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HomeMy WebLinkAboutDesktop Wind Resource Study - Dillon Platform - Cook Inlet - Jan 2024 - REF Grant 7015023 V3 Energy, LLC Page | 1 Technical Memorandum To: Mike Salze�, Homer Electric Associa�on From: Douglas Vaught, P.E., V3 Energy, LLC, Anchorage, Alaska Date: January 17, 2024 Subj: Desktop Wind Resource Characteriza�on of Dillon Pla�orm, Cook Inlet, Alaska Introduc�on This technical memorandum presents the an�cipated wind resource, as predicted by Global Wind Atlas modeling, at Dillon Pla�orm in Cook Inlet, Alaska in support of Homer Electric Associa�on’s award from Alaska Energy Authority for Renewable Energy Fund Round 15 for Cook Inlet Oil Platform Wind Project. Project Descrip�on Homer Electric Associa�on (HEA), through its genera�on subsidiary Alaska Electric & Energy Coopera�ve (AEEC), is inves�ga�ng the poten�al for construc�on of approximately 30 MW of wind energy genera�on capacity on or near the Kenai Peninsula. As part of that effort, the Cook Inlet Oil Platform Wind Project will evaluate construc�on of three-to-four offshore wind turbines on the A, C, Baker, and Dillon offshore oil pla�orms in Cook Inlet near Nikiski, Alaska. The turbines would connect electrically to the electrical grid at HEA’s Bernice Lake Substa�on in East Forelands area of Nikiski.1 Project Sites Although the four oil pla�orms noted above are presented as of equal interest in AEEC’s Renewable Energy Fund (REF) Round 15 applica�on, HEA has indicated that Dillon Pla�orm is primary interest with the other three pla�orms secondary interest for wind power development. This priority is logical with reference to Figure 1 as Dillon Pla�orm is nearest Nikiski and the Bernice Lake Substa�on. Per HEA communica�ons with V3 Energy, LLC, la�tude and longitude of the pla�orms are as follows (note that coordinates are datam NAD83): • Dillon Pla�orm (60.734670, -151.515210) • Baker Pla�orm (60.828651, -151.485942) • MGS-A Pla�orm (60.795843, -151.495668) • MGS-C Pla�orm (60.764040, -151.502160) 1 From REF Round 15 Application Summaries, AEA website V3 Energy, LLC Page | 2 Figure 1: Baker, A, C, and Dillon Oil Platforms in Cook Inlet, Google Earth image Global Wind Atlas The Global Wind Atlas (GWA) is a free, web-based applica�on developed to help policymakers, planners, and investors iden�fy high-wind areas for wind power genera�on virtually anywhere in the world, and then perform preliminary calcula�ons. The GWA facilitates online queries and provides freely downloadable datasets based on the latest input data and modeling methodologies. The current itera�on of the GWA (version 3.3) is the product of a partnership between the Department of Wind Energy at the Technical University of Denmark (DTU Wind Energy) and the World Bank Group (consis�ng of The World Bank and the Interna�onal Finance Corpora�on). Work on GWA 2.0 and GWA 3.0 was primarily funded by the Energy Sector Management Assistance Program (ESMAP), a mul�-donor trust fund administered by The World Bank and supported by thirteen official bilateral donors. It is part of the global ESMAP ini�a�ve on renewable energy resource mapping that includes biomass, small hydropower, solar energy, and wind energy. GWA 3.0 builds on an ongoing commitment from DTU Wind Energy to disseminate data and science on wind resources to the interna�onal community. Purpose The GWA primarily supports wind power development during the explora�on and preliminary wind resource assessment phases prior to the installa�on of meteorology measurement sta�ons on site. It Kenai Nikiski V3 Energy, LLC Page | 3 also serves as a useful tool for governments to beter understand their wind resource poten�al at provincial and local levels. Objec�ves The objec�ves of the GWA are to: • provide wind resource data accoun�ng for high-resolu�on effects. • use microscale modeling to capture small-scale wind speed variability (crucial for beter es�mates of total wind resource). • use a unified methodology over the en�re globe and update the Global Wind Atlas as methodologies develop. • ensure transparency about the methodology used. • support the verifica�on of the results in the long-term by coupling to