A Local Perspective on Wind Energy Potential in Six Reference Sites on the Western Coast of the Black Sea Considering Five Different Types of Wind Turbines
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Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function
applied sciences Article Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function Neeraj Bokde1,*,† , Andrés Feijóo 2,*,† and Daniel Villanueva 2,† 1 Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India 2 Departamento de Enxeñería Eléctrica-Universidade de Vigo, Campus de Lagoas-Marcosende, 36310 Vigo, Spain; [email protected] * Correspondence: [email protected] (N.B.); [email protected] (A.F.); Tel.: +91-90-2841-5974 (N.B.) † These authors contributed equally to this work. Received: 6 September 2018; Accepted: 26 September 2018; Published: 28 September 2018 Abstract: The representation of a wind turbine power curve by means of the cumulative distribution function of a Weibull distribution is investigated in this paper, after having observed the similarity between such a function and real WT power curves. The behavior of wind speed is generally accepted to be described by means of Weibull distributions, and this fact allows researchers to know the frequency of the different wind speeds. However, the proposal of this work consists of using these functions in a different way. The goal is to use Weibull functions for representing wind speed against wind power, and due to this, it must be clear that the interpretation is quite different. This way, the resulting functions cannot be considered as Weibull distributions, but only as Weibull functions used for the modeling of WT power curves. A comparison with simulations carried out by assuming logistic functions as power curves is presented. The reason for using logistic functions for this validation is that they are very good approximations, while the reasons for proposing the use of Weibull functions are that they are continuous, simpler than logistic functions and offer similar results. -
Renewable Energy in Small Islands
Renewable Energy on Small Islands Second edition august 2000 Sponsored by: Renewable Energy on Small Islands Second Edition Author: Thomas Lynge Jensen, Forum for Energy and Development (FED) Layout: GrafiCO/Ole Jensen, +45 35 36 29 43 Cover photos: Upper left: A 55 kW wind turbine of the Danish island of Aeroe. Photo provided by Aeroe Energy and Environmental Office. Middle left: Solar water heaters on the Danish island of Aeroe. Photo provided by Aeroe Energy and Environmental Office. Upper right: Photovoltaic installation on Marie Galante Island, Guadeloupe, French West Indies. Photo provided by ADEME Guadeloupe. Middle right: Waiah hydropower plant on Hawaii-island. Photo provided by Energy, Resource & Technology Division, State of Hawaii, USA Lower right: Four 60 kW VERGNET wind turbines on Marie Galante Island, Guadeloupe, French West Indies. Photo provided by ADEME Guadeloupe. Printing: Vesterkopi Printing cover; Green Graphic No. printed: 200 ISBN: 87-90502-03-5 Copyright (c) 2000 by Forum for Energy and Development (FED) Feel free to use the information in the report, but please state the source. Renewable Energy on Small Islands – Second Edition August 2000 Table of Contents Table of Contents Foreword and Acknowledgements by the Author i Introduction iii Executive Summary v 1. The North Atlantic Ocean Azores (Portugal) 1 Canary Island (Spain) 5 Cape Verde 9 Faeroe Islands (Denmark) 11 Madeira (Portugal) 13 Pellworm (Germany) 17 St. Pierre and Miquelon (France) 19 2. The South Atlantic Ocean Ascension Island (UK) 21 St. Helena Island (UK) 23 3. The Baltic Sea Aeroe (Denmark) 25 Gotland (Sweden) 31 Samsoe (Denmark) 35 4. -
Renewable Generation Data for European Energy System Analysis
