Shadow Flicker Assessment for Wind Projects in Kingston, MA

Transcription

1 Shadow Flicker Assessment for Wind Projects in Kingston, MA June 10, 2013 Submitted To: Peter McPhee Project Manager, MassCEC 55 Summer St, 9 th Floor Boston, MA Author: Checked By: Elizabeth King, Wind Analyst Brandon Storm, PhD Senior Meteorologist Tel: pmcphee@masscec.com Approved By: Bob Sherwin, Partner

2 Legal Notice and Disclaimer This report was prepared by EAPC Wind Energy Services, LLC (EAPC) expressly for the benefit of the client. Neither EAPC nor any person acting on their behalf: (a) makes any warranty, express or implied, with respect to the use of any information or methods disclosed in this report; or (b) assumes any liability with respect to the use of any information or methods disclosed in this report. Any recipient of this document, by their acceptance or use of this document, releases EAPC, its members, its parent corporations or partnerships, and its affiliates, from any liability for direct, indirect, consequential, or special loss or damage whether arising in contract, warranty, express or implied, tort or otherwise, and irrespective of fault, negligence, and strict liability. The responsibilities for the applications and use of the material contained in this document remain solely with the client EAPC Wind Energy Services, LLC Report Update EAPC bears no responsibility to update this report for any changes occurring subsequent to the final issuance of this report. i

3 TABLE OF CONTENTS Executive Summary Introduction Site Overview Shadow Flicker Background Study Methodology Results of Shadow Flicker Analysis Results of Line-of-Sight Field Survey Possible Turbine Curtailment Scenarios Conclusions LIST OF TABLES Table 1: Kingston wind turbine specifications for shadow flicker... 6 Table 2: Sunshine probability for Boston, MA (%)... 7 Table 3: Number of receptors in shadow flicker scenarios... 9 Table 4: Results for receptors over 30 h/yr theoretical worst case Table 5: Turbine curtailment scenarios LIST OF FIGURES Figure 1: Map of the study area... 3 Figure 2: Map of realistic case hours per year flicker... 9 Figure 3: Map of receptors used for curtailment study LIST OF APPENDICES Appendix A: Wind Turbine Coordinates Appendix B: Shadow Flicker Maps Appendix C: Receptor Coordinates and Flicker Results Appendix D: Line-of-sight Study Appendix E: WindPRO Shadow Flicker Report ii

4 Executive Summary EAPC was asked to perform a shadow flicker study for five operating wind turbines installed as three separate projects in the town of Kingston, Massachusetts. This study was conducted using computer modeling combined with on-the-ground verification of line-of-sight from public streets. EAPC identified the locations of 1,083 existing buildings and developable parcels within 1,600 meters (m) of the wind turbines at which to model flicker. Receptors representing an example house with dimensions measuring 20 meters in width and 10 meters in height were modeled at each location. All modeled houses were oriented with the widest section directly facing the wind turbines. The shadow flicker model identified 808 receptors theoretically affected by shadow flicker from all turbines without accounting for tree cover, neighboring buildings or any other obstacles. Of these, 188 receptors are expected to realistically receive greater than 10 flicker hours per year and 54 receptors are expected to realistically receive greater than 30 flicker hours per year. EAPC also analyzed how many hours each wind turbine would likely need to be curtailed to reduce shadow flicker to no more than 10 and 30 hours per year at any existing residential structure. To remain under these thresholds, each turbine would have to be curtailed a minimum of 6 hours and a maximum of 336 hours per year. 1

