Wind Power Potential in Interior Alaska from a Micrometeorological Perspective

• 作者: Gary Sellhorst Gerhard Kramm Hannah K. Ross John Cooney Megan Hinzman Nicole M?lders Ralph Dlugi Samuel Smock
• 来源期刊: 大气和气候科学(英文)
• 学科: 医学
• 关键词: WIND POWER POWER Efficiency WIND POWER Potential WIND POWER Prediction WRF/Chem Micrometeorology MOMENTUM Theory Blade Element Analysis Betz Limit Glauert’s Optimum Rotor BALANCE EQUATION for MOMENTUM EQUATION of Continuity BALANCE EQUATION for Kinetic Energy Reynolds’ AVERAGE Hesselberg’s AVERAGE Bernoulli’s EQUATION Integral Equations Weibull Distribution General Logistic Function Eva Creek WIND Farm
• 年，卷（期）: 2014,（1）
• 页码范围: 100-121
• 页数: 22页
• 分类号: R73

摘要

The wind power potential in Interior Alaska is evaluated from a micrometeorological perspective. Based on the local balance equation of momentum and the equation of continuity we derive the local balance equation of kinetic energy for macroscopic and turbulent systems, and in a further step, Bernoulli’s equation and integral equations that customarily serve as the key equations in momentum theory and blade-element analysis, where the Lanchester-Betz-Joukowsky limit, Glauert’s optimum actuator disk, and the results of the blade-element analysis by Okulov and Sorensen are exemplarily illustrated. The wind power potential at three different sites in Interior Alaska (Delta Junction, Eva Creek, and Poker Flat) is assessed by considering the results of wind field predictions for the winter period from October 1, 2008, to April 1, 2009 provided by the Weather Research and Forecasting (WRF) model to avoid time-consuming and expensive tall-tower observations in Interior Alaska which is characterized by a relatively low degree of infrastructure outside of the city of Fairbanks. To predict the average power output we use the Weibull distributions derived from the predicted wind fields for these three different sites and the power curves of five different propeller-type wind turbines with rated powers ranging from 2 MW to 2.5 MW. These power curves are represented by general logistic functions. The predicted power capacity for the Eva Creek site is compared with that of the Eva Creek wind farm established in 2012. The results of our predictions for the winter period 2008/2009 are nearly 20 percent lower than those of the Eva Creek wind farm for the period from January to September 2013.