Wind energy potential in Greece using a small wind turbine

Wind energy potential in Greece using a small wind turbine J. G. Fantidis, D. V. Bandekas*, N. Vordos and S. Karachalios Department of Electrical Engineering Institute of Technology St. Lucas, 65404 Greece dbandek@teikav.edu.gr Abstract:-The aim of this study is to outline the wind speed distribution in Greece and examine the potential of using this resource for generation of wind power in the country. Long-term wind speed data from 69 stations are analyzed in order to calculate the energy output for a small 2.5 kw installed wind turbine at each site in Greece. The Homer software was used to predict the energy production, the cost of energy and the green house gas emissions reductions. Key-words: - Renewable energy, Homer software, Wind power, Cost Of Energy, GHG Emissions 1. Introduction Energy has played a vital role in the development of human being since the industrial revolution of the 18 th century. The significance of energy in economic development has been recognized almost universally and energy consumption is an index of prosperity and standard of living of people in a country. Basic life requirements such as water and food are also obtained and transported by the energy. Hence, supplying high quality and uninterruptible energy with the lowest costs is an essential requirement. Due to climate effects, air quality issues, rapid depletion of fossil fuel resources and increasing of fuel prices national decision makers increasingly emphasize deployment of less carbon-intensive and more sustainable electricity sources [1-2]. The idea of using wind to produce work is not a new concept. Wind power was used, inter alia, by windmills and sailing ships, for water pumping and irrigation and for grinding grain [3]. Wind energy is clean, inexhaustible, environmentally friendly, and sustainable source of energy. Technology of the extraction of power from wind with modern turbines is a well established industry, at present. According to Resch et al. the global theoretical potential of wind energy was estimated at 6000 EJ however the technical potential of wind energy is 600 EJ [4]. The installed capacity in wind energy increased approximately 67 times in the last 18 years (from 2900 MW in 1996 to 194527 MW in 2010) [3, 5]. The main objective of the present work was to investigate the wind energy resources and the potential for wind energy generation for each of the 69 considered sites in Greece. This paper utilizes average monthly wind speed data of 69 locations in Greece. The annual energy production, the economic analysis and the environmental considerations calculated using Homer software. HOMER (Hybrid Optimization Model for Electric Renewables) is a modelling tool that facilitates design of electric power systems [6-7]. The assessment criterion of the analysis is the electricity production. The project takes for granted a small-micro wind turbine of 2.5 kw, at each of the 69 locations to calculate the electricity production, the Cost Of Energy (COE), the Capacity Factor (CF) and potential emission reduction due to the wind turbine. 2. Global trends of wind energy Wind energy technology has developed very fast during the last 20 years and wind is rapidly becoming a practical source of energy for electric utilities around the world (Figure 1). The recently data of wind energy development on various continents indicate that the growth rates is high not only in the European Union and in the USA but also on developing countries. In 2010 China installed almost half the global market (16.5 GW) and is the new leader of wind power in the world (Table 1) [5]. According to the forecasts of the Global Wind Energy Council [8] wind energy will supply approximately the 16% of the worldwide electricity demand. Installed wind power capacity (MW) 200000 150000 100000 50000 0 2900 2450 74390 59467 47489 39363 31412 24544 17684 4800 6115 7584 9842 13450 93908 121003 158920 194527 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year Figure 1. Global installed wind power capacity Table 1 Wind power capacity installed world wide (in MW) in the last 5 years. ISBN: 978-1-61804-157-9 25