5 1. INTRODUCTION The Massachusetts Clean Energy Center (MassCEC), on behalf of the Board of Health of Kingston, engaged EAPC Wind Energy (EAPC) to conduct a shadow flicker analysis for five wind turbines located in Kingston, Massachusetts. The turbines were installed as three separate projects. Three Gamesa G90 wind turbines are sited on private land and constitute the No Fossil Fuel (NFF) wind project. The Kingston Wind Independence (KWI) project includes one Hyundai HQ2000-WT86 turbine sited on the capped Kingston landfill. One Northern Power N100 turbine is installed at the Kingston MBTA station (MBTA). All of the turbines are installed within 1.5 kilometers of one another. Shadow flicker is assessed in this study through a desktop simulation and field verification of direct line-of-sight from public streets. EAPC identified receptors on each property within the rough extent of theoretical shadowing from the turbines. Both theoretical worst case and realistic case analyses were performed. The theoretical worst case model identifies all areas that could possibly experience shadow flicker given the size and shape of the turbines, terrain of the land around them and sun angles throughout the year. The realistic scenario incorporates operational hours for each turbine to more precisely model when the turbine is likely to be spinning and also the angle at which the rotor is oriented. Sunshine probability is also included as a realistic model variable because shadow flicker can only occur when the sun is shining with no cloud cover. Lastly, EAPC determined how many hours the operation of each turbine would need to be curtailed to hold shadow flicker exposure under an example threshold for any receptor identified as an existing residential building. 2. SITE OVERVIEW The study area is located in Kingston and a small portion of neighboring Plymouth. The wind projects are located about 50 kilometers southeast of Boston and 1.5 kilometers west of Cape Cod Bay. The wind turbine sites are surrounded by residential areas to the east, north, and west. A shopping complex is located to the southeast. Massachusetts Route 3, a divided highway, runs through the eastern side of the study area. The turbine locations have an elevation ranging from meters ( feet) above mean sea level. A map of the project location is shown in Figure 1. 2

6 Figure 1: Map of the study area 3. SHADOW FLICKER 3.1. BACKGROUND Shadow flicker from wind turbines occurs when rotating wind turbine blades move between the sun and the observer. Shadow flicker is generally experienced in areas near wind turbines where the distance between the observer and wind turbine blade is short enough that shadow flicker is noticeable. When the blades rotate this shadow creates an effect known as shadow flicker. If the blade s shadow is passing over the window of a building it will have the effect of increasing and decreasing the light intensity in the room at a frequency in the range of 0.5 to 1.2 Hz, hence the term flicker. This flickering effect can also be experienced outdoors, but the effect is typically less intense and becomes even less intense when farther from the wind turbine causing the flicker. The moving shadow of a wind turbine blade on the ground is similar to the effect one experiences when driving on a road when there are shadows cast across the road by an adjacent row of trees. The flickering effect is most noticeable within approximately one kilometer of the turbine, and becomes more diffused as distance increases. There are no uniform standards defining what distance from the turbine is regarded as an acceptable limit beyond which the shadow flicker is considered to be insignificant. There are also no uniform standards in the U.S. for the number of hours of flicker that are deemed to be acceptable. 3

7 Shadow flicker is typically greatest in winter months when the angle of the sun is lower and longer shadows are cast. However, total flicker hours are decreased in these months because there are fewer daylight hours. The effect is more pronounced around sunrise and sunset when the sun is near the horizon and shadows are longer. A number of factors influence the amount of shadow flicker experienced at each shadow receptor (simulated viewing point). One consideration is the environment around the shadow receptor. Obstacles such as terrain, trees or buildings between the wind turbine and the receptor can significantly reduce or eliminate shadow flicker effects. Deciduous trees may block some degree of shadow flickering depending on the tree density, species present and time of year. They can lead to a reduction of shadow flicker during the summer when the trees are bearing leaves. However, during the winter months, these trees are without their leaves and their impact on shadow flicker is not as significant. Thick coniferous trees may provide shading year round. Forestry information was not included in either the theoretical worst case or realistic case shadow flicker models. EAPC was asked to perform a field study which assessed the visibility of turbines from all streets within the study area. The role of tree foliage in screening views of the turbines from publicly-accessible portions of the study area was investigated through this field study. Another consideration for describing the effects of shadow flicker is the time of day when shadow flicker occurs. For example, a factory or office building would not be significantly affected if all the shadow flicker impact occurred before or after business hours. The climate also needs be considered when assessing shadow flicker. In areas with high incidence of overcast weather there is less shadow flicker. Also, if the wind is not blowing the turbines are not operational and do not cause shadow flickering STUDY METHODOLOGY Shadow flicker modeling was performed using WindPRO, a sophisticated wind modeling software program. WindPRO is used to calculate detailed shadow flicker maps across an area of interest with a 1,500 meter distance limit, or at site-specific locations using shadow receptors. Shadow maps, which indicate where shadows will be cast and for how long, can be calculated at varying resolutions. Fine resolution was used for this study; it represents shadow flicker calculations that determine the sun angle every 3 minutes, every 3 rd day, over the period of an entire year, over a grid resolution of 10 meters by 10 meters. Point-specific shadow flicker calculations are modeled at a higher resolution than the shadow flicker maps to include the highest precision possible within WindPRO. Shadow flicker at each shadow receptor location is calculated every minute of 4