measurement data and campaigns. The correct usage of the Global Wind Atlas dataset is for aggrega�on, upscaling analysis and energy integra�on modeling for energy planners and policy makers. It is not advisable to solely use the data and tools for wind farm site selec�on. Limita�ons of GWA The GWA uses two major modeling components that can introduce uncertainty into the calcula�ons. These components are mesoscale modeling and microscale modeling. Uncertain�es associated with the mesoscale modeling include representa�veness of the large scale forcing and sampling, model grid size, descrip�on of the surface characteris�cs, model spin-up, simula�on �me and modeling domain size. Uncertain�es associated with the microscale modeling include the orographic flow model within WAsP 2, the surface descrip�on, and departures from the reference wind profile. Concerning the orographic flow model, the model performs well when the surrounding terrain is sufficiently gentle and smooth to ensure mostly atached flows. With the global coverage of the GWA, we use the BZ-model in areas beyond its recommended opera�onal envelope. The GWA website allows users to see where the flow modeling is likely to be increasingly uncertain, by adding a RIX layer to the set of maps. The RIX layer represents ruggedness index and is an objec�ve measure of the steepness or ruggedness of the terrain. Large RIX values will lead to large errors in the flow modeling, most likely leading to an overes�ma�on of mean wind speeds on ridges and hilltops. We therefore recommend users to inspect the RIX of their region of interest.3 Model of Cook Inlet-wide Wind Resource The GWA model of the Cook Inlet wind resource at the 100-meter level (refer to Figure 2) indicates rela�vely high wind speeds over water compared to surrounding lower eleva�on land masses. The 2 Wind energy industry-standard software - WAsP 3 Global Wind Atlas (About menu; this and preceding sections) V3 Energy, LLC Page | 4 model predicts highest over-water wind speeds in Upper Turnagain Arm and lower Cook Inlet with more moderate, but s�ll robust, wind speeds in central Cook Inlet. Figure 2: The GWA model of 100-meter level wind speeds in Cook Inlet (Dillon Platform location noted with blue pin) Dillon Pla�orm Modeled Wind Resource Pinning the loca�on of Dillon Pla�orm within the GWA so�ware creates a 3 km x 3 km (9 km2) box around the pla�orm with data presented for the 10% windiest area, or 90th percen�le probable wind, within the box. This is helpful in complex terrain where wind speeds vary considerably over short distances. Over a large expanse of water, this probability feature is insigh�ul primarily because it demonstrates a rela�ve lack of wind speed variability over longer distances (refer to Figure 3 and Figure 4). At the 100-meter level at Dillon Pla�orm, GWA predicts a 7.74 m/s mean wind speed with extremely low variability within the 9 km2 box. V3 Energy, LLC Page | 5 Figure 3: Dillon Platform, GWA-modeled wind speed in 9 km2 boxed area, 100-meter level Figure 4: GWA-modeled distribution of wind power density and wind speed within 9 km2 boxed area of Dillon Platform The GWA models prevailing winds at Dillon Pla�orm as primarily north-northeasterly with secondary south-southwest winds (refer to Figure 5). This matches the secondary intercardinal orienta�on of Cook Inlet, especially when considering the long fetch of the Susitna River to the north-northeast instead of Knik Arm. The modeled wind power rose though indicates that power-producing winds are overwhelmingly northerly and north-northeasterly (refer to Figure 5). Figure 5: GWA-modeled wind frequency and power roses at Dillon Platform V3 Energy, LLC Page | 6 Modeled monthly wind speed variability (refer to Figure 6) predicts higher winds during the cold weather months compared to summer, which is expected and generally aligns with residen�al electric load demand. Modeled hourly, or diurnal, wind speed variability predicts highest winds in the early morning hours, peaking at 6:00 a.m. (refer to Figure 6), which is less aligned with load demand. Figure 6: GWA-modeled monthly and hourly wind speed variability at Dillon Platform Wind Power Class NREL’s seven wind power