Geographic area 2006 2007 2008 2009 2010 European Union 48122.7 56614.6 65172.3 75106.4 84339 Rest of Europe 564 608 1022 1377 1874 Total Europe 48686.7 57222.6 66194.3 78492.4 86213 United States 11603 16824 25237 35086 40180 Canada 1460 1846 2369 3319 4009 Total North America 13063 18670 27606 38405 44189 India 6270 7845 9655 10926 13065 Japan 1394 1528 1880 2085 2304 China 2594 7845 12104 25805 42287 Other Asian countries 392 504 633 823 985 Total Asia 10650 15787 24272 39639 58641 Rest of the world 1990 2228 2931 4393 5484 Total World 74389.7 93907.6 121003,3 158920.4 194527 Europe has the highest total installed capacity mainly in the countries of the European Union (Table 1). The European Union has set a binding target of a 20% renewable energy contribution by 2020 [9]. Around 180 GW of onshore and offshore wind power could be installed in the European Union (estimates from the European Commission [10] and the European Wind Energy Association [11]); meaning between 10 and 15% of the total EU electricity demand. Leader of wind power in Europe is Germany followed by Spain, Italy, France and the UK (Figure 2). However, if population numbers are considered Denmark is the pioneer in front of Spain, Portugal and Germany. France and the UK are low in the country rankings (Figure 3). Installed wind power capacity (MW) 100000 10000 1000 100 10 1 1010.6 Austria 888.0 Belgium 375.0 Bulgaria 82.0 Cyprus 215.0 Czech Republic 380 Denmark 148.8 197.0 566 27214.7 1208.0 293.0 1428.0 5797.0 31.0 154.0 43.3 Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden UK Country Figure 2. European Union installed wind power capacities at the end of 2010. Wind power capacity per 1000 inhabitants (kw/1000 inhab) 800 600 400 200 0 120.7 81.9 49.6 102.1 20.5 686.6 111.0 36.8 87.5 332.7 106.9 29.3 319.6 2245.0 1185.0 96.1 13.8 46.3 86.2 135.4 31.0 3897.8 366.4 418.0 5.0 19.5 0.9 Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden UK Country Figure 3. Wind power capacity per 1000 EU inhabitants in 2010. Greece has considerable wind energy potential because of its geographical characteristics, such as its shoreline (the maximum in the Europe) and the many mountains. The development of wind energy in 20676.0 449.6 2163.0 231.6 5203.8 83.9 Greece from 1995 to 2010 is shown in Fig. 4. In terms of installed capacity Greece is in the 11 th position while, if population numbers are considered is in the 10 th position. Despite the financial crisis, 121 MW of wind power installed in Greece in 2010 [5]. Installed wind power capacity (MW) 1400 1200 1000 800 600 400 200 0 28 29 29 39 82 226 294 294 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Figure 4. Wind energy in Greece from 1995 to 2010. 3. Wind resource In order to calculate the power output from a wind turbine it is necessary to collect accurate meteorological data about the wind data in a targeted location. In this study the CRES [12] and the TEE- TCG [13] are the source for these data. CRES (Centre for Renewable Energy Sources and Saving) is the Greek national entity for the promotion of renewable energy sources, rational use of energy and energy conservation. TCG (Technical Chamber of Greece) is a public legal entity, with elected administration. It aims at developing Science and Technology in sectors related to the disciplines of its members, for the economic, social, and cultural development of the country, in accordance with the principles of sustainability and environmental protection. In the present work 69 representative locations covering all areas of Greece have been identified for wind potential resource assessment. The average monthly and yearly wind speeds at these locations are given in Table 2. The geography in terms of latitude and longitude are also presented in the same table. In the present study, wind turbine of 2.5 kw from Wind Energy Solution is used. The wind power curve of the WES 5 Tulipo wind machine is shown in Fig. 5. WES 5 Tulipo, has 5 m rotor diameter and 10 m of tower [14]. Table 2. Long-term mean values of wind speed for 69 locations in Greece. N. City Latitude Longitude Average N E 1 39 13 22 48 2.650 2 39 30 23 00 4.389 3 38 37 21 23 1.917 4 s 40 51 25 56 3.642 5 38 23 23 06 4.564 6 37 55 21 17 2.533 7 38 09 21 25 2.683 8 37 36 22 47 3.963 9 38 11 20 29 3.292 Year 375 465 573 746 871 985 1087 1208 ISBN: 978-1-61804-157-9 26

