Kütahya is considered as a candidate region for a wind farm investment due to Turkey's 2023 energy targets and its proximity to other wind farm investments. In this study, two years of wind data collected from a hill near the Evliya Çelebi Campus of Kütahya Dumlupınar University was used to evaluate the wind farm potential of Kütahya. First, the wind speed, wind direction, wind shear, turbulence intensity and wind speed ramp characteristics were determined. Second, the WAsP software was used to create a wind atlas for the region. Three sites with strong wind potential were evaluated. A techno-economic analysis was conducted using five types of wind turbines selected from the WAsP database. Third, optimization of a wind farm layout was conducted by considering different hub height options for 14 commercial wind turbines using MATLAB software. It was shown theoretically that a wind farm with a power capacity of 25 MW can operate with a capacity factor of 35%. However, due to the relatively high topographical ruggedness index on the wind farm site, the calculated value for the capacity factor could not be reached in a real-life application.
Republic of Turkey Ministry of Energy and Natural Resources (MENR). Strategic Plan, www.enerji.gov.tr/tr-TR/Stratejik-Plan (2011, accessed 19 June 2020).
2.
Melikoglu M. Vision 2023: Feasibility analysis of Turkey's renewable energy projection. Renew Energy 2013; 50: 570–575.
3.
AdaramolaMSAgelin-ChaabMPaulSS. Assessment of wind power generation along the coast of Ghana. Energy Convers Manage2014; 77: 61–69.
4.
BoudiaSMGuerriO. Investigation of wind power potential at Oran, northwest of Algeria. Energy Convers Manage2015; 105: 81–92.
5.
BaseerMAMeyerJPAlamMM, et al.Wind speed and power characteristics for Jubail industrial city, Saudi Arabia. Renew Sustain Energy Rev2015; 52: 1193–1204.
6.
SolyaliDAltunçMTolunS, et al.Wind resource assessment of Northern Cyprus. Renew Sustain Energy Rev2016; 55: 180–187.
7.
LiMLiX. Investigation of wind characteristics and assessment of wind energy potential for Waterloo region, Canada. Energy Convers Manage2005; 46: 3014–3033.
8.
FazelpourFMarkarianESoltaniN. Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran. Renew Energy2017; 109: 646–667.
9.
AukitinoTKhanMGMAhmedMR. Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters. Energy Convers Manage2017; 151: 641–660.
10.
MohammadiKAlaviOMostafaeipourA, et al.Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Convers Manage2016; 108: 322–335.
11.
SharmaKAhmedMR. Wind energy resource assessment for the Fiji Islands: Kadavu Island and Suva Peninsula. Renew Energy2016; 89: 168–180.
12.
NouriAAit BabramMElwarrakiE, et al.Moroccan wind farm potential feasibility. Case study. Energy Convers Manage2016; 122: 39–51.
13.
Gallego-CastilloCCuerva-TejeroALopez-GarciaO. A review on the recent history of wind power ramp forecasting. Renew Sustain Energy Rev2015; 52: 1148–1157.
14.
RenGLiuJWanJ, et al.Measurement and statistical analysis of wind speed intermittency. Energy2017; 118: 632–643.
15.
OzturkUANormanBA. Heuristic methods for wind energy conversion system positioning. Electric Power Syst Res2004; 70: 179–185.
16.
MosettiGPoloniCDiviaccoB. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J Wind Eng Ind Aerodyn1994; 51: 105–116.
17.
KöseR. An evaluation of wind energy potential as a power generation source in Kütahya, Turkey. Energy Convers Manage2004; 45: 1631–1641.
18.
KoseRArif OzgurMErbasO, et al.The analysis of wind data and wind energy potential in Kutahya, Turkey. Renew Sustain Energy Rev2004; 8: 277–288.
19.
Özgur MA. Statistical analysis of wind characteristic of Kütahya and electricity production applicability. PhD Thesis, Osmangazi University, Institute of Applied Science, 2006 (in Turkish).
20.
Bailey B, McDonald S, Bernadett D, et al. Wind resource assessment handbook: Fundamentals for conducting a successful monitoring program. AWS Sci 1997; 41: 65.
21.
Burton T, Jenkins N, Sharpe D, et al. Wind energy handbook. 2nd ed. Chichester, UK: John Wiley & Sons, Ltd., 2011.
22.
JustusCGHargravesWRMikhailA, et al.Methods for estimating wind speed frequency distributions. J Appl Meteorol1978; 17: 350–353.
23.
AzadAKRasulMGYusafT. Statistical diagnosis of the best Weibull methods for wind power assessment for agricultural applications. Energies2014; 7: 3056–3085.
24.
AhmedS. Wind energy: theory and practice, 3rd ed. New Delhi: PHI Learning Private limited, 2016.
25.
AkdaǧSADinlerA. A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manage2009; 50: 1761–1766.
26.
ZhengCWPanJLiJX. Assessing the China Sea wind energy and wave energy resources from 1988 to 2009. Ocean Eng2013; 65: 39–48.
27.
ZhengCWXiaoZNPengYH, et al.Rezoning global offshore wind energy resources. Renew Energy2018; 129: 1–11.
28.