Renewable Generation Data for European Energy System Analysis Alexander Kies1, Bruno U. Schyska2, Mariia Bilousova1;3, Omar El Sayed1;3, Jakub Jurasz4;5;6, Horst Stoecker1;3;7 1Frankfurt Institute for Advanced Studies, Goethe-University Frankfurt, Ruth-Moufang-Str. 1, 60438 Frankfurt, Germany 2German Aerospace Center (DLR), Institute of Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany 3Goethe-University, Frankfurt, Germany 4Faculty of Environmental Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland 5Faculty of Management, AGH University, 30-059 Cracow, Poland 6School of Business, Society and Engineering, M¨alardalens H¨ogskola,72113 V¨aster˚as, Sweden 7GSI Helmholtzzentrum f¨urSchwerionenforschung, Planckstraße 1, 64291 Darmstadt, Germany Abstract In the process of decarbonization, the global energy mix is shifting from fossil fuels to renewables. To study decarbonization pathways, large-scale energy system models are utilized. These models require accurate data on renewable generation to develop their full potential. Using different data can lead to conflicting results and policy advice. In this work, we compare several datasets that are commonly used to study the transition towards highly renewable European power system. We find significant differences between these datasets and cost-difference of about 10% result in the different energy mix. We conclude that much more attention must be paid to the large uncertainties of the input data. Keywords: Energy System Analysis, Renewable Generation Data, Energy arXiv:2101.08741v1 [physics.soc-ph] 21 Jan 2021 Meteorology, MERRA-2, ERA5, renewables.ninja, EMHires Preprint submitted to Journal Name January 22, 2021 1. Introduction Sustainable energy sources are a major solution for the imminent thread of climate change [1, 2]. -
Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains
energies Article Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains Rebecca J. Barthelmie 1,* , Tristan J. Shepherd 2 , Jeanie A. Aird 1 and Sara C. Pryor 2 1 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA; [email protected] 2 Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA; [email protected] (T.J.S.); [email protected] (S.C.P.) * Correspondence: [email protected] Received: 13 July 2020; Accepted: 11 August 2020; Published: 18 August 2020 Abstract: Continued growth of wind turbine physical dimensions is examined in terms of the implications for wind speed, power and shear across the rotor plane. High-resolution simulations with the Weather Research and Forecasting model are used to generate statistics of wind speed profiles for scenarios of current and future wind turbines. The nine-month simulations, focused on the eastern Central Plains, show that the power scales broadly as expected with the increase in rotor diameter (D) and wind speeds at hub-height (H). Increasing wind turbine dimensions from current values (approximately H = 100 m, D = 100 m) to those of the new International Energy Agency reference wind turbine (H = 150 m, D = 240 m), the power across the rotor plane increases 7.1 times. The mean domain-wide wind shear exponent (α) decreases from 0.21 (H = 100 m, D = 100 m) to 0.19 for the largest wind turbine scenario considered (H = 168 m, D = 248 m) and the frequency of extreme positive shear (α > 0.2) declines from 48% to 38% of 10-min periods. -
Investigation of Different Airfoils on Outer Sections of Large Rotor Blades
School of Innovation, Design and Engineering Bachelor Thesis in Aeronautical Engineering 15 credits, Basic level 300 Investigation of Different Airfoils on Outer Sections of Large Rotor Blades Authors: Torstein Hiorth Soland and Sebastian Thuné Report code: MDH.IDT.FLYG.0254.2012.GN300.15HP.Ae Sammanfattning Vindkraft står för ca 3 % av jordens produktion av elektricitet. I jakten på grönare kraft, så ligger mycket av uppmärksamheten på att få mer elektricitet från vindens kinetiska energi med hjälp av vindturbiner. Vindturbiner har använts för elektricitetsproduktion sedan 1887 och sedan dess så har turbinerna blivit signifikant större och med högre verkningsgrad. Driftsförhållandena förändras avsevärt över en rotors längd. Inre delen är oftast utsatt för mer komplexa driftsförhållanden än den yttre delen. Den yttre delen har emellertid mycket större inverkan på