DOCSLIB.ORG

On the Benefits of Distributed Generation of Wind Energy in Europe

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
On the Benefits of Distributed Generation of Wind Energy in Europe 2QWKH%HQHILWVRI'LVWULEXWHG*HQHUDWLRQRI :LQG(QHUJ\LQ(XURSH 9RP)DFKEHUHLFK3K\VLNGHU &DUOYRQ2VVLHW]N\8QLYHUVLWlW2OGHQEXUJ ]XU(UODQJXQJGHV*UDGHVHLQHV 'RNWRUVGHU1DWXUZLVVHQVFKDIWHQ 'UUHUQDW DQJHQRPPHQH'LVVHUWDWLRQ YRQ*UHJRU*LHEHO JHEDPLQ:LHVEDGHQ I (UVWUHIHUHQW3URI'U-UJHQ3DULVL .RUHIHUHQW3URI'U6¡UHQ/DUVHQ .RUHIHUHQW3URI'U-UJHQ3HLQNH 7DJGHU'LVSXWDWLRQ 'HWHUVY UWDWVSnLV URPIUHPWLGHQ 5REHUW6WRUP3HWHUVHQJHQ6WRUP3 II 7DEOHRI&RQWHQWV 7$%/(2)&217(176 ,,, 7$%/(2)$%%5(9,$7,216 9 7$%/(2)6<0%2/6 9,, ,1752'8&7,21 02'(/6$1'0(7+2'6 2.1 NUMERICAL WEATHER PREDICTION......................................................................................................................3 *HQHUDOIHDWXUHVRI1:3PRGHOV +,5/$0 1HVWHG*ULG0RGHO 2.2 LOCAL SCALE MODELS...........................................................................................................................................6 :$V3 3$5. 2.3 SHORT TERM FORECASTING ..................................................................................................................................7 3HUVLVWHQFHDQGVLPLODUPRGHOV 1HXUDOQHWZRUNV 7KH5LV¡0RGHO 2WKHUPRGHOV 2.4 MODEL OUTPUT STATISTICS................................................................................................................................11 2.5 KALMAN FILTER / EXTENDED KALMAN FILTER...................................................................................................13 2.6 OPTIMISATION: DOWNHILL SIMPLEX, GENETIC ALGORITHMS .............................................................................15 2.7 NATIONAL GRID MODEL......................................................................................................................................16 '$7$ 3.1 DANISH WIND DATA............................................................................................................................................19 3.2 ENGLAND AND WALES WIND DATA ....................................................................................................................19 3.3 CEGB ENGLAND AND WALES GRID DATA..........................................................................................................19 3.4 IOWA WIND DATA ...............................................................................................................................................19 3.5 IOWA GRID DATA ................................................................................................................................................19 3.6 EUROPEAN WIND DATA.......................................................................................................................................20 3.7 EUROPEAN GRID DATA........................................................................................................................................22 3.8 EUROPEAN WIND DATA FROM REANALYSIS ........................................................................................................23 02'(/28738767$7,67,&6 4.1 INTRODUCTION ....................................................................................................................................................25 4.2 NEW REFERENCE.................................................................................................................................................26 4.3 STATIC MOS IMPLEMENTATIONS.........................................................................................................................29 4.4 KALMAN FILTER RESULTS ...................................................................................................................................34 4.5 OPTIMISATION OF THE KF STIFFNESS ...................................................................................................................36 %(1(),762)*22')25(&$67,1* 5.1 INTRODUCTION ....................................................................................................................................................39 5.2 NGM – SENSITIVITY ANALYSIS ...........................................................................................................................41 III 60227+,1*2)',675,%87(':,1'32:(5*(1(5$7,21 6.1 CROSS-CORRELATIONS ........................................................................................................................................48 6.2 SMOOTHING OF SPATIALLY AVERAGED TIME SERIES ............................................................................................50 6.3 PROPERTIES OF THE AVERAGED TIME SERIES .......................................................................................................52 &$3$&,7<())(&762):,1'(1(5*<,1(8523( 7.1 INTRODUCTION ....................................................................................................................................................55 