DOCSLIB.ORG

Statistical Characteristics and Mapping of Near-Surface and Elevated Wind Resources in the Middle East

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Statistical Characteristics and Mapping of Near-Surface and Elevated Wind Resources in the Middle East Statistical characteristics and mapping of near-surface and elevated wind resources in the Middle East Dissertation by Chak Man Andrew Yip In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia November, 2018 2 EXAMINATION COMMITTEE PAGE The dissertation of Chak Man Andrew Yip is approved by the examination committee Committee Chairperson: Georgiy L. Stenchikov Committee Members: Marc G. Genton, Gerard T. Schuster, Kristopher B. Kar- nauskas 3 ©November, 2018 Chak Man Andrew Yip All Rights Reserved 4 ABSTRACT Statistical characteristics and mapping of near-surface and elevated wind resources in the Middle East Chak Man Andrew Yip Wind energy is expected to contribute to alleviating the rise in energy demand in the Middle East that is driven by population growth and industrial development. However, variability and intermittency in the wind resource present significant chal- lenges to grid integration of wind energy systems. The first chapter addresses the issues in current wind resource assessment in the Middle East due to sparse meteorological observations with varying record lengths. The wind field with consistent space-time resolution for over three decades at three hub heights over the whole Arabian Peninsula is constructed using the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset. The wind resource is assessed at a higher spatial resolution with metrics of temporal variations in the wind than in prior studies. Previously unrecognized locations of interest with high wind abundance and low variability and intermittency have been identified in this study and confirmed by recent on-site observations. The second chapter explores high-altitude wind resources that may provide al- ternative energy resources in this fossil-fuel-dependent region. This study identifies areas favorable to the deployment of airborne wind energy (AWE) systems in the Middle East and computes the optimal heights at which such systems would best operate. AWE potential is estimated using realistic AWE system specifications and assumptions about deployment scenarios and is compared with the near-surface wind generation potential concerning diurnal and seasonal variability. The results show 5 the potential utility of AWE in areas in the Middle East where the energy demand is high. The third chapter investigates the potential for wind energy to provide a con- tinuous energy supply in the region. We characterize the wind power variability at various time-scales of power operations to illustrate its effects across the Middle East via spectral analysis and clustering. Using a high-resolution dataset obtained from Weather Forecasting and Research (WRF) model simulations, this study showcases how aggregate variability may impact operation, and informs the planning of large- scale wind power integration in the Middle East in light of the scarcity of observational data. 6 ACKNOWLEDGEMENTS The research reported in this dissertation was supported by King Abdullah Uni- versity of Science and Technology (KAUST) and Saudi Basic Industries Corporation (SABIC) under grant number RGC/3/1815-01. I want to thank my advisor Prof. Georgiy Stenchikov for his wisdom, kindness, and patience, and Dr. Udaya Bhaskar Gunturu for the many collaborative thoughts and actions throughout my research journey. I'd also thank Dr. Stoitchko Kalenderski for the historical simulations on WRF, Dr. Suleiman Mostamandi for his help in the setup and configuration of the simulations, and Dr. Alexander Ukhov for his help in computing environment. I have my parents to thank for their unlimited support always and my dear for saving me in the many last-minute crises throughout my time at KAUST. 7 TABLE OF CONTENTS Examination Committee Page 2 Copyright 3 Abstract 4 Acknowledgements 6 List of Figures 9 List of Tables 14 1 Introduction 15 1.1 Near-surface wind resource assessment . 18 1.2 Elevated wind resource assessment . 19 1.3 High-resolution simulation of wind resource and spatial analysis . 20 2 Surface wind resource analysis 21 2.1 Overview . 22 2.2 Methods . 25 2.2.1 Data . 25 2.2.2 Metrics . 27 2.3 Results . 30 2.3.1 Abundance . 30 2.3.2 Variability . 40 2.3.3 Intermittency . 40 2.4 Discussions . 43 3 Airborne wind resource analysis 52 3.1 Overview . 52 3.2 Methods . 