Assessing Risk in Fixing Wind Energy Rates
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Wind turbines are an increasing source of renewable electricity generation. This increase can be attributed in part to growth in voluntary renewable energy markets. Those who contribute to the voluntary market before a potential crossover point (where the cost of renewable energy is less than that of after than non-renewable generation) are providing a benefit to all who use the energy market in the future, because contributions to the voluntary market allow for the purchase of renewable capacity that will be operational for several decades. In order for contributors to the voluntary energy market to financially benefit from their contributions, the cost of wind energy could be fixed by establishing a green rate. In order to accurately fix a green rate based on wind, the uncertainty regarding monthly wind energy generation and capacity availability must be analyzed. This study explored methods that estimate monthly wind energy generation and capacity values with associated levels of certainty on those values. The results suggest that even with robust data sets (20+ years), the sample size of the monthly data is too low to accurately estimate the quantiles needed for the 95% confidence level. The bootstrap method of simulating additional months succeeded in more completely populating the extreme quantiles and succeeded when compared with the holdout year. In regards to capacity, this study suggests that no capacity can be counted on from a single wind facility at a 95% level of certainty. In summary, it is concluded that a single wind project?s contribution to a fixed green rate product must exclude any capacity claims and be that of an energy-producing asset only and that the P99.6 quantile should be utilized for confidently estimating monthly energy generation
Wind turbines--Economic aspects.
University of Wisconsin-Green Bay. Dissertations. "A thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Environmental Science and Policy." Includes bibliography. Illustrations and maps. Approved: Dr. John Katers, Major Professor and Dr. Gregory J. Davis, Director of Graduate Studies; thesis committee members: Dr. Steven J. Meyer, Robert D. Benninghoff. LC classification: TJ828. Print version: OCLC#884963872.