DOCSLIB.ORG

Wind Generation Forecasting Methods and Proliferation of Artificial Neural

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

sustainability Review Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend Muhammad Shahzad Nazir 1,* , Fahad Alturise 2 , Sami Alshmrany 3, Hafiz. M. J Nazir 4, Muhammad Bilal 5 , Ahmad N. Abdalla 6, P. Sanjeevikumar 7 and Ziad M. Ali 8,9 1 Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China 2 Computer Department, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Saudi Arabia; [email protected] 3 Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; [email protected] 4 Institute of Advance Space Research Technology, School of Networking, Guangzhou University, Guangzhou 510006, China; [email protected] 5 School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 6 Faculty of Information and Communication Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 7 Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark; [email protected] 8 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia; [email protected] 9 Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt * Correspondence: [email protected] or [email protected]; Tel.: +86-1322-271-7968 Received: 8 April 2020; Accepted: 23 April 2020; Published: 6 May 2020 Abstract: To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method. Keywords: environment; sustainability; quality of life; forecasting; instrument; ANN; wind energy 1. Introduction The advancement of civilizations, and their capability to withstand bulk populations, are similar challenges to changes in the volume and nature of energy available to meet the demand for nourishment, quality of life and to complete tasks [1–3]. Lower access to energy is an undesirable impact of poverty Sustainability 2020, 12, 3778; doi:10.3390/su12093778 www.mdpi.com/journal/sustainability SustainabilityTheThe advancementadvancement2020, 12, 3778 ofof civilizations,civilizations, andand theirtheir capabicapabilitylity toto withstandwithstand bulkbulk populations,populations, areare similarsimilar2 of 27 challengeschallenges toto changeschanges inin thethe volumevolume andand naturenature ofof energyenergy availableavailable toto meetmeet thethe demanddemand forfor nourishment,nourishment, qualityquality ofof lifelife andand toto completecomplete tasktaskss [1–3].[1–3]. LowerLower accessaccess toto energyenergy isis anan undesirableundesirable impactimpactand the ofof most povertypoverty probable andand thethe cause mostmost of probable inferiorprobable e causefficauseciency ofof inferior andinferior a human’s efficiencyefficiency quality andand aa of human’shuman’s life. Indeed, qualityquality energy ofof life.life. is Indeed,Indeed,fundamental energyenergy to isis providing fundamentalfundamental suffi tocientto providingproviding services sufficiesufficie such asntnt food, servicesservices primary suchsuch as healthcare,as food,food, primaryprimary water, healthcare,healthcare, education, water,water,communication, education,education, and communication,communication, reasonable employment. andand reasonablereasonable The employment.employment. majority of electricity TheThe majoritymajority used ofof by electricityelectricity our societies usedused hasbyby ourourbeen societiessocieties generated hashas from beenbeen fossil generatedgenerated and nuclear fromfrom fossilfossil fuels, andand which nuclnucl areearear today fuels,fuels, facing whichwhich severe areare todaytoday issues, facingfacing such severe assevere diminished issues,issues, suchsuchsupply asas diminished anddiminished security, supplysupply financial andand a ff security,security,ordability, financialfinancial ecological affoaffo sustainabilityrdability,rdability, ecologicalecological and disaster susustainabilitystainability hazards andand [4,5 ].disasterdisaster For fix hazardshazardsthese undesirable [4,5].