54 3.2.1 Wind Speed Maxima (WSM) . 56 3.2.2 Altitudes of WSM . 56 8 3.2.3 Capacity factor . 57 3.2.4 Deployment assumptions . 58 3.3 Results . 58 3.3.1 Average WSM . 59 3.3.2 Variability in WSM . 61 3.3.3 Altitude of WSM . 63 3.3.4 Variability in WSM altitudes . 63 3.3.5 Regional AWE potential . 64 3.3.6 Comparison with near-surface wind resources . 65 3.4 Discussions . 68 4 Variability mitigation with spatial clustering 70 4.1 Overview . 70 4.2 Methods . 74 4.2.1 Data . 74 4.2.2 Methods . 75 4.3 Results . 77 4.3.1 Dominant time scales of variability . 78 4.3.2 Clustering analysis . 80 4.3.3 Cluster variance . 80 4.3.4 Clustering characteristics . 82 4.3.5 In-cluster variability reduction . 82 4.4 Discussions . 84 5 Summary 87 References 90 Appendices 101 9 LIST OF FIGURES 2.1 Median WPD (W m−2) computed at 50 m AGL using the wind fields reconstructed from the MERRA data. The color scale is uniform except for values beyond 100 W m−2 and below 30 W m−2. Large extreme values beyond the 98-percentile in the spatial domain are masked in grey. Selected locations of prior studies are shown. 31 2.2 Categorized median WPD (fig. 2.1) at 50 m AGL into three regimes: regime I where median WPD is above 67 W m−2, regime II where me- dian WPD is from 46 W m−2to67 W m−2, and regime III where median WPD is below 46 W m−2. Elevation contours at 400 m intervals are drawn using elevation data from MERRA. Selected locations of prior studies are shown. 32 2.3 Average WPD (W m−2) computed at 50 m AGL using the wind fields reconstructed from the MERRA data. The color scale is uniform except for values beyond 200 W m−2 and below 60 W m−2. Large extreme values beyond the 98-percentile in the spatial domain are masked in grey. Selected locations of prior studies are shown. 33 2.4 Changes of median WPD (fig. 2.1) and average WPD (fig. 2.3) at 80 m and 140 m from those at 50 m AGL. Large extreme values beyond the 98-percentile in the spatial domain are masked in grey. The color scale is uniform except for values beyond 45%. 37 2.5 Average wind speed (m s−1) computed at 100 m AGL using the wind fields reconstructed from the MERRA data. The color scale is uni- form except for values beyond 6:6 m s−1 and below 2:4 m s−1. Selected locations of prior studies are shown. 38 2.6 Differences in the average wind speed (m s−1) at 100 m AGL between our calculation and that from estimation of Vestas (Vestas - MERRA) are illustrated with a color scale that is uniform except for values be- yond 2:5 m s−1 and below −2:5 m s−1. Selected locations of prior stud- ies are shown. The MERRA dataset is regridded to 1 km resolution by nearest neighbor for illustration. 39 10 2.7 Robust coefficient of variation computed at 50 m AGL using the wind fields reconstructed from the MERRA data. The color scale is uniform except for values beyond 1:45 and below 1:15. Selected locations of prior studies are shown. 41 2.8 Availability of wind resource computed at 50 m AGL using the wind fields reconstructed from the MERRA data. The color scale is uniform except for values beyond 0:45 and below 0:1. Large extreme values beyond the 98-percentile in the spatial domain are masked in grey. Selected locations of prior studies are shown. 42 2.9 Changes of availability (fig. 2.8) at 80 m and 140 m from those at 50 m AGL. Large extreme values beyond the 98-percentile in the spa- tial domain are masked in grey. The color scale is uniform except for values beyond 40% and below 15%. 43 2.10 Median length of wind episode (h) computed at 50 m, 80 m, and 140 m AGL using the wind fields reconstructed from the MERRA data. The color scale is uniform except for values beyond 10 h. Selected locations of prior studies are shown. 44 2.11 Country map in the Middle East . 46 2.12 Illustration of a skewed distribution of WPD at Yanbu . 47 2.13 Average wind speed (m s−1) is computed at 50 m AGL using the wind fields reconstructed from the MERRA data. Selected locations of prior studies (table 2.1) are shown. 48 2.14 Fractional change in WPD at 80 m and 140 m from 50 m over temporal median of roughness lengths . 49 2.15 Distribution of fractional changes in rCV at 80 m and 140 m from 50 m out of 1638 grid cells . 50 2.16 Distribution of differences in MEL at 80 m and 140 m from 50 m out of 1638 grid cells . 51 3.1 (a) Definition of wind speed maxima (WSM) and its associated altitude (H), where the size of the box indicates the magnitude of the wind speed (b) Power curve, power (kW) as a function of wind speed (m/s), of a lift-type groud-based AWES of 3 MW rating.