8 every day throughout the entire year. Shadow receptors can be configured to represent an omni-directional window of a specific size (greenhouse mode) or a window facing a single direction of a specific size (single direction mode). The shadow receptors used in this analysis were configured as greenhouse-mode receptors representing a 20 meter wide by 10 meter high window. This represents exposure to the full façade of a large house directly facing each turbine, and thus produces a conservative estimate of shadow flicker impact on each building. Shadow flicker exposure is recorded by the model if the turbine casts a shadow on any part of this receptor during any minute of any day throughout the year. As part of the calculation method, WindPRO must determine whether or not a turbine will be visible at the receptor locations due to local topography. It does this by performing a preliminary Zones of Visual Influence (ZVI) calculation using a terrain model with 10 meter grid spacing. If a particular turbine is not visible within the 10 meter x 10 meter area containing the shadow receptor, that turbine is not included in the shadow flicker calculation for that receptor. The inputs for the WindPRO shadow flicker model include the following: Turbine Coordinates Turbine Model Specifications Shadow Receptor Coordinates 10 meter USGS Digital Elevation Model (DEM) (height contour data) Sunshine Probability (for the realistic case) Annual Wind Speed and Direction Frequency (for the realistic case) A description of each input variable and how they affect the shadow flicker calculation are included below. Turbine Coordinates: The intensity of the shadow flicker is partially dependent upon the distance from the wind turbine. The coordinates and elevations of the wind turbines used in this study are included in Appendix A. Turbine Specifications: A wind turbine s total height and rotor diameter are included in the WindPRO shadow flicker model. The taller the wind turbine, the more likely shadow flicker could have an impact on local shadow receptors as the likelihood of clearing terrain obstacles is greater. The larger the rotor diameter, the wider the area where shadows will be cast. Also included with the turbine specifications are the cut-in and cut-out wind speeds within which the wind turbine is operational. If the wind speed is below the cut-in threshold or above the cut-out threshold, the turbine rotor will not be spinning and thus shadow flicker will not occur. The specifications of the turbine model used in this study were provided by the client and are included in Table 1. The Gamesa hub heights could not be verified as 78 or 80 meters. EAPC used an 80 meter hub height to provide a more conservative estimate of shadow flicker potential. 5

9 Table 1: Kingston wind turbine specifications for shadow flicker Manufacturer Model Project Hub Height (m) Rotor Diameter (m) No. of Turbines Gamesa G90 NFF Hyundai HQ2000 KWI Northern Power N100 MBTA Shadow Receptor Coordinates: The position of a shadow receptor in relation to a wind turbine is a primary factor in determining the impact of shadow flicker. EAPC used a thorough process to identify 1,083 receptors within a study area defined as follows. Theoretical worst case shadow flicker modeling was used to create a shadow flicker map describing the maximum extent of the area around the five wind turbines which could experience one hour or more of shadow flicker each year. A 100 meter buffer was drawn outside the shadow flicker extent to ensure that all potentially affected areas were accounted for. The whole area encompassed by this buffer was considered the study area. The majority of this area falls within 1.5 kilometers of at least one turbine; this radius is EAPC s benchmark threshold for shadow flicker analysis because atmospheric refraction tends to make the effect of shadow flicker beyond this distance negligible. Parcel data acquired from MassGIS were used as the primary unit of analysis for identifying receptor points. 1 Parcels in the Town of Kingston were last updated in May, Parcels in the Town of Plymouth were last updated in August, Using a geographic information system (GIS), points were drawn at the centroid of each parcel intersecting the study area. Aerial photography from Bing Maps, accessed via ArcGIS and originally captured between March and October of 2011 (prior to installation of any of the five Kingston turbines), were used to visually inspect each portion of the study area using a gridded search pattern. For parcels where a primary structure was visually identifiable, points were moved by hand to the approximate center of the primary structure. For residential parcels, primary structures were identified as the most likely candidate for the house of other residential structure. In most cases these were the largest structures on the property. In several cases where condominium structures were evident, a point was placed near the center of each visually identifiable structural unit. On commercial parcels, a single point was placed on either the largest structure or the structure where the most human activity seemed to take place based on visual interpretation of the aerial imagery. On the Indian Pond Country Club parcel, a point was 1 MassGIS. (2013). Level 3 Assessors Parcel Mapping. Executive Office for Administration and Finance. State of Massachusetts. Available Online at: 6

