacity factor. The capacity factor is the amount of power produced as a fraction of the maximum if it were running at full power all the time. For example, a 3.6 MW wind turbine running at full power for 365 days * 24 h, has an annual production capacity of 31,536 MWh. Since our calculated turbine output at buoy would have produced 10,481 MWh in 2003, we derive a capacity factor of This is slightly lower than derived by other analysis of this area [21]. In Table 6, we recalculate the resource, using the exclusion areas from this study and the capacity factors from another study [13]. That other study, by Kempton et al., used a longer time period (23 years) and a more complex extrapolation to hub height, and found higher capacity factors than those calculated here. That study also characterized the wind in the center of the Delaware Bay, allowing a calculation of the power available there as well. The available areas and turbine count of the present study are combined with the capacity factors derived from Kempton et al. in Table 6. Using this method, average power production from all the non-excluded Bay area is 1209 MW, from the ocean areas is 6203 MW, for a total resource of 7412 MW average output power. Again, this compares with the average electric use of the entire state of 1300 MW. (These figures can be multiplied by 8760 h/year to yield MWh/year.) For the Atlantic Ocean area computed by both methods, the second method gives output power 17% higher, a reasonable error margin. More detailed assessments of the wind regime and the predicted load of the region are warranted. The age and pollution levels of the local Indian River coal-fired plant, coupled with rapid population growth in the area make the wind resource off the Delaware shore increasingly attractive. 5. Conclusion The method described here provides a passable evaluation of oceanic wind resources, based on existing data and public algorithms. This makes a resource assessment possible by a state, local government, or even a nongovernmental organization, if some expertise, and some wind data, are available locally. All the data used in this resource assessment are publicly available. Regarding the specific area used in the case study, the power available for offshore Delaware varied by 17% between the two methods compared but in either case was over four times the electricity consumption of the entire state of Delaware. Revenue computed from the method yielding the smaller wind resource, at current market prices, was over $2 billion/year, without considering the value of renewable energy credits or other incentives. This is an estimate of the total resource available and feasible, not a policy recommendation that all available resource should be built out. This approximate method can also be used to assess the wind resource and its value over a large area, in contrast to the task of the wind developer, which is to obtain more a precise estimate suitable for investment decision at a single site. Acknowledgments This paper derives from a University of Delaware graduate seminar project by Dhanju, Whitaker and Sandra Burton, and was previously presented as a poster, Methodology for Assessing the Offshore Wind Resource: Application to Delaware at the Windpower 2006 conference by AWEA. We are grateful to Paul Kerlinger for advice on avian exclusions. This project was partially funded by the UD College of Marine and Earth Sciences and the Delaware Energy Office, Department of Natural Resources and Environmental Control. References [1] Berlinski M, Connors S. Economic and Environmental Performance of Potential Northeast Offshore Wind Energy Resources, LFEE , Laboratory for Energy and the Environment, Massachusetts Institute of Technology. Cambridge: Massachusetts; [2] Elliott D, Schwartz M. Wind resource mapping for United States offshore areas. In: Windpower 2006 conference and exhibition, American Wind Energy Association, Pittsburgh, PA, June 4 7, [3] NOAA ENC Direct website, / MCD/enc/index.htmS.

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