Load more

Recommended publications
  • Optimizing the Visual Impact of Onshore Wind Farms Upon the Landscapes – Comparing Recent Planning Approaches in China and Germany
    Ruhr-Universität Bochum Dissertation Submission to the Ruhr-Universität Bochum, Faculty of Geosciences For the degree of Doctor of natural sciences (Dr. rer. nat) Submitted by: Jinjin Guan. MLA Date of the oral examination: 16.07.2020 Examiners Dr. Thomas Held Prof. Dr. Harald Zepp Prof. Dr. Guotai Yan Prof. Dr. Wolfgang Friederich Prof. Dr. Harro Stolpe Keywords Onshore wind farm planning; landscape; landscape visual impact evaluation; energy transition; landscape visual perception; GIS; Germany; China. I Abstract In this thesis, an interdisciplinary Landscape Visual Impact Evaluation (LVIE) model has been established in order to solve the conflicts between onshore wind energy development and landscape protection. It aims to recognize, analyze, and evaluate the visual impact of onshore wind farms upon landscapes and put forward effective mitigation measures in planning procedures. Based on literature research and expert interviews, wind farm planning regimes, legislation, policies, planning procedures, and permission in Germany and China were compared with each other and evaluated concerning their respective advantages and disadvantages. Relevant theories of landscape evaluation have been researched and integrated into the LVIE model, including the landscape connotation, landscape aesthetics, visual perception, landscape functions, and existing evaluation methods. The evaluation principles, criteria, and quantitative indicators are appropriately organized in this model with a hierarchy structure. The potential factors that may influence the visual impact have been collected and categorized into three dimensions: landscape sensitivity, the visual impact of WTs, and viewer exposure. Detailed sub-indicators are also designed under these three topics for delicate evaluation. Required data are collected from official platforms and databases to ensure the reliability and repeatability of the evaluation process.
    [Show full text]
  • Assessment of Wind Energy Resources for Electricity Generation Using WECS in North-Central Region, Nigeria
    Renewable and Sustainable Energy Reviews 15 (2011) 1968–1976 View metadata, citation and similar papers at core.ac.uk brought to you by CORE Contents lists available at ScienceDirect provided by Covenant University Repository Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser Assessment of wind energy resources for electricity generation using WECS in North-Central region, Nigeria Olayinka S. Ohunakin Mechanical Engineering Department, Covenant University, P.M.B 1023, Ota, Ogun State, Nigeria article info abstract Article history: This paper presents a statistical analysis of wind characteristics of five locations covering the North- Received 1 October 2010 Central (NC) geo-political zone, Nigeria, namely Bida, Minna, Makurdi, Ilorin and Lokoja using Weibull Accepted 4 January 2011 distribution functions on a 36-year (1971–2007) wind speed data at 10 m height collected by the mete- orological stations of NIMET in the region. The monthly, seasonal and annual variations were examined Keywords: while wind speeds at different hub heights were got by extrapolating the 10 m data using the power law. Weibull distribution The results from this investigation showed that all the five sites will only be adequate for non-connected Wind energy conversion systems electrical and mechanical applications with consideration to their respective annual mean wind speeds Mean wind speeds Wind energy of 2.747, 4.289, 4.570, 4.386 and 3.158 m/s and annual average power densities of 16.569, 94.113, 76.399, 2 Nigeria 71.823 and 26.089 W/m for Bida, Minna, Makurdi, Ilorin and Lokoja in that order.
    [Show full text]
  • 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.
    [Show full text]
  • 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].
    [Show full text]
  • 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.