10 placed on each visually identifiable major structure. On the Independence Mall parcel, a point was placed on each visually identifiable section of the mall building. Except for the aforementioned cases, each parcel was identified with one receptor point in an effort to equitably represent the effect of shadow flicker on each property owner. Several exceptionally small parcels and parcels containing primarily wetlands and water bodies were omitted from analysis and were not assigned receptors. Parcels containing the wind turbines, as well as directly adjacent undeveloped or industrial properties owned by the wind turbine operators and by the Town of Kingston were also omitted. Coordinates and other identifying information for each receptor are included in Appendix C. USGS Digital Elevation Model (DEM) (height contour data): For this study, 10 meter USGS National Elevation Database (NED) DEMs were used to construct height contour lines with an interval of approximately 3 meters (10 feet) for the WindPRO shadow flicker model. The height contour information is important to the shadow flicker calculation because it allows the model to place the wind turbines and the shadow receptors at the correct elevations. The height contour lines also allow the model to account for the topography of the site when calculating zones of visual influence surrounding the wind turbine and shadow receptor locations. Sunshine Probability: Shadow flicker is only produced when the sun is shining. To model a more realistic scenario, EAPC input sunshine data to estimate shadow flicker hours. Using data from the National Climatic Data Center (NCDC), EAPC assumed sunshine percentages shown in Table 2. Table 2: Sunshine probability for Boston, MA (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Wind Data: Shadow flicker affects the greatest area when sun is shining on the turbine perpendicular to the plane of the spinning blades. To model a more realistic scenario, EAPC input meteorological data for Kingston. Data were provided by the UMass Amherst Wind Energy Center website for July 12, 2005 through July 12, EAPC used this 50 meter wind speed and direction dataset to calculate the operational hours for each of the five wind turbines. EAPC was also asked to perform on-the-ground verification of line-of-sight to any of the five wind turbines from street segments within the study area. Two members of the EAPC team visited Kingston on May 15, They drove each street segment within the study area and noted the areas where one or more 7

11 turbines were visible. They also took photographs to demonstrate the view of turbines from key vantage points along many of the segments. A map reporting the results of this survey, along with associated photographs, is included in Appendix D. EAPC did not identify line-of-sight to turbines from the location of individual receptors. Such a process would have necessitated permission from landowners to access receptor locations on private property. Results from the line-of-sight study did not contribute to quantitative modeling of shadow flicker at receptors, to shadow flicker maps, or to curtailment maps RESULTS OF SHADOW FLICKER ANALYSIS The term theoretical worst case, as used in this report, means that turbine operational hours, wind direction, and local sunshine probabilities have not been accounted for. As such, theoretical worst case estimates are conservative. The term realistic, as used in this report, means that turbine operational time, rotor orientation, and sunshine probabilities are factored into the model. Blocking or shading effects due to trees or structures have not been accounted for. Both theoretical worst case and realistic values are estimates based on model inputs. A total of 1,083 receptors (primary structures and parcel centroids) were analyzed, and a fine resolution shadow flicker map was generated for both theoretical worst case and realistic modeling scenarios. The fine resolution shadow flicker map is included in Appendix B and also shown below in Figure 2. 8

12 Figure 2: Map of realistic case hours per year flicker WindPRO calculated the hours per year, days per year, and minutes per day of shadow flicker. A table showing the results for all receptors can be found in Appendix C. A WindPRO report showing the exact hours for those receptors of concern can be found in Appendix E. Table 3 shows the number of receptors that fall above three thresholds: 1) receptors modeling over 10 hours per year in the realistic study, 2) receptors modeling over 30 minutes per day in the theoretical worst case study, and 3) receptors modeling over 30 hours per year in the theoretical worst case study. Table 3: Number of receptors in shadow flicker scenarios Greater Than 10 h/yr, Realistic Case Greater Than 30 min/dy Maximum, Theoretical Worst Case Greater Than 30 h/yr, Theoretical Worst Case All Receptors Existing Residential Structure Receptors Table 4 shows the results for receptors over 30 hours per year theoretical worst case. 9

