Kingston, MA Shadow Flicker Study Elizabeth King Wind Analyst Chester Harvey GIS Specialist 256 Farrell Farm Rd. Norwich, VT 05055 Ph: 802. 649. 1511
Goals 1. 2. 3. Estimate shadow flicker time by location Estimate curtailment time required to meet example shadow flicker thresholds Document areas with line-of-sight to turbine(s)
Site Overview • 5 Wind Turbines • 1083 Receptors within 1. 6 km of turbines
Methodology 1. Desktop estimate of shadow flicker exposure – Shadow flicker modeled using Wind. PRO • Incorporates GIS terrain model, daily sun paths based on latitude, local weather data and wind data – – 2. Receptors identified using aerial images & GIS data No tree or building obstacles are accounted for Field documentation of line-of-sight – Assessed by car from public streets
Flicker Modeling • Theoretical Worst Case – – Sun always shining; wind turbines always operating – • Maximum possible shadow hours for a given location Is a step in process for deriving realistic case estimates Realistic Case – Incorporates sunshine probability and likely wind turbine operational hours • Sunshine data, 61 years – Boston, MA (National Climatic Data Center) • Wind data, 1 year (July 05 – July 06) – Kingston, MA (UMass Amherst)
Receptors • 20 meters wide x 10 meters tall – • Intended to simulate the façade of a house Each receptor modeled so that it faces perpendicular to each wind turbine in each iteration of analysis (Greenhouse Mode)
Receptors
Receptors 20 me ters w 10 meters tall Receptor area facing perpendicular to direct line to turbine ide Shadow modeled on receptor area 1. 5 m figure for scale Receptor point at bottom-center of modeled receptor area
Wind. PRO Inputs 9
Wind. PRO Inputs 10
Wind. PRO Inputs 11
Wind. PRO Inputs 12
Isolines show shadow flicker estimates derived from a realistic case model using a 10 m grid resolution Hours per Year at 1. 5 meters above ground level
Wind. PRO Report
Wind. PRO Report
Wind. PRO Report Calendar Graphs Receptor A Receptor B
Wind. PRO Report
Flicker Results Realistic Case More Than 10 Hours per Year of Flicker More Than 30 Hours per Year of Flicker More Than 50 Hours per Year of Flicker Number of Receptors Affected 189 55 31 Number of Existing Residential Receptors Affected 121 38 24
Line-of-Sight Survey • • • Assesses line-of-sight to each turbine from public streets within the study area Accounts for trees and buildings that block line-of-sight to turbines Line-of-sight results are not incorporated into modeling results
Photo 27
Curtailment Analysis • • Only receptors for existing residential structures are included Accounts for coincident flicker across multiple receptors
Curtailment Results Realistic Case Theoretical Worst Case Estimated Curtailment Required for 10 hrs of Flicker Per Year (h/yr) Estimated Curtailment Required for Maximum 30 hrs of Flicker Per Year (h/yr) NFF West (Gamesa 1) 94 286 NFF Southeast (Gamesa 2) 47 140 NFF North (Gamesa 3) 107 326 KWI (Hyundai) 126 366 MBTA (Northern Power) 6 14
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