classes are based on wind power density (WPD), calculated with the following equa�on: 𝑃𝑃𝐴𝐴=1/2𝜌𝜌𝑣𝑣3 Classifica�on by wind power density or mean wind speed is not a design standard for wind turbine manufacturers, but rather a useful and intui�ve method to assess and compare sites (refer to Table 1). The reader is cau�oned to note that wind turbine energy yield versus wind class is a non-linear rela�onship. Class WPD (W/m2) (50 m) Mean Speed (m/s) (50 m) Rating 1 <200 <5.6 Poor 2 200 – 300 5.6 – 6.4 Marginal 3 300 – 400 6.4 – 7.0 Fair 4 400 – 500 7.0 – 7.5 Good 5 500 – 600 7.5 – 8.0 Excellent 6 600 – 800 8.0 – 8.8 Outstanding 7 >800 >8.8 Superb Vertical extrapolation based on 1/7 power law (0.14 wind shear exponent) Mean speed based on Rayleigh distribution; Weibull k = 2.0 Table 1: NREL classification of wind power density, at 50-meter level Dillon Pla�orm For the loca�on of Dillon Pla�orm in Cook Inlet, at the 50-meter level, the GWA predicts a 7.11 m/s mean wind speed and a 601 W/m2 mean WPD. With reference to Table 1, this equates to a Class 4 (good) wind resource by wind speed and a Class 5 (excellent) to Class 6 (outstanding) wind resource by WPD. This difference can be explained by the colder than standard condi�ons mean air temperature of Cook Inlet and perhaps also by a possible non-Rayleigh distribu�on of wind speeds (where Weibull k ≠ 2.0). V3 Energy, LLC Page | 7 IEC 61400-1 Classifica�on Design standards for wind power are published by the Interna�onal Electrotechnical Commission (IEC), based in Geneva, Switzerland, with regional centers in North and South America, Africa, and Asia. The IEC was founded in 1906 and is the world’s leading organiza�on for all electrical, electronic, and related technologies. Their mission: achieve worldwide use of IEC International Standards and Conformity Assessment systems to ensure the safety, efficiency, reliability, and interoperability of electrical, electronic and information technologies, to enhance international trade, facilitate broad electricity access and enable a more sustainable world. IEC’s Technical Commitee (TC) 88 covers wind genera�on via the 61400 series. The standard of interest is IEC 61400-1, Wind energy genera�on systems – Part 1: Design requirements. Although a new 4th edi�on of IEC 61400-1 was published in 2019, most wind turbines of interest to HEA were designed under 3rd edi�on (2005) standards and hence it will be the focus of this review. Six parameters or analyses comprise IEC 61400-1 wind classifica�on as listed below, with the simplified classifica�on (that references only extreme wind and turbulence intensity) in Table 2. They are: • Extreme wind • Turbulence intensity • Wind shear • Wake turbulence • Flow inclina�on • Wind distribu�on Table 2: IEC 61400-1, 3rd edition, simplified wind classification Wind Class I II III S Vref (m/s) 50.0 42.5 37.5 Values specified by the designer A (TIref) 0.16 B (TIref) 0.14 C (TIref) 0.12 Extreme Wind The classifica�on of extreme wind is by Vref, the reference wind speed; the highest measured or probable 10-minute average wind speed in a 50-year return period. This can be calculated probabilis�cally with a periodic maxima analysis (by Gumbel distribu�on), the method of independent storms (also a Gumbel distribu�on), and EWTS II.4 Table 3 includes Vref as presented in Table 2, plus Ve50, the maximum measured or probable 3-second average or gust wind speed in a 50-year return period. Ve50 is defined as Vref x 1.4 (for equivalent height), though the later is not part of the IEC 61400-1, 3rd edi�on simplified classifica�on noted in Table 2. Table 3: IEC 61400-1, 3rd edition, extreme wind classes Wind Class I II III S Vref (m/s) 50.0 42.5 37.5 Designer spec. Ve50 (m/s) 70.0 59.5 52.5 4 EWTS II (European Wind Turbine Standards II) does not consider peak winds, rather only mean wind speed and Weibull k value. V3 Energy, LLC Page | 8 Dillon Pla�orm The GWA only predicts Vref or Ve50 qualita�vely, hence numerical informa�on for IEC classifica�on must be derived from wind measurement with a met tower or Lidar. Cook Inlet is exposed to occasional tropical-origin North Pacific storms, though the energy of the storms typically diminishes further