N. City Latitude Longitude Average N E 10 37 47 20 54 3.396 11 38 03 23 40 4.557 12 37 54 23 45 3.650 13 38 28 23 36 2.750 14 35 29 24 07 3.517 15 38 28 26 08 4.525 16 40 54 24 36 2.542 17 38 10 23 60 3.167 18 40 48 21 26 1.958 19 35 00 25 44 4.725 20 39 42 20 49 3.438 21 35 20 25 11 4.783 22 37 04 22 00 2.758 23 39 22 20 48 2.592 24 38 01 24 25 5.883 25 38 54 21 47 4.300 26 40 27 21 17 1.508 27 40 90 24 40 4.643 28 39 37 19 55 2.317 29 35 50 23 70 5.214 30 36 17 23 10 5.717 31 40 18 21 47 3.008 32 /Velo 40 03 20 45 2.675 33 36 47 27 04 5.100 34 40 18 21 47 3.089 35 38 51 22 24 3.648 36 40 60 23 20 4.014 37 39 39 22 27 1.692 38 38 50 20 43 3.175 39 39 55 25 14 4.975 40 36 50 21 42 5.125 41 36 43 24 27 6.750 42 39 04 26 36 4.717 43 37 06 25 23 6.833 44 36 50 23 10 5.043 45 37 01 25 08 5.750 46 38 15 21 44 2.367 47 37 40 21 18 2.458 48 40 23 23 26 2.108 49 39 00 20 80 3.617 50 35 21 24 31 3.900 51 36 24 28 07 4.617 52 37 42 26 55 5.567 53 41 05 23 34 1.517 54 38 00 22 70 3.963 55 37 25 24 57 6.925 56 35 12 26 06 5.271 57 38 54 24 33 5.500 58 35 33 24 07 3.683 59 37 04 22 25 1.825 60 38 19 23 33 3.000 61 40 31 22 58 2.983 62 36 25 25 24 5.369 63 39 33 21 46 3.396 64 40 36 22 33 3.128 65 37 32 22 24 2.517 66 35 00 24 46 5.378 67 41 08 24 53 3.420 68 36 08 22 60 4.767 69 39 10 21 00 4.745 Figure 5. Wind power curve of WES % Tulipo wind turbine. In order to characterize the wind regimes HOMER uses the two-parameter Weibull distribution. The probability density function is given by the equation: k 1 k k v v f( v) = exp c c c (1) where: v is the wind speed (m/s), k is the Weibull shape factor (unitless) and c is the Weibull scale parameter (m/s). The cumulative distribution function is given by the following equation: k v Fv ( ) = 1 exp c (2) The following equation relates the two Weibull parameters and the average wind speed: 1 v = c Γ + 1 κ (3) where: Γ is the gamma function. Finally, HOMER calculates the yearly energy production (YEP) using the following equation: v= C out YEPv ( m) = f( v) Pv ( ) 8760 (4) v= C in where v m is the average wind speed, C in is the Cut-in speed of the wind turbine, C out is the Cut-out speed of the wind turbine, P(v) is the turbine power at wind speed v, f(v) is the Weibull probability function for wind speed v, calculated for the average wind speed v m [15]. ISBN: 978-1-61804-157-9 27

1175 Recent Advances in Energy, Environment and Development 258 120 198 113 507 895 776 374 331 704 941 1211 1834 1096 1353 1009 1339 1235 1006 1775 1799 2100 2593 2181 1996 2165 2663 2695 2593 2407 3171 3190 2940 3269 3275 3217 4031 3884 4130 4031 4906 5182 5792 5542 5884 5647 5667 6205 6227 6088 6338 6097 6957 7279 6787 7198 8145 7619 7467 7960 7860 8361 8718 8667 9197 11152 10880 11330 0 2000 4000 6000 8000 10000 12000 Production (kwh/year) Figure 6. Annual energy production for 69 locations in Greece. 6773 7642 7651 7922 5367 6332 6732 7909 6104 6154 3390 7009 7916 7885 8213 7207 2187 7125 7634 7942 6871 4150 5183 4972 8042 7782 7644 8195 7755 8178 7736 6359 2818 6263 6971 7269 7741 5632 6142 7972 7864 4859 7631 2109 7417 8050 5456 5775 6773 7609 7638 3734 5361 6343 6753 7570 5760 6898 6732 7587 6540 7207 5643 5318 6929 7597 3675 7501 5586 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Hours of operation Figure 7. Hours of operations of the wind turbine. 12 05 09 05 23 41 35 17 15 43 32 55 84 50 62 46 61 56 46 54 96 0.118 81 82 0.100 91 99 0.122 0.123 0.134 0.118 0.110 0.149 0.177 0.145 0.146 0.150 0.147 0.184 0.189 0.184 0.224 0.253 0.237 0.264 0.269 0.258 0.259 0.283 0.284 0.278 0.289 0.278 0.318 0.332 0.310 0.329 0.372 0.348 00 0.100 0.200 0.300 0.400 0.500 0.600 CF Figure 8. CF for 69 locations in Greece. 