kraft och lastalstring. Här är efterfrågan på god aerodynamisk prestanda mycket stor. Vingprofiler för mitten/yttersektionen har undersökts för att passa till en 7.0 MW rotor med diametern 165 meter. Kriterier för bladprestanda ställdes upp och sensitivitetsanalys gjordes. Med hjälp av programmen XFLR5 (XFoil) och Qblade så sattes ett blad ihop av varierande vingprofiler som sedan testades med bladelement momentum teorin. Huvuduppgiften var att göra en simulering av rotorn med en aero-elastisk kod som gav information beträffande driftsbelastningar på rotorbladet för olika vingprofiler. Dessa resultat validerades i ett professionellt program för aeroelasticitet (Flex5) som simulerar steady state, turbulent och wind shear. De bästa vingprofilerna från denna rapportens profilkatalog är NACA 63-6XX och NACA 64-6XX. Genom att implementera dessa vingprofiler på blad design 2 och 3 så erhölls en mycket hög prestanda jämfört med stora kommersiella HAWT rotorer. -
Turbine Directory
Turbine Directory Wind turbines are the one component that wind farms Ten turbine manufacturers were selected for this simply cannot do without. More than 100 wind turbine directory, based on U.S. wind energy capacity installed manufacturers exist globally, offering as many 1,000 during 2012* (Source: AWEA U.S. Wind Industry An- turbine models. No single turbine is the right fit for nual Market Report Year End 2012). Technical spec- every application. ifications were taken from manufacturers’ literature In the following pages, Wind Systems has compiled or otherwise provided by the manufacturers. Readers news, turbine models, and general specifications from should contact the turbine manufacturer directly for common utility-scale wind turbine manufacturers, in its complete specifications. inaugural Turbine Directory. * Companies with top-ten 2012 market share that have ceased manufacture of new wind turbines were not included in this directory. windsystemsmag.com 21 inFOCUS: Turbine Directory GE Energy General Electric’s onshore wind turbine portfolio consists of five models ranging from 1.7 to 3.2 MW, with various configurations to meet project requirements. GE is the top wind turbine manufacturer in the U.S., with 3,003 turbines (5,014 MW) installed during 2012, accounting for a 38.2 percent market share. GE turbines account for more than 24 GW of installed wind power capacity in the U.S. GE’s 2.5-120 turbine now operating commercially at German site Two months after the commercial operation in 8,000-megawatt hours a of the “Energiewende” on a completion of installation, Schnaittenbach, a town in year, which is equivalent regional level. -
3.8.5.Post0+Ug.Gdd887c5.D20201118
InVEST User’s Guide, Release 3.8.5.post0+ug.gdd887c5.d20201118 InVEST User’s Guide Integrated Valuation of Ecosystem Services and Tradeoffs Version 3.8.5.post0+ug.gdd887c5.d20201118 Editors: Richard Sharp, James Douglass, Stacie Wolny. Contributing Authors: Katie Arkema, Joey Bernhardt, Will Bierbower, Nicholas Chaumont, Douglas Denu, James Douglass, David Fisher, Kathryn Glowinski, Robert Griffin, Gregory Guannel, Anne Guerry, Justin Johnson, Perrine Hamel, Christina Kennedy, Chong-Ki Kim, Martin Lacayo, Eric Lonsdorf, Lisa Mandle, Lauren Rogers, Richard Sharp, Jodie Toft, Gregory Verutes, Adrian L. Vogl, Stacie Wolny, Spencer Wood. Citation: Sharp, R., Douglass, J., Wolny, S., Arkema, K., Bernhardt, J., Bierbower, W., Chaumont, N., Denu, D., Fisher, D., Glowinski, K., Griffin, R., Guannel, G., Guerry, A., Johnson, J., Hamel, P., Kennedy, C., Kim, C.K., Lacayo, M., Lonsdorf, E., Mandle, L., Rogers, L., Toft, J., Verutes, G., Vogl, A. L., and Wood, S. 2020, InVEST 3.8.5.post0+ug.gdd887c5.d20201118 User’s Guide. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund. 1 InVEST User’s Guide, Release 3.8.5.post0+ug.gdd887c5.d20201118 2 CONTENTS 1 Introduction 5 1.1 Data Requirements and Outputs Summary Table............................5 1.2 Why we need tools to map and value ecosystem services........................5 1.2.1 Introduction...........................................5 1.2.2 Who should use InVEST?...................................5 1.2.3 Introduction to InVEST..................................... 10 1.2.4 Using InVEST to Inform Decisions.............................. 12 1.2.5 A work in progress....................................... 15 1.2.6 This guide............................................ 15 1.3 Getting Started............................................. -