[4,5]. ForFor issues, fixfix thesethese many undesirableundesirable countries are issues,issues, enacting manymany sustainable countriescountries energy areare enacting policiesenacting to sustainablesustainable focus on increasing, energyenergy policiespoliciesintegrating toto focusfocus and on developingon increasing,increasing, renewable integratingintegrating energy andand developing technologies.developing renewablerenewable Resources energyenergy are increasing technologies.technologies. significantly ResourcesResources in arearethe increasing energyincreasing mix significantlysignificantly around the globeinin thethe [ 4energyenergy,6]. Among mixmix arar renewableoundound thethe energy globeglobe [4,6]. resources,[4,6]. AmongAmong the renewablerenewable wind is one energyenergy of the resources,resources,oldest renewable thethe windwind sources isis oneone used ofof thethe for oldestoldest power renewarenewa generationbleble sourcessources worldwide. usedused Globalforfor powerpower wind generationgeneration power generation worldwide.worldwide. has GlobalGlobalreached windwind about powerpower 20% annually generationgeneration within hashas the reachedreached past decade. aboutabout Figure20%20% annuallyannually1 depicts withinwithin the annual thethe past additionpast decade.decade. and previousFigureFigure 11 depictsdepictsyear’s capacity thethe annualannual atlas additionaddition of wind andand power previousprevious (2014–2018), year’syear’s while capacitycapacity the atlas worldatlas ofof top windwind 10 countries powerpower (2014–2018),(2014–2018), wind energy whilewhile capacity thethe worldworldis presented toptop 1010 incountriescountries Figure2 wind[wind7]. energyenergy capacitycapacity isis presentedpresented inin FigureFigure 22 [7].[7]. FigureFigure 1.1. TheTheThe worldworld world annualannual annual additionaddition addition andand and previousprevious previous year’syear’s capacitycapacity capacity atlasatlas atlas ofof of windwind wind powerpower (2014–2018).(2014–2018). FigureFigureFigure 2.2. TheTheThe worldworld world toptop top 1010 10 countriescountries countries atlasatlas atlas ofof of instinst installedalledalled windwind powerpower capacitycapacity inin 20182018 [7].[7]. [7]. SustainabilitySustainabilityWind 20202020 energy,, 1212,, x;x; has doi:doi: become FORFOR PEERPEER an REVIEWREVIEW essential source of energy worldwide,www.www. andmdpi.com/journal inmdpi.com/journal 2018, the global/sustainability/sustainability capacity reached 600 GW, so the construction of a new generation of wind energy varies from year to year and by region. For example, in Europe, wind power capacity has decreased by 32% in 2018, compared to Sustainability 2020, 12, 3778 3 of 27 2017. The countries with the largest wind capacity are China, the United States, the United Kingdom, Germany, Canada, Italy, France, India, and Brazil [7]. China’s installed capacity is 221 GW, and is the world leader in wind energy, producing more than a third of the world’s capacity. The province of Gansu has the largest onshore wind farm in the world, and currently has an output of 7965 MW: five times more than its closest rival. The farm currently operates at only 40% of its capacity, and a total of 120,000 MW can be installed by 2020 to achieve a total capacity of 20,000 MW (20 GW), which is expected to cost USD 17.5 billion. Despite their size, Gansu turbines appear to be “scarecrow inactive”, due to lower demand [8,9]. The USA occupies second place, with 96.4 GW installed capacity, and is particularly resistant to onshore wind energy. Six of the ten largest onshore wind farms are located in the United States. These include the Alta Wind Energy Center in California, the second-largest onshore wind farm in the world with an output of 1548 MW, Shepherd’s Flat wind farm in Oregon (845 MW), and the Roscoe wind farm in Texas (781 MW). Texas alone produces 24.9 GW of American wind power, delivering more wind power than the other 25 countries. Germany has the highest installed wind power in Europe with 59.3 GW. The
Recommended publications
  • History and Trends in Solar Irradiance and PV Power Forecasting: a Preliminary Assessment and Review Using Text Mining