13 Table 4: Results for receptors over 30 h/yr theoretical worst case Parcel Address Map Index Shadow Flicker Time per Year (hh:mm) 10 Theoretical Worst Case Shadow Flicker Days per Year Maximum Shadow Time per Day (h:mm) Realistic Case Shadow Flicker Time per Year (hh:mm) 2 Schofield Rd, Kingston C : :46 88:26 2 Schofield Rd, Kingston C : :45 84:41 2 Schofield Rd, Kingston C : :46 80:37 49 Prospect St, Kingston B : :06 75:41 2 Schofield Rd, Kingston C : :45 77:52 2 Schofield Rd, Kingston C : :45 75:42 50 Prospect St, Kingston B : :41 70:34 2 Schofield Rd, Kingston C : :45 74:44 2 Schofield Rd, Kingston C : :43 71:20 2 Schofield Rd, Kingston C : :42 69:58 2 Schofield Rd, Kingston C : :42 69:10 47 Prospect St, Kingston B : :58 63:22 2 Schofield Rd, Kingston C : :41 68:19 2 Schofield Rd, Kingston C : :40 66:30 2 Schofield Rd, Kingston C : :40 65:59 48 Prospect St, Kingston B : :31 57:24 43 Prospect St, Kingston B : :44 54: Country Club Wy, Kingston C : :17 43:10 2 Schofield Rd, Kingston C : :30 57:19 2 Schofield Rd, Kingston C : :30 56:19 2 Schofield Rd, Kingston C : :29 55:23 2 Schofield Rd, Kingston C : :29 54:05 2 Schofield Rd, Kingston C : :28 51:41 2 Schofield Rd, Kingston C : :27 51:04 4 Royson Dr, Kingston D : :08 46:43 2 Schofield Rd, Kingston C : :27 50:06 2 Schofield Rd, Kingston C : :26 48:50 46 Prospect St, Kingston B : :24 45: Country Club Wy, Kingston D : :01 34: Country Club Wy, Kingston D : :00 35:33 3 Leland Rd, Kingston B : :19 45:12 40 Prospect St, Kingston B : :17 40:34 11 Leland Rd, Kingston C : :18 41:31 38 Prospect St, Kingston B : :13 36:40 36 Prospect St, Kingston B : :12 34:03 4 Leland Rd, Kingston B : :09 35: Country Club Wy, Kingston D : :58 28: Country Club Wy, Kingston D : :55 26:17 24 Raboth Rd, Kingston E5-3 88: :55 28:51 37 Prospect St, Kingston B : :21 30: Country Club Wy, Kingston D : :52 25:17 14 Copper Beech Dr, Kingston B : :58 21:51 12 Leland Rd, Kingston C7-4 84: :08 32: Pond View Dr, Kingston D : :55 26: Country Club Wy, Kingston D4-2 78: :47 22:40 28 Raboth Rd, Kingston E5-5 74: :03 24:31 32 Prospect St, Kingston B : :10 25: Pond View Dr, Kingston D : :57 25: Country Club Wy, Kingston D : :46 20:05 39 Forest St, Kingston E : :48 27:40 13 Copper Beech Dr, Kingston B : :50 17: Country Club Wy, Kingston D4-4 67: :44 20:07 16 Copper Beech Dr, Kingston B : :56 18: Country Club Wy, Kingston C4-9 64: :49 18: Country Club Wy, Kingston D : :48 19: Pond View Dr, Kingston E3-3 60: :53 21:56 35 Prospect St, Kingston B7-9 60: :13 20:52

