A spatial decision support system evaluating energy and resources for wind turbine development: The case study of Iowa, USA

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesEnergy goals have been set to address climate change and mitigate greenhouse gas emissions in accordance with the Paris climate agreement, a result of the United Nations Convention on Climate Change in 2015. Wind turbines have received a fast-growing interest since they do not produce carbon emissions when converting wind energy to power. It is expected that wind turbines will make up 20 percent of the United States electricity market by 2030 and 35 percent by 2050. A spatial decision support system (SDSS) was developed for a quantitative research study, evaluating wind energy and resources through mathematical modeling and geographic information systems (GIS). The SDSS proposed in the study is comprised of four steps: acquisition of data, resource forecasting, simulation and analysis, and ranking of alternative strategies. The SDSS was then applied to a case study in Iowa, United States for the year 2013. Wind turbine and resource datasets were extracted from the U.S. Wind Turbine Database and WIND Toolkit, respectively. Resources were forecasted using Ordinary Kriging spatial interpolation and Weibull distribution modeling. Weibull parameters were estimated using the power density method. Wind power density, turbine rotor swept area, and the power coefficient were used to simulate power output and capacity factors of presently located wind turbines. Finally, alternative strategies for wind turbine development were ranked based on the estimated energy yields of presently located wind turbines. The results found that most of Iowa exhibits Class III wind speeds, as defined by the International Electrotechnical Commission. Overall, it can be determined that Iowa’s resources are economically suited for wind turbine development