up the inlet compared to the mouth. These storms can yield high extreme wind speeds, even at loca�ons with moderate mean winds. Besides mean wind speed, calcula�ng extreme wind probability from met tower or Lidar data is the primary objec�ve of the wind data collec�on effort as the extreme wind classifica�on (I, II, III, S) determines wind turbine model op�ons and subsequent wind turbine capacity factor (which equates to annual energy produc�on). That noted, the GWA graphically predicts Class II-C (or 2C) winds at Dillon Pla�orm (see Figure 7). Figure 7: Dillon Platform, GWA-modeled IEC Class 2C fatigue loads at 100-meter level Turbulence Intensity The turbulence intensity (TI) is a dimensionless number defined by the standard devia�on (σ) of the wind speed within each �me step (10 minutes for wind power analysis) divided by the mean wind speed (V) over that �me step. 𝑇𝑇𝑇𝑇= 𝜎𝜎𝜎𝜎𝑉𝑉𝜎𝜎� IEC 61400-1, 3rd ed., defines turbulence categories based on mean turbulence intensity at a fixed wind speed of 15 m/s (see Table 4). Table 4: IEC 61400-1, 3rd edition, turbulence categories Turb. Category S A B C TI at 15 m/s >0.16 0.14-0.16 0.12-0.14 <0.12 V3 Energy, LLC Page | 9 Dillon Pla�orm As with Vref or Ve50, the GWA only predicts TI qualita�vely, hence numerical data for IEC classifica�on must be obtained from wind measurement with a met tower or Lidar. Note however that with a very long fetch of open water in the prevailing wind direc�ons, it is exceedingly unlikely that turbulence intensity at Dillon Pla�orm hub height eleva�ons will be in the higher S, A, or B categories. As one would an�cipate and as noted in Figure 7, the GWA predicts turbulence category C winds at Dillon Pla�orm. Wind Shear A wind shear, or power law, exponent α is calculated by the following equa�on where V = wind speed and Z = height above ground level. α=0 would indicate no wind shear and α=0.2 would indicate very high wind shear.5 𝑉𝑉(𝑧𝑧)=𝑉𝑉(ℎ𝑢𝑢𝑢𝑢)× �𝑍𝑍𝑍𝑍ℎ𝑢𝑢𝑢𝑢��𝛼𝛼 Dillon Pla�orm The GWA predicts mean wind speed at five levels: 10, 50, 100, 150, and 200 meters. Using the previously referenced 50- and 100-meter level mean winds speeds and calcula�ng for α using the wind shear equa�on, a power law exponent of 0.122 is derived. Using predicated mean wind speed at the higher heights of 100 meters and 150 meters and calcula�ng for α, a power law exponent of 0.100 is derived. Either is low and highly desirable for wind power development. Another method to assess wind shear is via roughness length, which is an alternate measure of the rate which wind speed changes with height above ground level.6 The GWA-predicted roughness length for Dillon Pla�orm is 0.0002 meters (refer to Figure 8), which translates to a terrain descrip�on of “calm, open sea.” This too is indica�ve of low wind shear, which is highly desirable for wind turbine opera�ons. Figure 8: Dillon Platform, GWA-predicted roughness length (in meters) 5 High wind shear results in considerable mechanical stress from torquing of the main rotor shaft as aerodynamic lift will be higher at the top of the rotor swept area than at the bottom. 6 Windographer software Help. V3 Energy, LLC Page | 10 Wake Turbulence For comparison with the normal turbulence model, IEC 61400-1 suggests an effec�ve turbulence intensity, which is an ideal turbulence independent on wind direc�on and expected to cause the same fa�gue damage as variable turbulence in winds from all direc�ons. The effec�ve turbulence intensity includes added turbulence from wakes of neighbor turbines.7 Dillon Pla�orm Neither the GWA or wind resource measurement with a met tower or Lidar predict or measure wake turbulence by themselves. Rather this is es�mated with reference to prospec�ve wind farm turbine array layout designs. For HEA’s Cook Inlet project, presuming only one wind turbine per pla�orm and considering that the pla�orms are fixed loca�ons, inter-turbine distances are presently known. The Baker, A, C, and Dillon pla�orms are not exactly equidistant but average about 3,500 meters. For sake of example, a General Electric Haliade 150-6 MW offshore wind turbine 8 has a rotor diameter of 150 meters, which, for a 3,500-meter inter-turbine distance, is 23 rotor diameters. This