    [Show full text]
  • Offshore Renewables: Offshore
    OFFSHORE RENEWABLES: OFFSHORE OFFSHORE AN ACTION AGENDA FOR DEPLOYMENT RENEWABLES An action agenda for deployment OFFSHORE RENEWABLES A CONTRIBUTION TO THE G20 PRESIDENCY An action agenda for deployment A CONTRIBUTION TO THE G20 PRESIDENCY www.irena.org 2021 © IRENA 2021 © IRENA 2021 Unless otherwise stated, material in this publication may be freely used, shared, copied, reproduced, printed and/or stored, provided that appropriate acknowledgement is given of IRENA as the source and copyright holder. Material in this publication that is attributed to third parties may be subject to separate terms of use and restrictions, and appropriate permissions from these third parties may need to be secured before any use of such material. Citation: IRENA (2021), Offshore renewables: An action agenda for deployment, International Renewable Energy Agency, Abu Dhabi. ISBN 978-92-9260-349-6 About IRENA The International Renewable Energy Agency (IRENA) serves as the principal platform for international co-operation, a centre of excellence, a repository of policy, technology, resource and financial knowledge, and a driver of action on the ground to advance the transformation of the global energy system. An intergovernmental organisation established in 2011, IRENA promotes the widespread adoption and sustainable use of all forms of renewable energy, including bioenergy, geothermal, hydropower, ocean, solar and wind energy, in the pursuit of sustainable development, energy access, energy security and low-carbon economic growth and prosperity. www.irena.org Acknowledgements IRENA is grateful for the Italian Ministry of Foreign Affairs and International Cooperation (Directorate-General for Global Affairs, DGMO) contribution that enabled the preparation of this report in the context of the Italian G20 Presidency.
    [Show full text]
  • IEA WIND 2012 Annual Report
    IEA WIND 2012 Annual Report Executive Committee of the Implementing Agreement for Co-operation in the Research, Development, and Deployment of Wind Energy Systems of the International Energy Agency July 2013 ISBN 0-9786383-7-9 Message from the Chair Welcome to the IEA In 2013, we expect to approve Recommended Wind 2012 Annual Re- Practices on social acceptance of wind energy proj- port of the coopera- ects, on remote wind speed sensing using SODAR tive research, develop- and LIDAR, and on conducting wind integration ment, and deployment studies. The 12 active research tasks of IEA wind will (R,D&D) efforts of our offer members many options to multiply their na- member governments and tional research programs, and a new task on ground- organizations. IEA Wind based testing of wind turbines and components is helps advance wind en- being discussed for approval in 2013. ergy in countries repre- With market challenges and ever-changing re- senting 85% of the world's search issues to address, the IEA Wind co-operation wind generating capacity. works to make wind energy an ever better green In 2012 record ca- option for the world's energy supply. Considering pacity additions (MW) these accomplishments and the plans for the coming were seen in nine member countries, and coop- years, it is with great satisfaction and confidence that erative research produced five final technical re- I hand the Chair position to Jim Ahlgrimm of the ports as well as many journal articles and confer- United States. ence papers. The technical reports include: • IEA
    [Show full text]
  • Potential Offshore Wind Energy Areas in California: an Assessment of Locations, Technology, and Costs Walter Musial, Philipp Beiter, Suzanne Tegen, and Aaron Smith
    Potential Offshore Wind Energy Areas in California: An Assessment of Locations, Technology, and Costs Walter Musial, Philipp Beiter, Suzanne Tegen, and Aaron Smith National Renewable Energy Laboratory This report is available from the Bureau of Ocean Energy Management by referencing OCS Study BOEM 2016-074. The report may be downloaded from BOEM’s Recently Completed Environmental Studies –Pacific web page at http://www.boem.gov/Pacific-Completed-Studies/. This study was funded by the U.S. Department of the Interior, Bureau of Ocean Energy Management. NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the AlliaAlliancence for Sustainable Energy, LLCLLC This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Technical Report NREL/TP-5000-67414 December 2016 Contract No. DE-AC36-08GO28308 Potential Offshore Wind Energy Areas in California: An Assessment of Locations, Technology, and Costs Walter Musial, Philipp Beiter, Suzanne Tegen and Aaron Smith National Renewable Energy Laboratory Prepared under IAG No. M14PG00038; task number WFHA.1005 This report is available from the Bureau of Ocean Energy Management by referencing OCS Study BOEM 2016-074. The report may be downloaded from BOEM’s Recently Completed Environmental Studies – Pacific webpage at http://www.boem.gov/Pacific-Completed-Studies/ This study was funded by the U.S. Department of the Interior, Bureau of Ocean Energy Management. NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
    [Show full text]
  • Wind Resource Assessment Using the Wasp Software
    Downloaded from orbit.dtu.dk on: Oct 06, 2021 Wind resource assessment using the WAsP software Mortensen, Niels Gylling Publication date: 2018 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Mortensen, N. G. (2018). Wind resource assessment using the WAsP software. DTU Wind Energy. DTU Wind Energy E No. 174 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. 46200 Planning and Development of Wind Farms: Wind resource assessment using WAsP software Wind Energy E Report 2019 Department of Niels G. Mortensen DTU Wind Energy E-0174 December 2018 Authors: Niels G. Mortensen DTU Wind Energy E-0174 Title: Wind resource assessment using WAsP software December 2018 Department: Wind Energy Summary (max 2000 characters): These course notes are intended for the three-week course 46200 Planning and Development of Wind Farms given each year at the Technical University of Denmark.
