Optimization of wind farm layout is an expensive and complex task involving several engineering challenges. The layout of any wind farm directly impacts profitability and return of investment. Several software optimization modules in line with wind farm design tools in industry is currently attempting to place the turbines in locations with good wind resources while adhering to the constraints of a defined objective function. Assessment of these software tools needs to be performed clearly for assessing different tools in wind farm layout design process. However, there is still not a clear demonstration of benchmarking and comparison of these software tools even for simple test cases. This work compares two different optimization software namely openWind and WindPRO commercial software tools mutually.

This thesis applies a stochastic programming approach to the techno-economic analysis of a wind farm coupled with a pumped storage facility. The production of an optimal day-ahead generating schedule is considered. Wind forecasts contain an element of random error, and several methods of addressing this uncertainty in the optimization process are compared. The methods include robust and reliability-based design optimization in addition to a combination of both approaches, and results indicate that reliability-based design optimization is best-suited to this particular problem. Based on a set of wind forecast error scenarios and historical data, a probability-weighted forecast wind generation scenario set is developed. Reliability constraints are imposed to meet a minimum of 80% of the generating schedule time intervals. This methodology is applied to a case study on Vancouver Island. Preliminary results show that when compared to the base case of a standalone wind farm on Vancouver Island, a wind farm coupled with pumped storage can prove to be economically competitive with pumped storage capital costs below $1.53 million/MW installed pumped storage capacity and a firm energy price of $130/MWh. / Graduate

An accurate wind resource assessment (WRA) is crucial in energy prediction as the power is directly proportional to the wind speed cubed. This thesis analyses the discrepancy between operational assessment and WRA for a wind farm located on a moderately complex terrain in Ireland. As part of this research, a WRA was undertaken and the results were input to two wind farm design tools, WindPro and WindSim, to estimate the annual energy production. Two and a half years of data was available from a 50m met mast. The data was analysed and filtered to ascertain and limit the usage of erroneous data. The dataset was then correlated with an available online dataset utilising the Measure Correlate Predict (MCP) module in WindPro in order to estimate the long term wind resource at the site. The wind resource was then used to determine the annual energy produced at the site using both WindPro and WindSim. A loss of 8% was applied to the energy calculations for comparison with the original WRA. The results demonstrate the energy production from the original energy prediction, undertaken by a leading wind consultancy prior to construction, was overestimated by an average 10.19% over the three years of operation. The averaged wind speed at hub height in the original WRA was 8.2m/s. However, the prediction undertaken using WindPro in this study estimated an average hub height wind speed of 8.0m/s while WindSim estimated an average of 7.36m/s. These differing results had a significant contribution to the difference in Annual Energy Production (AEP). The calculated annual energy results were an overestimation of energy production by an average of 8.10% utilising WindPro, while WindSim underestimated the energy output by just 0.11%.

Ontario, as the rest of the world, is moving towards a clean energy sector and green economy and to this effect, the Government of Ontario has set a goal of phasing out all coal-fired generation by the end of the year 2014. Wind energy is one of the most mature renewable energy technologies; it is clean and abundant. With Canada’s wind profile and wind energy potential, Ontario has focused on increasing the wind generation penetration in its electrical grid to compensate for the phasing out of coal-fired generation. In this thesis, long-term statistical trend analysis of wind generation patterns in Ontario is carried out, using wind generation data sets of Ontario wind farms during 2007 – 2010, on hourly, monthly, seasonal, and yearly time-scales. The analysis carried out, includes, long-term total wind generation capacity factor (CF) trends on yearly, seasonal, and monthly scales. To arrive at a better understanding of the wind generation intermittency and variability in Ontario, long term wind generation variability trends are presented. The correlation between the CFs of Ontario’s wind farms is determined using the Pearson Product- Moment Correlation Coefficient and examined against their distances from one another to understand the effect of geographic diversity for wind farms on total wind generation. The electricity system demand for on- and off-peak periods is analyzed to examine the contribution of wind generation during these periods. These analyses provide critical inputs and guidelines to planners and policy makers on the role that wind can play in the supply mix of Ontario when coal-fired generating units are replaced with wind generation. Expansion of wind generation capacity requires a closer examination of the location and quality of wind resources and a detailed understanding of its operational impacts on the transmission grid. A transmission network model is further developed in the thesis, for Ontario, based on the 500 kV and 230 kV transmission corridors with their planned enhancements for the three specific years under study- 2010, 2015 and 2025. The zonal supply mix of generation resources included are, nuclear, wind, hydro, gas-fired and coal-fired generation. An optimal power flow model is developed considering the future years’ demand and generation scenarios, and used in a deterministic case study. Subsequently, Monte Carlo simulations are carried out considering the variability and uncertainty of wind generation. Both case studies examine the effect of different wind generation penetration levels on the Ontario electrical grid and analyze long-term wind generation impacts. Wind generation is characterized by its variability and uncertainty. Hence, wind penetration in the electricity grid presents major challenges to power system operators. Some of these challenges are tackled by this thesis, such as the operating reserves required for different levels of wind penetration to maintain the system’s adequacy, the operating costs as a result of wind generation’s intermittent nature, and the impact on power losses as a result of wind generation’s dependability on its location. Moreover, the associated Green-House-Gas emissions with different penetration level are determined. The results quantify the impact of the different wind generation penetration levels on the Ontario’s power system.

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 16, 2007) Includes bibliographical references (p. 71-74).

Thesis (Ph. D.)--University of Nevada, Reno, 2007. / "March, 2007." Includes bibliographical references (leaves 143-147). Online version available on the World Wide Web.

Thesis (MS )--Montana State University--Bozeman, 2008. / Typescript. Chairperson, Graduate Committee: Douglas S. Cairns. Includes bibliographical references (leaves 142-147).

Thesis (M.S.)--University of Nevada, Reno, 2008. / "May, 2008." Includes bibliographical references (leaves 144-147). Online version available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 84-87). Also available on the World Wide Web.

Thesis (doctoral)--Tallinn University of Technology, 2009. / Includes bibliographical references.