Open Access Thesis
Wind as a renewable and clean source of energy has begun to take a high position in the global dialog about energy production. Today, one of the big questions is to find the most suitable locations for wind farms, with the goal of achieving the highest rates of electricity production possible. In order to find most suitable places to build windfarms, we need to develop multifactor and multiscale dynamic models of windfarm suitability. The interest in the assessment of wind energy suitability in the Russian North regions comes from the expectation of the great potential of wind power in the northern regions in general. The Russian Arctic coastline can be considered one of the largest wind energy areas that provides an opportunity to implement wind energy technology. At the same time, northern communities face challenges of sustainable development associated with limited fuel energy resources. These challenges such as ecological sustainability and the problems of transportation of fuel in the harsh conditions of the North can be alleviated by the wind energy industry.
This research implements an improved wind energy resource characterization and suitability assessment methodology using multi-resolution datasets and a spatial decision support system approach. The wind turbine suitability assessment is based on collection and interpretation of study area environmental characteristics. The developed framework is based on multi-criteria decision systems approach and advanced for the particular study area with its regional features. The framework includes along with basic environmental criteria, such as wind speed or wind power, slope, elevation, proximity to road networks, settlements, protected federal areas etc., parameters specific for the Arctic regions and cold environmental conditions, such as icing losses and permafrost. All those factors are taken into an account for more precise results of wind power assessment for the Arctic territory of Russia.
One of the important results of this research is an improved framework of wind resource characterization, where wind power potential of the study area was calculated for twelve-month using an examination and use of global meteorological reanalysis data. Average annual estimates of wind power potential were adjusted for such possible production impairment factor as icing occurrence and potential losses due to it. The inclusion of this variable influenced the results which tells about an importance of such methodological improvements of using this criteria for wind energy potential estimates.
Wind turbine suitability assessment was completed with the use of appropriate to cold climates multi-criteria decision making system, this system was developed and implemented in this study. Multi-criteria site assessment method included best available data for the Russian Arctic and included 11 criteria for enhanced site selection. One of the new improvements in this research is the use of permafrost as an economic criterion, where risks of wind turbine construction on unstable permafrost were considered. As a result of this study, regional wind power potential and suitability estimates were provided for all eight Russian Arctic regions and showed high potentials of wind energy development. This research included downscaling to the regional-scale process with the use of finer resolution meteorological reanalysis and elevation data for the area of Nenets-Autonomous Okrug. Results of this process showed that downscaled results positively impacted on wind power potential assessments and negatively impacted on suitability site assessment.
The results of this study can be useful for an electric power industry development program in the Arctic region, where alternative energy sources can replace or reduce the use of the traditional fuel resources.
Date of Award
Master of Arts
Department of Geography
Andrey N. Petrov
1 PDF file (x, 124 pages)
©2017 - Narmina Iusubova
Iusubova, Narmina, "A multiscale assessment of wind energy resources and suitability in the Russian arctic" (2017). Electronic Theses and Dissertations. 433.