Description of an 8 MW Reference Wind Turbine
Journal of Physics: Conference Series PAPER • OPEN ACCESS Recent citations Description of an 8 MW reference wind turbine - Cyclic flexural test and loading protocol for steel wind turbine tower columns To cite this article: Cian Desmond et al 2016 J. Phys.: Conf. Ser. 753 092013 Chung-Che Chou et al - Techno-economic system analysis of an offshore energy hub with an outlook on electrofuel applications Christian Thommessen et al View the article online for updates and enhancements. - Evaluating wind turbine power coefficient—An undergraduate experiment Edward W. K. Chan et al This content was downloaded from IP address 170.106.40.139 on 26/09/2021 at 05:57 The Science of Making Torque from Wind (TORQUE 2016) IOP Publishing Journal of Physics: Conference Series 753 (2016) 092013 doi:10.1088/1742-6596/753/9/092013 Description of an 8 MW reference wind turbine Cian Desmond1, Jimmy Murphy1, Lindert Blonk2 and Wouter Haans2 1 MaREI, University College Cork, Ireland 2 DNV-GL, Turbine Engineering, Netherlands. E-mail: [email protected] Abstract. An 8 MW wind turbine is described in terms of mass distribution, dimensions, power curve, thrust curve, maximum design load and tower configuration. This turbine has been described as part of the EU FP7 project LEANWIND in order to facilitate research into logistics and naval architecture efficiencies for future offshore wind installations. The design of this 8 MW reference wind turbine has been checked and validated by the design consultancy DNV-GL. This turbine description is intended to bridge the gap between the NREL 5 MW and DTU 10 MW reference turbines and thus contribute to the standardisation of research and development activities in the offshore wind energy industry. -
Offshore Wind Energy Resource Assessment Across the Territory of Oman: a Spatial-Temporal Data Analysis
sustainability Article Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis Amer Al-Hinai 1,2,* , Yassine Charabi 3 and Seyed H. Aghay Kaboli 1,3 1 Sustainable Energy Research Center, Sultan Qaboos University, 123 Muscat, Oman; [email protected] 2 Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, 123 Muscat, Oman 3 Center for Environmental Studies and Research, Sultan Qaboos University, 123 Muscat, Oman; [email protected] * Correspondence: [email protected] Abstract: Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment Citation: Al-Hinai, A.; Charabi, Y.; and production of offshore wind energy in Oman. -
Wind Power Prediction Model Based on Publicly Available Data: Sensitivity Analysis on Roughness and Production Trend
WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND Dissertation in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE WITH A MAJOR IN WIND POWER PROJECT MANAGEMENT Uppsala University Campus Gotland Department of Earth Sciences Gireesh Sakthi [10th December, 2019] WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND Dissertation in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE WITH A MAJOR IN WIND POWER PROJECT MANAGEMENT Uppsala University Campus Gotland Department of Earth Sciences Approved by Supervisor, Dr. Karl Nilsson Examiner, Dr. Stefan Ivanell 10th December, 2019 II Abstract The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU to get a distributed wind speed. The distributed wind speed is then used to interpolate the wind power with the help of the power curve of the wind turbine. The interpolated wind power is then compared with the Actual Production Data (APD) to validate the prediction model. The simulation results show that the model works fairly predicting the Annual Energy Production (AEP) on monthly averages for all sites but the model could not follow the APD trend on all cases. -
