Wind power is a source of energy that’s each reasonably priced and renewable.
Nevertheless, decision-makers have been reluctant to speculate in wind energy on account of a perception that wind farms require a variety of land in comparison with electric power plants driven by fossil fuels. Research led by McGill University and based on the assessment of the land-use of near 320 wind farms within the U.S. (the most important study of its kind) paints a really different picture.
Misplaced preconceptions concerning the land use of gas-fuelled electricity
The study, which was published recently in Environmental Science and Technology, shows that, when calculations are made, your complete wind farm area is normally regarded as land given over to wind development. Nevertheless, the wind power infrastructure (comparable to the turbines and roads) typically only uses 5 per cent of your complete farmland — the remainder is commonly used for other purposes, comparable to agriculture.
The research also shows that if wind turbines are sited in areas with existing roads and infrastructure, comparable to on agricultural land, they could be roughly seven times more efficient, when it comes to energy produced per square metre of land directly impacted by the infrastructure, than projects which can be developed from scratch.
“The land use of wind farms has often been viewed as among the many predominant challenges to wind development,” explains Sarah Jordaan, an associate professor within the Department of Civil Engineering at McGill and the senior creator on the study. “But, by quantifying the land area utilized by nearly 16,000 wind turbines within the western U.S., we found that gas-fired generation offers no real advantages when it comes to lesser land use when the infrastructures, including all of the wells, pipelines, and roads related to the natural gas supply chain, are considered.”
A brand new approach to future energy technology assessments
It has been difficult to get a transparent picture of the land use related to wind power within the U.S. until now because earlier studies only checked out the infrastructure related to wind energy and land use on a comparatively small scale, making it difficult to extrapolate from their results. Other studies have relied on estimates of your complete wind farm, moderately than the land directly impacted by the infrastructure.
By combining information gathered through GIS (geographic information systems) with machine learning models developed using nearly 2000 images of wind farms from the American portion of the Western Interconnection (which provides electricity to 14 states within the U.S. in addition to to portions of Canada and Mexico), the researchers were in a position to train a deep learning model to investigate land use in wind farms. By doing so, they were in a position to assess a spread of things (placement of turbines, pre-existing roads, age of turbines, etc.) that contribute to the land directly impacted by wind infrastructure.
“The tactic we’ve got developed is potentially useable for future assessments of varied energy technologies, whether when it comes to environmental impact evaluation or energy systems planning for net zero emissions,” adds Jordaan. “In actual fact, it sets the stage for the primary consistent comparisons of environmental sustainability across different energy technologies in future.”
The research was supported by the Alfred P. Sloan Foundation.