7.2 DEFINITIONS AND TERMINOLOGY.........................................................................................................................56 7.3 PREVIOUS WORKS ................................................................................................................................................56 7.4 BENEFITS OF DISTRIBUTED PRODUCTION..............................................................................................................62 7.5 REPLACED FOSSIL FUEL CAPACITY - THE SINGLE EVENT PROBLEM .......................................................................65 7.6 VARIATIONAL ANALYSIS ......................................................................................................................................73 7.7 CONCLUSIONS......................................................................................................................................................75 7+(/21*7(505($1$/<6,6'$7$ 6800$5<&21&/86,216 $&.12:/('*(0(176 $33(1',; FORMULAE DESCRIBING THE WIND SPEED ....................................................................................................................86 STATISTICAL FORMULAE ...............................................................................................................................................86 5()(5(1&(6 IV 7DEOHRI$EEUHYLDWLRQV a.g.l. above ground level a.s.l. above surface level AT, BE, CH, DE… Country codes according to ISO 3166 [1] CC Capacity Credit CCGT Combined Cycle Gas Turbine CEGB Central Electricity Generating Board (UK) CR&W Combustible Renewables & Waste DMI Danish Meteorological Institute DoE (US) Department of Energy DTU Danish Technical University DWD Deutscher Wetterdienst (German meteorological service) ECMWF European Centre of Medium-Range Weather Forecasts EDF Electricité de France EdP Electricidade de Portugal EIA Energy Information Authority EKF Extended Kalman Filter ESB Electricity Supply Board, Ireland FLH Full Load Hours GA Genetic Algorithms HIRLAM HIgh Resolution Limited Area Model HWP HIRLAM/WAsP/PARK IMM Institute of Mathematical Modelling IPP Independent Power Producer KF Kalman Filter KNMI Koninlijk Nederlands Meteorologisch Institut (Royal Dutch Meteorological Institute) kWhel kWh of electrical output kWhth kWh of thermal energy LF Load Factor LOLE Loss Of Load Event LOLP Loss Of Load Probability MAE Mean Absolute Error MAPP Mid American Power Pool MOS Model Output Statistics MSE Mean Square Error NCAR National Centre for Atmospheric Research (US) NCEP National Centre for Environmental Protection (US) V NWP Numerical Weather Prediction NWS National Weather Service (US) OCGT Open Cycle Gas Turbine OSTI Office of Scientific and Technical Information (US DoE) RAL Rutherford Appleton Laboratory, UK RIX Ruggedness IndeX RMS Root Mean Square RMSE Root Mean Square Error SMHI Sveriges Meteorologiska och Hydrologiska Institut (Swedish Meteorological and Hydrological Institute) UCPTE Union pour la Coordination du Transport de l'Electricité VAR Variance WAsP Wind Atlas Application and Analysis Program WPPT Wind Power Prediction Tool VI 7DEOHRI6\PEROV DEFG, … Parameters in statistical models DN Parameter in the New Reference Model $*, %* Stability parameters in the geostrophic drag law $: Weibull magnitude parameter ' Decay parameter in exponential fit (II Wind farm efficiency I Coriolis parameter I X frequency of occurrence of X )L State-measurement relationship matrix in the KF * Geostrophic wind speed +L Autoupdate matrix in the KF N Time lag .L Kalman gain matrix N: Weibull shape parameter PL Measurement noise in the KF 1 Number of points for analysis QL System noise in the KF SN Measurements at time step N SHQL Installed wind capacity in the state model of the capacity credit TL State vector in the KF VL State in the state model of the capacity credit S Skill Score 6L State covariance matrix in the KF SR1 Fraction of Spinning Reserve depending on actual load SR2 Fraction of Spinning Reserve depending on wind power X Wind speed X Friction velocity XORFDO Predicted wind speed including local effects 9L Measurement noise covariance matrix in the KF :L System noise covariance in the KF \L Observation vector in the KF ] Aerodynamic roughness length Von Kármán constant Mean Value ρ Multiple correlation coefficient Standard Deviation VII ,QWURGXFWLRQ Wind energy has come of age [2]. As the industry is approaching maturity, the market is shifting from heavily subsidised technology demonstration plant to capital-driven shareholder value, for the industry themselves as well as for the customers. At the same time, the liberalisation of the electricity markets across the OECD has changed the market
Load more

Recommended publications
  • Distributed Wind Competitiveness Improvement Project