    History and Trends in Solar Irradiance and PV Power Forecasting: a Preliminary Assessment and Review Using Text Mining

    History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining Dazhi Yanga,∗, Jan Kleisslb, Christian A. Gueymardc, Hugo T. C. Pedrob, Carlos F. M. Coimbrab aSingapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A∗STAR), Singapore bCenter for Renewable Resources and Integration, Center for Energy Research Department of Mechanical and Aerospace Engineering, University of California, San Diego, CA, USA cSolar Consulting Services, Colebrook, NH, USA Abstract Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and orga- nizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineer- ing topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.
  • Characterisation of Intra-Hourly Wind Power Ramps at the Wind Farm Scale and Associated Processes

    Characterisation of Intra-Hourly Wind Power Ramps at the Wind Farm Scale and Associated Processes

    Wind Energ. Sci., 6, 131–147, 2021 https://doi.org/10.5194/wes-6-131-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes Mathieu Pichault1, Claire Vincent2, Grant Skidmore1, and Jason Monty1 1Department of Mechanical Engineering, The University of Melbourne, Melbourne, Victoria 3010, Australia 2School of Earth Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia Correspondence: Mathieu Pichault ([email protected]) Received: 12 May 2020 – Discussion started: 5 June 2020 Revised: 15 September 2020 – Accepted: 8 December 2020 – Published: 19 January 2021 Abstract. One of the main factors contributing to wind power forecast inaccuracies is the occurrence of large changes in wind power output over a short amount of time, also called “ramp events”. In this paper, we assess the behaviour and causality of 1183 ramp events at a large wind farm site located in Victoria (southeast Australia). We address the relative importance of primary engineering and meteorological processes inducing ramps through an automatic ramp categorisation scheme. Ramp features such as ramp amplitude, shape, diurnal cycle and seasonality are further discussed, and several case studies are presented. It is shown that ramps at the study site are mostly associated with frontal activity (46 %) and that wind power fluctuations tend to plateau before and after the ramps. The research further demonstrates the wide range of temporal scales and behaviours inherent to intra-hourly wind power ramps at the wind farm scale. 1 Introduction hourly) ramp forecasts (Zhang et al., 2017; Cui et al., 2015; Gallego et al., 2015a).
  • World Bank Document

    World Bank Document

    July 2019 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized © 2019 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: [email protected]. Furthermore, the ESMAP Program Manager would appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above or to [email protected]. Cover Image A system in University of British Columbia (UBC) with pyranometer and pyrgeometer for meteorological observations related to solar insolation.
  • Probabilistic Solar Power Forecasting: Long Short-Term Memory Network Vs Simpler Approaches

    Probabilistic Solar Power Forecasting: Long Short-Term Memory Network Vs Simpler Approaches

    Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches Vinayak Sharmaa,∗, Jorge Angel´ Gonz´alezOrdianob,c, Ralf Mikutb, Umit Calid aUniversity of North Carolina at Charlotte bInstitute for Automation and Applied Informatics, Karlsruhe Institute of Technology cColorado State University dNorwegian University of Science and Technology Abstract The high penetration of volatile renewable energy sources such as solar, make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14). Keywords: GEFCom14, Neural Networks, Quantile Regressions, LSTM, Probabilistic Forecasting 1. Introduction arXiv:2101.08236v1 [cs.LG] 20 Jan 2021 Over the past couple of years solar power has become one of the most pop- ular renewable energy sources (RES). Unfortunately, the generation of solar power depends completely on the Sun [5]. This dependency on weather adds uncertainty and variability to the generation of solar power.
  • 5 Minute Wind Forecasting Challenge: Exelon and GE's Predix

    5 Minute Wind Forecasting Challenge: Exelon and GE's Predix

    The 5 Minute Wind Forecasting Challenge: Exelon and GE’s Predix At a Glance A move toward digital industrial transformation As a leading utility company with more than $31 billion in global Renewable Energy revenues in 2016 and over 32 gigawatts (GW) of total generation, Exelon knows the importance of taking a strategic view of digital transformation across its lines of business. Challenge Exelon sought to optimize wind power forecasting by predicting wind Exelon was developing strategies for managing its various generation ramp events, enabling the company to dispatch power that could not be assets across nuclear, fossil fuels, wind, hydro, and solar power as well monetized otherwise. The result is higher revenue for Exelon’s large-scale wind farm operations. as determining how it would leverage the enormous amount of data those assets would generate going forward. Solution GE and Exelon teams co-innovated to build a solution on Predix that In evaluating its strategies, the company reviewed its current increases wind forecasting accuracy by designing a new physical and statistical wind power forecast model that uses turbine data on-premises OT/IT infrastructure across its entire energy portfolio. together with weather forecasting data. This model now represents Business leaders looked at the system administration challenges the industry-leading forecasting solution (as measured by a substantial and costs they would face to maintain the current infrastructure, let reduction in under-forecasting). alone use it as a basis for driving new revenue across its business Results units. This assessment made digital transformation an even greater Exelon’s wind forecasting prediction accuracy grew signifcantly, enabling imperative, and inspired discussions about how Exelon could leverage higher energy capture valued at $2 million per year.
  • NAWEA 2015 Symposium Book of Abstracts