14 Parcel Address Map Index Shadow Flicker Time per Year (hh:mm) Theoretical Worst Case Shadow Flicker Days per Year Maximum Shadow Time per Day (h:mm) Realistic Case Shadow Flicker Time per Year (hh:mm) 228 Country Club Wy, Kingston D : :42 16:58 40 Forest St, Kingston E : :43 22:51 4 Ocean Hill Dr, Kingston C : :40 18:56 2 Anderson Av, Kingston C : :44 18:34 8 Anderson Av, Kingston C : :44 18: Country Club Wy, Kingston C4-7 55: :52 16:19 8 Ocean Hill Dr, Kingston C : :39 18:10 11 Copper Beech Dr, Kingston B : :50 13:17 3 Anderson Av, Kingston C : :45 17: Pond View Dr, Kingston D : :45 17:44 95 Pond View Dr, Kingston E3-9 51: :47 19: Country Club Wy, Kingston D : :43 15:44 32 Forest St, Kingston E : :42 19:54 15 Copper Beech Dr, Kingston B : :49 13:26 55 Forest St, Kingston E : :45 18:37 55 Pond View Dr, Kingston E : :38 19:07 12 Ocean Hill Dr, Kingston C : :37 15:40 18 Copper Beech Dr, Kingston B : :53 14:16 12 Copper Beech Dr, Kingston B : :53 11:45 56 Smiths Ln, Kingston C : :43 15: Country Club Wy, Kingston D4-8 44: :40 14: Pond View Dr, Kingston E3-1 44: :44 15:57 87 Pond View Dr, Kingston E : :40 17: Country Club Wy, Kingston D3-9 43: :37 12:34 48 Pond View Dr, Kingston E : :35 17:31 47 Pond View Dr, Kingston E : :37 17:25 20 Copper Beech Dr, Kingston C6-3 42: :51 13: Country Club Wy, Kingston D : :39 12: Country Club Wy, Kingston C4-6 40: :45 12:01 16 Ocean Hill Dr, Kingston C : :35 13:10 17 Copper Beech Dr, Kingston B : :48 11:57 33 Prospect St, Kingston B : :02 13:56 48 Forest St, Kingston E : :40 15:30 31 Forest St, Kingston E : :37 16:21 7 Autumn Ln, Kingston D : :39 13:27 9 Orchard Av, Kingston C : :41 12:39 96 Pond View Dr, Kingston E3-6 38: :40 13:56 20 Ocean Hill Dr, Kingston C : :33 12:07 72 Pond View Dr, Kingston E : :34 14:27 54 Smiths Ln, Kingston C : :42 12:13 63 Pond View Dr, Kingston E : :38 13:50 11 Orchard Av, Kingston C : :39 11:29 19 Copper Beech Dr, Kingston B : :47 10: Country Club Wy, Kingston D3-6 34: :33 10:04 24 Ocean Hill Dr, Kingston C : :31 11: Wolf Pond Rd, Kingston D : :35 10:56 57 Smiths Ln, Kingston C : :42 11:57 8 Raboth Rd, Kingston E6-5 33: :37 10:48 10 Anderson Av, Kingston C : :35 11:22 52 Smiths Ln, Kingston C : :41 11:11 2 Autumn Ln, Kingston D : :35 10:39 64 Pond View Dr, Kingston E : :34 12: Country Club Wy, Kingston D : :34 9:57 28 Ocean Hill Dr, Kingston C : :30 10:31 56 Pond View Dr, Kingston E : :35 12:33 88 Pond View Dr, Kingston E : :36 11:48 13 Orchard Av, Kingston C : :38 10: Wolf Pond Rd, Kingston D : :35 10: Wolf Pond Rd, Kingston D : :35 10:52 11

15 Parcel Address Map Index Shadow Flicker Time per Year (hh:mm) Theoretical Worst Case Shadow Flicker Days per Year Maximum Shadow Time per Day (h:mm) Realistic Case Shadow Flicker Time per Year (hh:mm) 10 Raboth Rd, Kingston E6-4 30: :38 9:45 16 Hemlock St, Kingston E : :30 12:01 12 Anderson Av, Kingston C : :32 10:16 4. RESULTS OF LINE-OF-SIGHT FIELD SURVEY The majority of street segments with clear line-of-sight to any of the five Kingston turbines are in the central and eastern portions of the study area. Most roadways in the central portion of the study area, which has relatively little tree canopy and is comprised mostly of commercial and industrial land uses (the Independence Mall, other large commercial developments, and several car dealerships among other businesses) have direct line-of-sight to one or more of the turbines. Only small segments of roadway in the western residential portion of the study area have views to the turbines, particularly the NFF turbines directly to the east. In many cases these views are partially screened by trees, the majority of which are tall conifers and thus provide year-round screening. The MBTA turbine is visible only from nearby segments of Country Club Way. These segments, on the eastern end of the Country Club Way loop, also provide ample view of the KWI and NFF turbines. The eastern portion of the study area includes many residential areas with direct line-of-sight to the KWI turbine, including large portions of Prospect Street and adjacent connecting streets. While there are many coniferous trees that may provide some screening from shadow flicker, these streets are close enough to the KWI turbine that large portions of the blade swing are visible above the tree canopy. The shadow flicker impact on individual houses along this street is likely to be highly affected by the location of individual trees relative to the receptor. The relative proximity of the KWI turbine to this neighborhood means that shadow edges will be crisper (shadow edges blur with distance from the shadowing object) and may cause noticeable shadow flicker even through a thin tree canopy. While in many places line-of-sight from the roadway is representative of potential for line-of-sight from adjacent receptors, there are clear exceptions. For instance, Copper Beech Drive offers a clear view of a NFF turbine to the southwest, yet many homes along this street are unlikely to have a clear line-of-sight because of the tall hedgerows that separate each lot perpendicular to the street. Conversely, houses on the ridge to the east of Pondview Drive may have a line-of-sight to the NFF turbines although the adjacent road, which is set lower in the terrain, does not. The results of the street survey must be interpreted in conjunction with the bare-ground modeling to gain a clear understanding of which places are 12