    Distributed Wind Competitiveness Improvement Project The Competitiveness Improvement Project (CIP) is a periodic solicitation through the U.S. Department of Energy (DOE) and its National Renewable Energy Laboratory (NREL). Manufacturers of small and medium wind turbines are awarded cost-shared subcontracts via a competitive process to optimize their designs, develop advanced manufacturing processes, and perform turbine testing. The goals of the CIP are to make wind energy cost competitive with other distributed generation technology and increase the number of wind turbine Photo from Northern Power Systems, NREL 36193 Increased Energy Reduced Hardware Production Costs Performance & Safety CIP component innovations and CIP manufacturing process CIP awardee Primus system optimization awardee innovation awardee Pika Energy Windpower of Lakewood, Colorado, achieved Bergey Windpower of Norman, of Westbrook, Maine, reduced Oklahoma, achieved a 110% blade costs by approximately for safety, function, performance, and durability— energy production increase for the 90% by developing an innovative to international standards on two of their turbine Excel 15 turbine over the Excel 10 tooling and cooling strategy to models. Northern Power Systems also recently turbine by increasing blade length produce blades using injection- and improving blade aerodynamics molded plastic. Two additional CIP awardees are currently and system controls. Through six funding cycles, DOE and NREL have awarded 28 subcontracts to 15 companies, totaling $6.3 million of investment
    [Show full text]
  • Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee University of Nebraska-Lincoln, [email protected]
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Faculty Publications from the Department of Electrical & Computer Engineering, Department of Electrical and Computer Engineering 2013 Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee University of Nebraska-Lincoln, [email protected] Sohrab Asgarpoor University of Nebraska-Lincoln, [email protected] Wei Qiao University of Nebraska-Lincoln, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/electricalengineeringfacpub Part of the Computer Engineering Commons, and the Electrical and Computer Engineering Commons Kahrobaee, Salman; Asgarpoor, Sohrab; and Qiao, Wei, "Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households" (2013). Faculty Publications from the Department of Electrical and Computer Engineering. 280. http://digitalcommons.unl.edu/electricalengineeringfacpub/280 This Article is brought to you for free and open access by the Electrical & Computer Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Faculty Publications from the Department of Electrical and Computer Engineering by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 4, DECEMBER 2013 1791 Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households Salman Kahrobaee, Student Member, IEEE, Sohrab Asgarpoor, Senior Member, IEEE,and Wei Qiao, Senior Member, IEEE Abstract—In the near future, a smart grid will accommodate DI Decision interval in h. customers who are prepared to invest in generation-battery sys- tems and employ energy management systems in order to cut down Time-step of the simulation in h. on their electricity bills.
    [Show full text]
  • Whither the Weather Analysis and Forecasting Process?
    520 WEATHER AND FORECASTING VOLUME 18 FORECASTER'S FORUM Whither the Weather Analysis and Forecasting Process? LANCE F. B OSART Department of Earth and Atmospheric Sciences, The University at Albany, State University of New York, Albany, New York 5 December 2002 and 19 December 2002 ABSTRACT An argument is made that if human forecasters are to continue to maintain a skill advantage over steadily improving model and guidance forecasts, then ways have to be found to prevent the deterioration of forecaster skills through disuse. The argument is extended to suggest that the absence of real-time, high quality mesoscale surface analyses is a signi®cant roadblock to forecaster ability to detect, track, diagnose, and predict important mesoscale circulation features associated with a rich variety of weather of interest to the general public. 1. Introduction h day-1 QPF scores made by selected NCEP models, By any objective or subjective measure, weather fore- including the Nested Grid Model (NGM; Hoke et al. casting skill has improved signi®cantly over the last 40 1989), the Eta Model (Black 1994), and the Aviation years. By way of illustration, Fig. 1 shows the annual Model [AVN, now called the Global Forecast System threat score for 24-h quantitative precipitation forecast (GFS); Kanamitsu et al. (1991); Kalnay et al. (1998)]. (QPF) amounts of 1.00 in. (2.5 cm) or more over the The key point to be made from Fig. 2 is that HPC contiguous United States for 1961±2001 as produced by forecasters have been able to sustain approximately a forecasters at the Hydrometeorological Prediction Cen- 0.05 threat score advantage over the NCEP numerical ter (HPC) of the National Centers for Environmental models during this 12-yr period (and longer, not shown) Prediction (NCEP).