ZhengCWLiCYXuJJ. Micro-scale classification of offshore wind energy resource—a case study of the New Zealand. J Cleaner Product2019; 226: 133–141.
29.
ZhengCWLiXYLuoX, et al.Projection of future global offshore wind energy resources using CMIP data. Atmos-Ocean2019; 57: 134–148.
30.
Mortensen NG, Heathfield DN, Rathmann O, et al. Wind atlas analysis and application program: WAsP 11 help facility. Roskilde, Denmark: Technical University of Denmark, 2014.
31.
ChenYLiHJinK, et al.Wind farm layout optimization using genetic algorithm with different hub height wind turbines. Energy Convers Manage2013; 70: 56–65.
32.
ChenKSongMXZhangX, et al.Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm. Renew Energy2016; 96: 676–686.
33.
AbdulrahmanMWoodD. Investigating the power-COE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation. Renew Energy2017; 102: 267–278.
34.
FengJShenWZ. Design optimization of offshore wind farms with multiple types of wind turbines. Appl Energy2017; 205: 1283–1297.
35.
HollandJH. Adaptation in natural and artificial systems, Michigan: University of Michigan Press, 1975.
36.
JuXLiuF. Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation. Appl Energy2019; 248: 429–445.
37.
AbdelsalamAMEl-ShorbagyMA. Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renew Energy2018; 123: 748–755.
38.
WuYZhangSWangR, et al.A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner. Renew Energy2020; 146: 687–698.
39.
Koscedil1;ar O. Numerical investigation and optimization of the wind farm layout problem. PhD Thesis, Kütahya Dumlupınar University, Institute of Applied Science, 2018 (in Turkish).
40.
Jensen NO. A note on wind generator interaction. Riso Natl Lab Roskilde 1983. DOI: Riso-M-2411.
41.
Katic I, Højstrup J and Jensen NO. A simple model for cluster efficiency. In: European wind energy conference exhibit, 1986, pp. 407–410.
42.
HabaliSMHamdanMASJubranBA, et al.Wind speed and wind energy potential of Jordan. Sol Energy1987; 38: 59–70.
43.
SarkarMHussainM. The potential of wind electricity generation in Bangladesh. Renew Energy1991; 1: 855–857.
44.
Ahmed ShataASHanitschR. Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt. Renew Energy2006; 31: 1183–1202.
45.
OhunakinOSOyewolaOMAdaramolaMS. Economic analysis of wind energy conversion systems using levelized cost of electricity and present value cost methods in Nigeria. Int J Energy Environ Eng2013; 4: 2.
46.
BatainehKMDalalahD. Assessment of wind energy potential for selected areas in Jordan. Renew Energy2013; 59: 75–81.
47.
EmeisS. Wind energy meteorology: atmospheric physics for wind power generation. Green Energy Technol2013; 99: 189–192.
48.
Lokoshchenko MA, Yavlyaeva EA and Kirtzel HJ. Sodar data about wind profiles in Moscow city. Meteorol. Zeitschrift, 2009. DOI: 10.1127/0941-2948/2009/0383.
49.
Emeis S, Baumann-Stanzer K, Piringer M, et al. Wind and turbulence in the urban boundary layer - Analysis from acoustic remote sensing data and fit to analytical relations. Meteorol. Zeitschrift, 2007. DOI: 10.1127/0941-2948/2007/0217.
50.
Ahrens DC. Essentials of meteorology: an invitation to the atmosphere. 6th ed. Belmont, CA, USA: Brooks/Cole, 2011.
51.
Frederick K. Lutgens, Edward J. Tarbuck, Tasa DG. Atmosphere, The: An Introduction to Meteorology, 12th ed. Glenview, USA: Pearson, 2013.
52.
KruskalJB. On the shortest spanning subtree of a graph and the traveling salesman problem. Proc Am Math Soc1956; 7: 48.
53.
SongMXChenKHeZY, et al.Bionic optimization for micro-siting of wind farm on complex terrain. Renew Energy2013; 50: 551–557.
54.
SongMXChenKHeZY, et al.Optimization of wind farm micro-siting for complex terrain using greedy algorithm. Energy2014; 67: 454–459.
55.
SongMXChenKZhangX, et al.The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain. Energy2015; 80: 567–574.
56.
SongMXChenKHeZY, et al.Wake flow model of wind turbine using particle simulation. Renew Energy2012; 41: 185–190.
57.
Rathmann O, Mortensen NG, Landberg L, et al. Assessing the accuracy of WAsP in non-simple terrain. In: BWEA18 conference, 24–27 September 1996, pp. 6–11. UK: Univ Exet Exeter.
58.
Bowen AJ and Mortensen NG. WAsP prediction errors due to site orography. Riso-E Rep 2004.
59.
Mortensen NG, Bowen AJ and Antoniou I. Improving WAsP predictions in (too) complex terrain. In: European wind energy conference and exhibition (EWEC), Athens, Greece, 27 February–2 March 2006, pp. 175-183.
60.
Mortensen NG, Rathmann O, Tindal A, et al. Field validation of the ΔRIX performance indicator for flow in complex terrain. In: European wind energy conference and exhibition, Brussels, Belgium, 31 March-3 April 2008, pp. 186–204.