    NAWEA 2015 Symposium Book of Abstracts

    NAWEA 2015 Symposium Tuesday 09 June 2015 - Thursday 11 June 2015 Virginia Tech Campus Goodwin Hall Book of Abstracts i Table of contents Wind Farm Layout Optimization Considering Turbine Selection and Hub Height Variation ....................... 1 Graduate Education Programs in Wind Energy ................................................................................. 2 Benefits of vertically-staggered wind turbines from theoretical analysis and Large-Eddy Simulations ........... 3 On the Effects of Directional Bin Size when Simulating Large Offshore Wind Farms with CFD ................... 7 A game-theoretic framework to investigate the conditions for cooperation between energy storage operators and wind power producers ............................................................................................................ 9 Detection of Wake Impingement in Support of Wind Plant Control ....................................................... 11 Sensitivity of Wind Turbine Airfoil Sections to Geometry Variations Inherent in Modular Blades ................ 15 Exploiting the Characteristics of Kevlar-Wall Wind Tunnels for Conventional Aerodynamic Measurements with Implications for Testing of Wind Turbine Sections ...................................................................... 16 Spatially Resolved Wind Tunnel Wake Measurements at High Angles of Attack and High Reynolds Numbers Using a Laser-Based Velocimeter .................................................................................................... 17 Windtelligence:
  • Solar Forecasting: Maximizing Its Value for Grid Integration

    Solar Forecasting: Maximizing Its Value for Grid Integration

    Solar Forecasting: Maximizing its value for grid integration Introduction The forecasting of power generated by variable energy resources such as wind and solar has been the focus of academic and industrial research and development for as long as significant amounts of these renewable energy resources have been connected to the electric grid. The progress of forecasting capabilities has largely followed the penetration of the respective resources, with wind forecasting having achieved a more mature state-of-the-art compared to its solar equivalent [Lew 2010]. Still in the last 5 years, there has been substantial and material progress in the state-of-the-art of solar forecasting [Kleissl 2016]. Numerical Weather Prediction (NWP) models became more sophisticated in assessing cloud interactions with aerosols; infrared satellite imagery allowed discovery of pre-sunrise cloud formations; advanced data processing methods such as deep machine learning became increasingly accessible; probabilistic forecasts began replacing deterministic ones; and, in balancing areas with high PV penetration, solar forecasts are now used operationally. Bulk grid integration As solar electricity penetration in the distribution grid is increasing, the power generated by those Distributed Energy Resources (DERs) needs to be taken into account in the operations and planning of IPPs, ISOs, and Balancing Authorities [Mills 2013]. Since the reliable performance of the bulk grid depends on the balancing of a continuously varying load with equal amounts of generation, knowledge of the load ahead of time (forecasting) is necessary for the economically optimal – and technically feasible – dispatch of generation sources. Solar electricity generation presents two challenges to the process above. First, a very large fraction of it comes from distributed PV systems (DPV) that are connected behind-the-meter (BTM) and are thus only visible to the system operator as load.
  • A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

    A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Enlighten energies Review A Critical Review of Wind Power Forecasting Methods—Past, Present and Future Shahram Hanifi 1, Xiaolei Liu 1,* , Zi Lin 2,3,* and Saeid Lotfian 2 1 James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; s.hanifi[email protected] 2 Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK; saeid.lotfi[email protected] 3 Department of Mechanical & Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK * Correspondence: [email protected] (X.L.); [email protected] (Z.L.) Received: 16 June 2020; Accepted: 20 July 2020; Published: 22 July 2020 Abstract: The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc.
  • Energia Eólica Panfleto Dez07

    Energia Eólica Panfleto Dez07

    EEEEnnnneeeerrrrggggiiiiaaaa EEEEóóóólllliiiiccccaaaa DDDeeezzzeeemmmbbbrrrooo 222000000777 INEGI O INEGI - Instituto de Engenhar ia Mecânica e Gestão Industrial tem procurado , desde a sua criação, fomentar e empenhar -se no estudo da utilização das fontes de energia não convencionais, e na poupança e utilização racional da energia. Dedicando -se ao estudo do aproveitamento da Energia Eólica, o INEGI pretende apoiar o desenvolvimento das energias renováveis, contribuindo para a diversificação dos recursos primários usados na geração de elect ricidade e para a preservação do meio ambiente. Desde 1991, o INEGI tem uma equipa especialmente dedicada à Energia Eólica . Para além do planeamento e condução de campanhas de avaliação do recurso eólico, o INEGI disponibiliza hoje diversos outros serviço s relacionados com o tema, como sejam os cálculos de produtividade e a optimização da configuração de parques eólicos, a realização de estudos de viabilidade técnico -económica de projectos, o apoio na elabor ação de cadernos de encargos, apreciação de propo stas e comparação de soluções, a avaliação do desempenho de aerogeradores, a verificação de garantias de produção , a realização de auditorias e avaliaç ões de projectos para instituições financeiras e outras e o apoio em acções de planeamento e ordenamento. Inicialmente restritas ao Norte e Centro de Portugal, as actividades do INEGI neste domínio estendem -se actualmente a todo o país e, desde 1999, também ao estrangeiro. Fazendo uso da experiência adquirida pela participação dos colaboradores em projectos i nternacionais, foram adoptadas metodologias e técnicas de operação que permitem fornecer aos seus clientes serviços de qualidade. Através do contacto com institutos congéneres e consultores de toda a Europa, e da participação em conferências e seminários i nternacionais, procura o INEGI manter -se actualizado nos recursos e nas práticas seguidas.
  • Make the Right Connections Photo: Roehle Gabriele