    [Show full text]
  • The Operational CMC–MRB Global Environmental Multiscale (GEM)
    VOLUME 126 MONTHLY WEATHER REVIEW JUNE 1998 The Operational CMC±MRB Global Environmental Multiscale (GEM) Model. Part I: Design Considerations and Formulation JEAN COÃ TEÂ AND SYLVIE GRAVEL Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada ANDREÂ MEÂ THOT AND ALAIN PATOINE Canadian Meteorological Centre, Atmospheric Environment Service, Dorval, Quebec, Canada MICHEL ROCH AND ANDREW STANIFORTH Meteorological Research Branch, Atmospheric Environment Service, Dorval, Quebec, Canada (Manuscript received 31 March 1997, in ®nal form 3 October 1997) ABSTRACT An integrated forecasting and data assimilation system has been and is continuing to be developed by the Meteorological Research Branch (MRB) in partnership with the Canadian Meteorological Centre (CMC) of Environment Canada. Part I of this two-part paper motivates the development of the new system, summarizes various considerations taken into its design, and describes its main characteristics. 1. Introduction time and space scales that are commensurate with those An integrated atmospheric environmental forecasting associated with the phenomena of interest, and this im- and simulation system, described herein, has been and poses serious practical constraints and compromises on is continuing to be developed by the Meteorological their formulation. Research Branch (MRB) in partnership with the Ca- Emphasis is placed in this two-part paper on the con- nadian Meteorological Centre (CMC) of Environment cepts underlying the long-term developmental strategy,
    [Show full text]
  • Microgen & DG Report June 2020
    Small Distribution-Connected Generation Resources in Alberta Within Alberta, electric distribution systems are operated by utility companies and are the portion of the electric system that operates at 25 kilovolts or less. These distribution systems are used to deliver electricity from the Alberta interconnected electric system to an end-use customer. A Distributed Energy Resource is any resource that is connected to and can supply energy to an electric distribution system. This includes distribution-connected generation resources and energy storage. This report focuses on distribution-connected generation resources, which are typically connected to a distribution system at customer locations. Distribution-connected generation, if it qualifies, can be designated as micro-generation. For the purposes of this report, resources that are not designated as micro-generation will be referred to simply as distributed generation. This report has a section containing a brief description of each class of generation. In addition, there is data on the number of sites and total installed capacity broken out by energy source. Micro-Generation Distributed generation may qualify as micro-generation under the Micro-generation Regulation. To qualify as micro-generation, a generating unit must use renewable or alternative energy sources, be intended to offset consumer load, and have a nameplate capacity that does not exceed 5,000 kW. Electricity may be generated from solar, wind, hydro, fuel cells or biomass. Other sources may be permitted if they have current EcoLogo certification or their greenhouse gas intensity is lower then the regulated limit. Micro-generators producing electricity in excess of on-site load receive credits for what they feed to the electricity grid.
    [Show full text]
  • Distributed Generation: a Brighter Future?
    DISTRIBUTED GENERATION: A BRIGHTER FUTURE? Sponsored by DISTRIBUTED GENERATION A BRIGHTER FUTURE? CONTENTS 2 About this report 3 Executive summary 5 Part one: The shift to decentralised power production 9 Part two: Early movers 14 Part three: Looking ahead 17 Conclusion © The Economist Intelligence Unit Limited 2018 1 DISTRIBUTED GENERATION A BRIGHTER FUTURE? ABOUT THIS REPORT Distributed generation: a brighter future? is an Economist Intelligence Unit report, sponsored by E.ON. In this paper, The Economist Intelligence Unit examines the growth in distributed generation, as an increasing number of UK businesses are meeting a greater proportion of their energy needs through electricity generated themselves, on-site. This report seeks to examine the impact that distributed generation could have on UK businesses. To do so, we surveyed 450 senior executives with familiarity of their companies’ energy strategies in April-May 2018. The survey focused exclusively on executives from energy-intensive industries: manufacturing, transport and logistics, hospitality and retail. Half of the respondents are either members of their companies’ boards or hold C-level positions; the remainder are other senior managers and executives. All are from UK businesses with annual revenue exceeding £100m. The Economist Intelligence Unit supplemented the survey results with in-depth interviews with executives and industry experts. We would like to thank all survey respondents, as well as the following executives (listed alphabetically by company), for their time and insights: l Richard Carter, head of finance and sustainability, Adnams l Ilesh Patel, partner—energy and resources, Baringa l Simon Virley, partner and UK head of power and natural resources, KPMG l Caroline Hill, head of sustainability and public affairs, Landsec l Tom Byrne, sustainability manager, Landsec l Stuart Ravens, principal research analyst, Navigant Research l James Pitcher, director of sustainability, Whitbread This paper was written by Jessica Twentyman and edited by Jeremy Kingsley.
