Excerpts from a 2005 UBC study at http://www.geog.ubc.ca/courses/geob370/students/class05/cskwan/discussion.html

Introduction

“Anticipation of wind power becoming a major energy source throughout the world is more than just dust in the wind. Since the year 2000, the use of wind power has increased 30% annually worldwide, making it the fastest growing energy source (Canadian Wind Energy Association Quick Facts, 2005). The United States and numerous European countries, such as Denmark, Germany, and Holland, have lead the way in wind power development, but the benefits and potential of wind power have piqued the interest of other countries, including China, India, Australia, and Canada (NOVA, 1998). Though approximately 39,000 MW of energy is currently produced through wind power globally, it is estimated that the possible capacity of global wind power is five times this amount (Canadian Wind Energy Association Quick Facts, 2005).

“It would be extremely advantageous for the world’s nations to develop wind power. This energy source boasts many environmental and economic benefits. Unlike traditional energy sources, wind power does not produce hazardous wastes, lacks air and water emissions, does not exploit natural non-renewable resources, and does not create environmental damage through resource removal and haulage (American Wind Energy Association, 2005). Of course, like all energy production, wind power does have some environmental impacts, but these impacts are small and local, which make them easier to detect, supervise, and mitigate (American Wind Energy Association, 2005).

Method

After assessing the 8 outcomes of the Multi-Criteria Evaluation model, and selecting two distinct representations, we were able to identify the best sites by optimal area identification. We compared the 8 outcomes of the Multi-Criteria Evaluation in a sequential fashion in order to identify areas consistently optimal for wind farm development. The comparison is illustrated below in a flash presentation. The variation in suitability is the result of differential factor weights on wind speed, roads, transmission lines and elevational variation.
Multi-Criteria Evaluation Models for Wind Development-Dark areas indicate a higher suitability for wind farm development.-

Two outcomes were selected in order to comprehensively analyze the impact of the factor weights. Models 5 and 6 were chosen because both these outcomes include variation in elevation as a factor input, thus making these models more complete than those without elevation. Moreover, these two models illustrate the most extreme factor weighting schemes.

Two Options for Wind Farm Development in BC

Multi-Criteria Evaluation Model 5
Results Model 5 
Multi-Criteria Evaluation Model 6
Results Model 6 

For further analysis, a map for each model was created, and on each map the 3 areas with the highest factor scores were indicated. For the map of model 5 (wind 70%, roads 10%, transmission lines 10%, elevation variation 10%), these scores were 69.81, 70.49, and 73.17. For the map of model 6 (wind 40%, roads 20%, transmission lines 20%, elevation variation 20%), these scores were 69.82, 70.34, and 71.43.

Sites Most Conducive for Wind Farm Development

Three Optimal Sites Based on Model 5

Model 5 Map

Three Optimal Sites Based on Model 6

Model 6 Map

See the full UBC study at http://www.geog.ubc.ca/courses/geob370/students/class05/cskwan/discussion.html