• Wind and Structures
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• v.20 no.1
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• pp.1-13
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• 2015

This study applied long-term wind speed data from Penghu and Dongjidao weather stations to simulate the wind energy production for eight onshore and one offshore wind farms at Penghu Island, Taiwan by a commercial software package, Wind Atlas Application Program (WAsP). In addition, the RET Screen software suite was also applied to analyze economic characteristics of these nine wind farms (WFs). The results show that the capacity factors (CFs) of the nine wind farms mentioned above are in the range of 44.5% to 49.1%. In addition, utilizing 1.8-MW wind turbines (WTs) for all onshore WFs was the most feasible selection among the four potential types of WTs (600, 900, 1,800 and 3,600 kW) considered. 3-MW WTs selected for the offshore WF can produce the most wind energy and the smallest wake loss among the three possible types of WTs (1, 2 and 3MW). As a consequence of implementing these WFs, the emission of about 680,977 tons carbon dioxide ($tCO_2$) into the local atmosphere in Penghu Island annually could be avoided. Finally, based on the payback periods achieved, the order of implementation of the considered WFs can be identified more clearly. Longmen WF should be the first priority, and the next one should be SiyuWF and so on. Besides, this study provides much useful information for WF planning on Penghu Island.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.