4. Economic analysis of hybrid energy systems HOMER defines the levelized COE as the average cost/kwh of useful electrical energy produced by the system. In order to calculate the COE, HOMER divides the annualized cost of producing electricity by the total useful electric energy production. The equation for the COE is as follows: COE Cann, tot = (5) E prim, AC + E E prim, DC + grid, sales where C ann,tot is the total annualized cost ( /yr), E prim,ac is AC primary load served (kwh/year), E prim,dc is DC primary load served (kwh/year), E grid,sales is total grid sales (kwh/year). The initial capital cost, replacement cost, maintenance cost and lifetime of wind turbine is given in Table 3 [16]. The project life time has been considered to be 15 yr and the annual real interest rate has been taken as 5%. 0.341 0.363 0.359 0.382 0.398 0.396 0.420 0.509 0.497 0.517 ISBN: 978-1-61804-157-9 28

59 59 46 47 45 48 32 91 44 63 94 42 53 33 60 34 50 54 51 42 49 62 75 40 58 60 89 66 65 65 91 71 COE ( /kwh) 1 0.1 1 10 0.137 0.389 0.174 0.141 0.112 0.115 0.115 0.183 0.136 0.169 0.362 0.124 0.273 0.112 0.141 0.152 0.114 Figure 9. Cost of energy (COE). 0.206 0.203 0.168 0.302 0.199 0.721 0.409 0.471 0.334 0.270 0.296 0.364 0.311 Table 3 Costs in connection with the WES 5 Tulipo wind turbine. Characteristics Wind turbine Model WES 5 Tulipo Power 3 kw Life time 15 year Price 5000$/turbine Replacement 4000$/turbine Maintenance 50$/turbine 5. Results and discussion Given the wind speed data at a certain site, the Homer calculates the net energy output of the wind turbine. The long-term seasonal variation of wind speed at 69 locations is examined in Table 2. As it can be seen from the Table 2, wind speeds are generally higher during the months November to March as compared to other months. This indicates that a wind generator 0.520 0.978 1.418 1.105 1.847 3.048 3.237 produce more energy during winter. Notably, the data also shows that there is a considerable variation of the monthly average wind speed during summer. According to the data the Aegean Archipelago islands, especially Cyclades and Dodecanese, which are island complexes belonging in the South Aegean Prefecture and located in the south-eastern region of Greece and the European Union possesses excellent wind potential. Is very interesting the fact that in most of these islands the electricity production cost is extremely high (e.g. 0.5 h/kwh) [17]. The electricity from the wind turbine, the number of hours of the year during which the wind turbine output was greater than zero, and the CF for the 69 locations are given in Fig. 6, Fig. 7 and Fig. 8 respectively. According to the results Crete, Cyclades and Dodecanese prefectures are the more attractive areas. The lowest energy and CFs values are obtained in the North and Central Greece (Thrace, Macedonia, Thessalia and Epirus). The COE is an important parameter in every power system. As depicted in Fig. 9, the results show that the Aegean Archipelago islands (Crete, Cyclades and Dodecanese) have the lowest production cost of about 6 /kwh while the areas in the North Greece have the higher cost ( 3.237 /kwh). For the comparison the electricity price for the domestic users in all Greece is approximately 0.1 /kwh [18]. From 69 investigated areas the 32 has COE lower than 0.1 /kwh so in these areas the installation of small turbine is financially sound. Replacing energy generated by conventional methods with small wind turbines could provide further benefits to Greece in the form of reduced emissions of priority air pollutants and green house gas (GHG) as the wind turbines has zero GHG emissions. The Homer is capable of estimating the amount of GHG which could be avoided as a result of usage of wind generators [19] [20]. Calculation of the annual reduction in GHG emissions-estimated to occur if the proposed WES 5 Tulipo wind turbine is implemented -was performed. The amount of GHG reduction for the 69 locations is presented in Fig. 10. Based on Fig. 10 the highest GHG emissions mitigation of 11.22 tons/year was observed at. 6. Conclusion Greece has high energy production potential for wind turbine power plants. This paper presented an extended study for placement a small 2.5 kw wind turbine in Greece. The long-term meteorological parameters for each of the 69 considered sites in Greece from CRES and TEE-TCG are analyzed and the results corroborate that Greece has a high content of wind power potential all over the year. ISBN: 978-1-61804-157-9 29