    Make the Right Connections Photo: Roehle Gabriele

    Make the right connections Photo: Roehle gabriele Event Guide EWEA Annual Event 14 - 17 March 2011, Brussels - Belgium Table of contents Conference ....................................................................................................... 4 - 44 Conference programme ....................................................................................... 4 Poster presentations ......................................................................................... 26 Belgian Day ...................................................................................................... 38 Workshops ....................................................................................................... 40 Side events ...................................................................................................... 42 Useful Information .......................................................................................... 46 - 52 Practical information ......................................................................................... 46 Relaxation area ................................................................................................. 49 Social events .................................................................................................... 50 Sustainability ................................................................................................... 52 Thank you ...................................................................................................... 54 - 61 Supporting organisations
  • Potential Benefits of Wind Forecasting and the Application of More-Care in Ireland Ruairi Costello, Damian Mccoy, Philip O’Donnel, Geoff Dutton, Georges Kariniotakis

    Potential Benefits of Wind Forecasting and the Application of More-Care in Ireland Ruairi Costello, Damian Mccoy, Philip O’Donnel, Geoff Dutton, Georges Kariniotakis

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Archive Ouverte en Sciences de l'Information et de la Communication Potential benefits of wind forecasting and the application of more-care in Ireland Ruairi Costello, Damian Mccoy, Philip O’Donnel, Geoff Dutton, Georges Kariniotakis To cite this version: Ruairi Costello, Damian Mccoy, Philip O’Donnel, Geoff Dutton, Georges Kariniotakis. Potential benefits of wind forecasting and the application of more-care in Ireland. Med power 2002, Nov Athènes, Greece. hal-00534004 HAL Id: hal-00534004 https://hal-mines-paristech.archives-ouvertes.fr/hal-00534004 Submitted on 4 May 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Potential Benefits of Wind Forecasting and the Application of More-Care in Ireland R. Costello*, D. McCoy, P. O’Donnell A.G. Dutton G.N. Kariniotakis ESB National Grid CLRC Rutherford Appleton Laboratory Ecole des Mines de Paris/ARMINES, Power System Operations Energy Research Unit Centre d’Energétique Ireland United Kingdom France. * ESB National Grid, 27 Fitzwilliam Street Lower, Dublin 2, Tel: +353-1-7027245, Fax: +353-1- 4170539, [email protected] ABSTRACT: The Irish Electricity System and its future II.
  • Financial Statement 2012 of Enel Green Power S.P.A

    Financial Statement 2012 of Enel Green Power S.P.A

    Annual Report 2012 Annual Report 2012 Contents Report on operations Consolidated financial statements The Enel Green Power structure | 7 Consolidated Income Statement | 90 Corporate boards | 8 Statement of Consolidated Comprehensive Income | 91 Letter to the shareholders and other stakeholders | 10 Consolidated Balance Sheet | 92 Summary of results | 12 Statement of Changes in Consolidated Shareholders’ Equity | 93 Significant events in 2012 | 17 Consolidated Statement of Cash Flows | 94 The contribution of renewable energy to sustainability | 24 Notes to the financial statements | 95 Reference scenario | 28 > Enel Green Power and the financial markets | 28 Economic and energy conditions in 2012 | 30 Corporate governance > Economic developments | 30 > Developments in the main market indicators | 31 Corporate governance and ownership > International commodity prices | 32 structures report | 162 Electricity markets | 33 Overview of the Group’s performance and financial position | 52 Declaration of the Chief Executive Officer Performance and financial position by segment | 62 and the officer responsible for the preparation > Italy and Europe | 64 of corporate financial reports > Iberia and Latin America | 66 > North America | 68 Declaration of the Chief Executive Officer > Retail | 69 and the officer responsible for the preparation Main risks and uncertainties | 71 of corporate financial reports | 202 Outlook | 73 Innovation | 74 Human resources and organization | 77 Annexes Regulations governing non-EU subsidiaries | 83 Regulations governing