The CLIMIX Model a Tool to Create and Evaluate Spatially-Resolved
Renewable and Sustainable Energy Reviews 42 (2015) 1–15 Contents lists available at ScienceDirect Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development S. Jerez a,b,n, F. Thais c, I. Tobin a, M. Wild d, A. Colette e, P. Yiou a, R. Vautard a a Laboratoire des Sciences du Climat et de l’Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, 91191 Gif sur Yvette, France b Department of Physics, University of Murcia, 30100 Murcia, Spain c Institut de Technico-Economie des Systèmes Energétiques (I-Tésé), CEA/DEN/DANS, 91191 Gif sur Yvette, France d Institute for Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland e Institut National de l’Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, 60550 Verneuil en Halatte, France article info abstract Article history: Renewable energies arise as part of both economic development plans and mitigation strategies aimed Received 16 April 2014 at abating climate change. Contrariwise, most renewable energies are potentially vulnerable to climate Received in revised form change, which could affect in particular solar and wind power. Proper evaluations of this two-way 5 August 2014 climate–renewable energy relationship require detailed information of the geographical location of the Accepted 26 September 2014 renewable energy fleets. However, this information is usually provided as total amounts installed per administrative region, especially with respect to future planned installations. To help overcome this Keywords: limiting issue, the objective of this contribution was to develop the so-called CLIMIX model: a tool that Wind power performs a realistic spatial allocation of given amounts of both photovoltaic (PV) and wind power Solar photovoltaic power installed capacities and evaluates the energy generated under varying climate conditions. -
LCOE Reduction for the Next Generation Offshore Wind Turbines
LCOE reduction for the next generation offshore wind turbines OUTCOMES FROM THE INNWIND.EU PROJECT October 2017 Funded by the European Co-fundedCommunity’s by theSeventh Intelligent Energy Europe ProgrammeFramework ofProgramme the European Union LCOE reduction for the next generation offshore wind turbines OUTCOMES FROM THE INNWIND.EU PROJECT October 2017 www.innwind.eu PRINCIPAL AUTHORS: Peter Hjuler Jensen (DTU) Takis Chaviaropoulos (NTUA) Anand Natarajan (DTU) AUTHORS (PROJECT PARTNERS): Flemming Rasmussen (DTU) Helge Aagaard Madsen (DTU) Peter Jamieson (Univ. of Strathclyde) Jan-Willem Van Wingerden (TU Delft) Vasilis Riziotis (NTUA) Athanasios Barlas (DTU) Henk Polinder (TU Delft) Asger Bech Abrahamsen (DTU) David Powell (Magnomatics) Gerrit Jan Van Zinderen (DNV GL) Daniel Kaufer (Rambøll) Rasoul Shirzadeh (Univ. of Oldenburg) Jose Azcona Armendariz (CENER) Spyros Voutsinas (NTUA) Andreas Manjock (DNV GL) Uwe Schmidt Paulsen (DTU) James Dobbin (DNV GL) Sabina Potestio (WindEurope) PROJECT COORDINATION: Peter Hjuler Jensen (DTU) PUBLICATION COORDINATION: Sabina Potestio (WindEurope) ACKNOWLEDGEMENTS: Ervin Bossanyi (DNV GL), Kais Atallah (Univ. of Sheffield), Paul Weaver (Univ. of Bristol), Arno Van Wingerde (Fraunhofer IWES), Martin Kuhn (University of Oldenburg), Stoyan Kanev (ECN), Bernard Bulder (ECN), Detlev Heinemann (Univ. of Oldenburg), John Dalsgaard Sørensen (AAU), Zhe Chen (AAU), Po Wen Cheng (Univ. of Stuttgart), Frank Lemmer (Univ. of Stuttgart), Ole Petersen (DHI), Ben Hendriks (WMC), Kimon Argyriadis (DNV GL), Raimund Rolfes (Univ. of Hannover), Dimitris Saravanos (Univ. Patras), Andreas Makris (CRES), Niklas Magnusson (SINTEF), William Leithead (Univ. of Strathclyde), Carlos Pizarro De La Fuente (Gamesa), Arwyn Thomas and Per H. Lauritsen (Siemens), Harald Bersee (SE Blades), Carlo Bottasso (Politecnico di Milano), Alessandro Croce (Politecnico di Milano), Jason Jonkman (NREL), Antonio Ugarte (CENER), Paul Todd (Magnomatics).