İnşaat Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/395

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  • Conference Object
    Citation - Scopus: 2
    Wind Energy Development in Turkey: Policies and Challenges
    (European Wind Energy Association, 2013) Kucukali, S.; Küçükali, Serhat; İnşaat Mühendisliği
    This paper aims to explore the availability and potential of wind energy in Turkey as well as assessing related government policies and challenges. As a European Union (EU) candidate several incentives were developed in Turkey for electricity generation from renewable energy sources by the enactment of Law No. 5346 in 2005 which was later restructured by Law No. 6094 in 2010. By June 2012, 57 wind power plants in operation with a total installed capacity of 1776 MW; whereas there are 212 wind power plants are under construction with a total installed capacity of 7166 MW. Within the scope of the study a survey was conducted with experts in order to determine the main barriers for wind energy development. The survey results revealed that the grid connection and change of laws were considered as the most important risks for wind energy development in Turkey. The findings of the case studies demonstrated that the perception of inadequate understanding of the risks elements can lead to project schedule overrun which can result in significant revenue loss.
  • Article
    Citation - WoS: 32
    Citation - Scopus: 31
    Wind Energy Resource Assessment of Izmit in the West Black Sea Coastal Region of Turkey
    (Pergamon-elsevier Science Ltd, 2014) Kucukali, Serhat; Dinckal, Cigdem
    The wind energy potential of Izmit (41.19 N, 30.30 E), which is located in the West Black Sea Coastal Region of Turkey, is assessed with the statistical analysis of the gathered wind data at the 50-m height measurement mast covering the period of 06/2008-06/2009. The annual average wind speed is calculated as 6 m/s and the prevailing wind direction is ENE (60 degrees). The Weibull distribution parameters of shape and scale factor are found as 2.03 and 6.73 m/s, respectively. The measured wind speed data are compared with the data of nearby meteorological stations and the results show that there is a considerable difference between the onsite measurements and the measurements of the meteorological stations. Moreover, a turbulence analysis is carried out and the turbulence intensity is negatively correlated with the normalized height from ground level with canopy height. The energy generation performances of three different wind turbines are evaluated by using the onsite wind speed measurements and the assessment shows that the capacity factor increase by a factor of two from 17% to 34% depending on the type of the turbine. Furthermore, an economic analysis is carried out for a 50 MW wind energy project for the potential site and the proposed project benefit/cost ratio is calculated as 8. (C) 2013 Elsevier Ltd. All rights reserved.
  • Article
    Citation - WoS: 30
    Citation - Scopus: 32
    Risk Scorecard Concept in Wind Energy Projects: an Integrated Approach
    (Pergamon-elsevier Science Ltd, 2016) Kucukali, Serhat
    The proposed risk assessment tool quantifies economic, environmental, political, and societal risks in wind energy projects. The risks are quantified based on the measured data and document evidence. An important component of the proposed methodology includes converting different external risks into a common scale and these scales express the level of risk factors. A survey was conducted with the experts in order to determine the relative importance of external risks. Applicability of the proposed tool is tested on real time wind power plants that are located in Izmir Province on the Aegean coast of Turkey. Change in laws and regulations, environmental issues, local community, grid connection, land use and permits, and erroneous wind resource assessment appeared to be key risk factors. The findings of case studies showed that the perception of inadequate understanding of the potential risks can lead to significant revenue loss. The proposed method estimates each risk factor level separately and then aggregates them by calculating the Project Risk Score (PRS) which is linked to the normalized revenue loss. (C) 2015 Elsevier Ltd. All rights reserved.