is well above the minimum recommended spacing distance for wind turbines, whether perpendicular to or parallel with the GWA -predicted prevailing winds (refer to Figure 5).9 Flow Inclina�on A wind flow vector not exceeding 8 degrees from horizontal (plus or minus). Dillon Pla�orm The GWA does not predict wind flow angle, or inclina�on, hence this informa�on for IEC classifica�on must be derived from wind measurement with a met tower or Lidar. It is exceedingly unlikely however that wind crossing a long fetch of water such as Cook Inlet will deviate significantly from 0° at the power- producing wind speeds of 4 m/s to 25 m/s. Wind Distribu�on A wind speed, or histogram, where a Weibull func�on10 yields a unitless shape factor (k) of 2.0 (known as a Rayleigh distribu�on) or less. Dillon Pla�orm The GWA does not predict wind speed distribu�on or histogram (Weibull shape factor), hence this informa�on for IEC classifica�on must be derived from wind measurement with a met tower or Lidar. It is highly unusual, however, to observe wind speed distribu�ons with Weibull k > 2.0 in loca�ons outside mountainous areas where bi-modal wind variability 11 is occasionally observed. 7 The IEC 61400-1 turbine safety standard - WAsP. 8 Haliade 150-6MW Offshore Wind Turbine | GE Renewable Energy. 9 The Danish Wind Energy Association says: “As a rule of thumb, turbines in wind parks are usually spaced between 5- and 9-rotor diameters apart in the prevailing wind direction, and between 3- and 5-rotor diameters apart in the direction perpendicular to the prevailing winds.” WAsP software help section. 10 Weibull distribution - Wikipedia. 11 Two wind speed peaks: a normal lower peak and a secondary high peak V3 Energy, LLC Page | 11 Wind Turbine Energy Yield Lidar data – the longer �me the beter so annual variability can be assessed with probability predic�ons P90, P50, etc. 12 for annual energy produc�on – will enable HEA to es�mate annual energy produc�on (AEP) with a high degree of confidence, but for ini�al modeling the GWA includes capacity factor es�mates for representa�ve-type IEC Class I, II, and III turbines. Because the GWA predicts an IEC Class II- C (or 2C) wind resource at Dillon Pla�orm (refer to Figure 7), the predicted IEC Class II wind turbine gross capacity factor13 at Dillon Pla�orm is 0.45 (at the 150 meter level, a possible hub height of wind turbines constructed on the pla�orm deck). To convert to (gross) AEP, mul�ply by 8,760 hours/year. So, for a 3 MW wind turbine at the reference hub height: 𝐴𝐴𝐴𝐴𝑃𝑃=3 𝑀𝑀𝑀𝑀 × 0.45 × 8,760 ℎ𝑟𝑟 𝑦𝑦𝑟𝑟= 11.8 𝐺𝐺𝑀𝑀ℎ/𝑦𝑦 Figure 9: Dillon Platform, GWA-predicted IEC Class II wind turbine capacity factor, 150-meter level For reference, the GWA predicts a 0.41 gross capacity factor for an IEC Class I wind turbine at Dillon Pla�orm (at 100 meters). For a 3 MW capacity wind turbine, this equates to 10.8 GWh/y AEP. For an IEC Class III wind turbine, the GWA predicts a 0.48 gross capacity factor, which for a 3 MW capacity wind turbine equates to a 12.6 GWh/y AEP. Baker, A, and C Pla�orms At Baker Pla�orm, the furthest north of the four pla�orms, the GWA predicts a 7.76 m/s mean wind speed and 717 W/m2 WPD at the 100-meter level, which are only slightly higher than those predicted at Dillon Pla�orm. Considering an IEC Class II wind turbine at the 150-meter level, the GWA predicts an equivalent 0.45 gross capacity factor at Baker, A, and C Pla�orms as at Dillon Pla�orm. A wind turbine located at Baker Pla�orm may have a slightly higher gross capacity factor and hence AEP, but the 12 Probability of Exceedance, see Terminology explained: P10, P50 and P90 (dnv.com) for explanation. 13 Mean power output divided by rated power. V3 Energy, LLC Page | 12 difference would be likely be minimal and probably offset by higher development costs as Baker Pla�orm is further offshore than Dillon Pla�orm and hence further away from Bernice Lake Substa�on, thus requiring longer electrical distribu�on connec�on. For planning purposes at this stage of the project, it is reasonable to assume equivalent wind turbine AEP at Dillon, Baker, A, and C Pla�orms, and given their distance from each other, negligible wake effects should all four be developed.