16 theoretically vulnerable to shadow flicker and in which of these places the line-ofsight is advantageously blocked by nearby buildings and trees. 5. POSSIBLE TURBINE CURTAILMENT SCENARIOS EAPC has selected two example scenarios in order to model possible curtailment required to meet each scenario. These scenarios are not suggested limits for shadow flicker impacts but are intended to provide insight into the operational impacts of mitigating shadow flicker. They do not include line-of-sight results but can help inform impacts on a case-by-case basis. While the previously presented flicker estimates describe impacts on all types of structures and developable parcels (e.g. residential, commercial and other uses) the curtailment analysis in this section accounts for impacts only to existing residential structures. A map of receptors used for the curtailment study is shown in Figure 3. Figure 3: Map of receptors used for curtailment study 13

17 EAPC calculated how many hours the wind turbines would need to be shut down to keep theoretical worst case exposure to shadow flicker under 30 hours per year for all existing residential structures. As such, only receptors with theoretical worst case exposure greater than 30 hours per year were included in the analysis. A ratio was used to approximate how many curtailment hours would be required to hold realistic shadow flicker under 10 hours per year. A complex Excel workbook was used to calculate when receptors were experiencing shadow flicker cast from multiple turbines simultaneously, for how many minutes each receptor would require turbine curtailment to remain under the shadow flicker threshold, and for how many unique minutes each turbine would need to be curtailed. There is some uncertainty introduced by the semi-manual calculation process. The total flicker hours per turbine as well as the resulting hours each turbine would need to be shut off to reduce theoretical worst case shadow flicker to no more than 30 hours per year or realistic shadow flicker to no more than 10 hours per year are shown below in Table 5. Table 5: Turbine curtailment scenarios Cumulative Flicker Theoretical Worst Case (hr/yr) Curtailment to Achieve Maximum 30 Hours per Year Theoretical Worst Case (hr/yr) Curtailment to Achieve Maximum 10 Hours per Year Realistic Case (hr/yr) NFF West (Gamesa 1) NFF Southeast (Gamesa 2) NFF North (Gamesa 3) KWI (Hyundai) MBTA (Northern Power) CONCLUSIONS The conservative results of this study indicate that of the 1,083 receptors modeled, 275 modeled zero shadow flicker, 186 modeled over 30 hours per year theoretical worst case, and 189 modeled over 10 hours per year realistic. The realistic shadow flicker impacts on receptors were calculated with consideration for turbine operational time and sunshine probabilities. This analysis is based on a number of other conservative assumptions including: A human would always be present at the receptor to observe the effect. A human would be situated in an area where the flickering occurs. The receptors are omni-directional rather than modeling specific building facades or window openings. The overall effect of using these conservative assumptions indicates that the actual number of hours of shadow flicker that would be observed will likely be less than those predicted by this study. 14

18 The curtailment analysis demonstrates that the operation of each turbine would need to be curtailed between 6 (MBTA Northern Power) and 366 (KWI Hyundai) hours to reduce shadow flicker exposure to all receptors representing existing residential structures to no more than 30 theoretical worst case flicker hours per year. These are conservative estimates and would likely change if forestry information was taken into account. EAPC also performed field verification of line-of-sight between any of the five turbines and each public street within the study area. The results of this survey, included in Appendix D, provide further insight on how surface obstacles such as trees and buildings reduce the potential for shadow flicker in large portions of the study area. 15