1.16 Recent Advances in Energy, Environment and Development 2.64 0.93 2.08 2.57 1.76 1.78 0.26 3.24 3.99 0.12 3.85 3.14 0.50 0.89 0.77 2.16 0.20 4.09 3.16 1.98 1.20 1.82 0.70 0.11 1.09 1.34 2.67 0.37 2.14 1.00 2.91 1.33 3.24 2.57 2.38 3.99 1.22 1.00 3.18 0.33 6.14 6.16 7.88 7.78 8.06 7.54 8.28 5.73 8.63 6.89 6.03 7.21 6.72 7.13 8.58 7.39 5.83 4.86 9.11 6.27 6.04 5.49 5.59 5.61 5.13 11.04 10.77 11.22 Figure 10. Green house gases reduction due usage of PV systems for different locations. The trend shows that wind speeds are relatively low in the north Greece and increases to relatively high speeds in the south. The results of energy production analysis show that the Agean Archipelago Islands have the maximum values of renewable energy production and CF. Based on economical indicators, although the small wind turbines are not that competitive as the bigger generators, 32 areas have COE lower than the electricity price for the domestic users. From environmental point of view, it was calculated the quantity of green house gases can be avoided entering into the local atmosphere each year. 0 2 4 6 8 10 12 GHG/Redution (Tons/year) References: [1] Abulfotuh F., Energy efficiency and renewable technologies: the way to sustainable energy future. Desalination 209 (2007) 275 282. [2] Becerra T., Bravo X. L., Bárcena F. B., Proposal for territorial distribution of the EU 2020 political renewable energy goal. Renewable Energy 36 (2011) 2067 2077. [3] Michalak P., Zimny J., Wind energy development in the world, Europe and Poland from 1995 to 2009; current status and future perspectives, Renewable and Sustainable Energy Reviews 15 (2011) 2330 2341. [4] Resch G., Held A., Faber T., Panzer C., Toro F., Haas R., Potentials and prospects for renewable energies at global scale. Energy Policy 36 (2008) 4048 4056. [5] EurObserv ER, 2011. Wind-power barometer. Le journal de l éoliel N 8 2011. [6] HOMER Publications, NREL (NationalRenewable Energy Laboratory) available at: https://analysis.nrel.gov/homer/includes/downloads/ HOMERPublications.pdf. [7] NREL (National Renewable Energy Laboratory), HOMER Computer Software, Version 2.68 beta. Retrieved from https://analysis.nrel.gov/homer/s, 2011. [8] Global Wind Energy Council, GWEC. Global Wind Energy Outlook 2006 Report. Available at http://www.gwec.net; 2006. [9] Pryor S. C., Barthelmie R. J., Climate change impacts on wind energy: a review. Renew Sustain Energy Rev 14 (2010) 430 437. [10] European Commission, EC. European Energy and Transports. Scenarios on Energy Efficiency and Renewables. Office for Official Publications of the European Communities: Luxemburg; 2006. [11] European Wind Energy Association, EWEA. No Fuel: wind power without fuel, EWEA Campaign. Available at: http://www.no-fuel.org/; 2006. [12] http://www.cres.gr [13] http://www.tee.gr [14] http://www.windenergysolutions.nl/ [15] Mondal H., Denich M., Assessment of renewable energy resources potential for electricity generation in Bangladesh, Renew Sustain Energy Rev 14 (2010) 2401 2413 [16] Nandi S. K., Ghosh R. H., Prospect of wind PV battery hybrid power system as an alternative to grid extension in Bangladesh, Energy 35 (2010) 3040 3047. [17] Kaldellis J. K., Kavadias K., Christinakis E., Evaluation of the wind hydro energy solution for remote Islands. J Energy Convers Manage 42 (2001) 1105 1120. [18] Kaldellis J. K., Integrated electrification solution for autonomous electrical networks on the basis of RES and energy storage configurations. Conversion and Management 49, (2008) 3708 3720. [19] K. Karakoulidis, K. Mavridis, D. V. Bandekas, P. Adoniadis, economic analysis of a stand alone hybrid photovoltaic-diesel battery- fuel cell power system, Renewable Energy, Vol.36, Issue 8, pp. 2238-2244, 2011. [20] J. G. Fantidis, D. V. Bandekas, N. Vordos, Techno-Economical Study of Hybrid Power System for a Remote Village in Greece, Proceedings of the 6th WSEAS International Conference on Renewable Energy Sources (RES '12), Porto, 2012. ISBN: 978-1-61804-157-9 30