    [Show full text]
  • Introduction to Airborne Wind Energy
    Introduction to Airborne Wind Energy March 2020 Udo Zillmann Kristian Petrick Stefanie Thoms www.airbornewindeurope.org 1 § Introduction to Airborne Wind Energy Agenda § Airborne Wind Energy – principle and concepts § Advantages § Challenges § Airborne Wind Europe § Meeting with DG RTD www.airbornewindeurope.org 2 § AWE principle and concepts Overview § Principles § Ground generation § On-board generation § Different types § Soft wing § Rigid wing § Semi-rigid wing § Other forms www.airbornewindeurope.org 3 § AWE principle Ground generation (“ground gen”) or yo-yo principle Kite flies out in a spiral and creates a tractive pull force to the tether, the winch generates electricity as it is being reeled out. Kite Tether Winch Tether is retracted back as kite flies directly back to the starting point. Return phase consumes a few % of Generator power generated, requires < 10 % of total cycle time. www.airbornewindeurope.org 4 § AWE principle On-board generation (“fly-gen”) Kite flies constantly cross-wind, power is produced in the on-board generators and evacuated through the tether www.airbornewindeurope.org 5 § AWE principle The general idea: Emulating the movement of a blade tip but at higher altitudes Source: Erc Highwind https://www.youtube.com/watch?v=1UmN3MiR65E Makani www.airbornewindeurope.org 6 § AWE principle Fundamental idea of AWE systems • With a conventional wind turbine, the outer 20 % of the blades (the fastest moving part) generates about 60% of the power • AWE is the logical step to use only a fast flying device that emulates the blade tip. www.airbornewindeurope.org 7 § AWE concepts Concepts of our members – soft, semi-rigid and rigid wings www.airbornewindeurope.org 8 § AWE Concept Overview of technological concepts Aerostatic concepts are not in scope of this presentation Source: Ecorys 2018 www.airbornewindeurope.org 9 § AWE Concept Rigid kite with Vertical Take Off and Landing (VTOL) 1.
    [Show full text]
  • 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.............................................
    [Show full text]
  • IEA Wind Technology Collaboration Programme
    IEA Wind Technology Collaboration Programme 2017 Annual Report A MESSAGE FROM THE CHAIR Wind energy continued its strong forward momentum during the past term, with many countries setting records in cost reduction, deployment, and grid integration. In 2017, new records were set for hourly, daily, and annual wind–generated electricity, as well as share of energy from wind. For example, Portugal covered 110% of national consumption with wind-generated electricity during three hours while China’s wind energy production increased 26% to 305.7 TWh. In Denmark, wind achieved a 43% share of the energy mix—the largest share of any IEA Wind TCP member countries. From 2010-2017, land-based wind energy auction prices dropped an average of 25%, and levelized cost of energy (LCOE) fell by 21%. In fact, the average, globally-weighted LCOE for land-based wind was 60 USD/ MWh in 2017, second only to hydropower among renewable generation sources. As a result, new countries are adopting wind energy. Offshore wind energy costs have also significantly decreased during the last few years. In Germany and the Netherlands, offshore bids were awarded at a zero premium, while a Contract for Differences auction round in the United Kingdom included two offshore wind farms with record strike prices as low as 76 USD/MWh. On top of the previous achievements, repowering and life extension of wind farms are creating new opportunities in mature markets. However, other challenges still need to be addressed. Wind energy continues to suffer from long permitting procedures, which may hinder deployment in many countries. The rate of wind energy deployment is also uncertain after 2020 due to lack of policies; for example, only eight out of the 28 EU member states have wind power policies in place beyond 2020.
    [Show full text]