    [Show full text]
  • Grid Energy Storage
    Grid Energy Storage U.S. Department of Energy December 2013 Acknowledgements We would like to acknowledge the members of the core team dedicated to developing this report on grid energy storage: Imre Gyuk (OE), Mark Johnson (ARPA-E), John Vetrano (Office of Science), Kevin Lynn (EERE), William Parks (OE), Rachna Handa (OE), Landis Kannberg (PNNL), Sean Hearne & Karen Waldrip (SNL), Ralph Braccio (Booz Allen Hamilton). Table of Contents Acknowledgements ....................................................................................................................................... 1 Executive Summary ....................................................................................................................................... 4 1.0 Introduction .......................................................................................................................................... 7 2.0 State of Energy Storage in US and Abroad .......................................................................................... 11 3.0 Grid Scale Energy Storage Applications .............................................................................................. 20 4.0 Summary of Key Barriers ..................................................................................................................... 30 5.0Energy Storage Strategic Goals .......................................................................................................... 32 6.0 Implementation of its Goals ...............................................................................................................
    [Show full text]
  • USAID Energy Storage Decision Guide for Policymakers
    USAID ENERGY STORAGE FOR DECISION GUIDE POLICYMAKERS www.greeningthegrid.org | www.nrel.gov/usaid-partnership USAID ENERGY STORAGE FO R DECISION GUIDE P OLICYMAKERS Authors Ilya Chernyakhovskiy, Thomas Bowen, Carishma Gokhale-Welch, Owen Zinaman National Renewable Energy Laboratory July 2021 View the companion report: USAID Grid-Scale Energy Storage Technologies Primer www.greeningthegrid.org | www.nrel.gov/usaid-partnership Prepared by NOTICE This work was authored, in part, by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the United States Agency for International Development (USAID) under Contract No. IAG-17-2050. The views expressed in this report do not necessarily represent the views of the DOE or the U.S. Government, or any agency thereof, including USAID. This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov. Front cover: photo from iStock 506609532; Back cover: photo from iStock 506611252 NREL prints on paper that contains recycled content. Acknowledgments The authors are greatly indebted to several individuals for their support and guidance. We wish to thank Sarah Lawson, Andrew Fang, and Sarah Dimson at the U.S. Agency for International Development (USAID) for their thoughtful reviews. We also wish to thank Peerapat Vithayasrichareon, Jacques Warichet, Enrique Gutierrez Tavarez, and Luis Lopez at the International Energy Agency, and Dr.
    [Show full text]
  • Hydroelectric Power Generation and Distribution Planning Under Supply Uncertainty
    Hydroelectric Power Generation and Distribution Planning Under Supply Uncertainty by Govind R. Joshi A Thesis Submitted to The Department of Engineering Colorado State University-Pueblo In partial fulfillment of requirements for the degree of Master of Science Completed on December 16th, 2016 ABSTRACT Govind Raj Joshi for the degree of Master of Science in Industrial and Systems Engineering presented on December 16th, 2016. Hydroelectric Power Generation and Distribution Planning Under Supply Uncertainty Abstract approved: ------------------------------------------- Ebisa D. Wollega, Ph.D. Hydroelectric power system is a renewable energy type that generates electrical energy from water flow. An integrated hydroelectric power system may consist of water storage dams and run-of-river (ROR) hydroelectric power projects. Storage dams store water and regulate water flow so that power from the storage projects dispatch can follow a pre-planned schedule. Power supply from ROR projects is uncertain because water flow in the river, and hence power production capacity, is largely determined by uncertain weather factors. Hydroelectric generator dispatch problem has been widely studied in the literature; however, very little work is available to address the dispatch and distribution planning of an integrated ROR and storage hydroelectric projects. This thesis combines both ROR projects and storage dam projects and formulate the problem as a stochastic program to minimize the cost of energy generation and distribution under ROR projects supply uncertainty. Input data from the Integrated Nepal Power System are used to solve the problem and run experiments. Numerical comparisons of stochastic solution (SS), expected value (EEV), and wait and see (W&S) solutions are made. These solution approaches give economic dispatch of generators and optimal distribution plan that the power system operators (PSO) can use to coordinate, control, and monitor the power generation and distribution system.
    [Show full text]
  • MP - Mechanistic Habitat Modeling with Multi-Model Climate Ensembles - Final - HMJ5.Docx
    MP - Mechanistic Habitat Modeling with Multi-Model Climate Ensembles - Final - HMJ5.docx Mechanistic Habitat Modeling with Multi-Model Climate Ensembles A practical guide to preparing and using Global Climate Model output for mechanistically modeling habitat, as exemplified by the modeling of the fundamental niche of a pagophilic Pinniped species through 2100. Masters project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University, May 2013. Author: Hunter Jones Adviser: Dr. David Johnston Abstract Projections of future Sea Ice Concentration (SIC) were prepared using a 13- member ensemble of climate model output from the Coupled Model Inter- comparison Project (CMIP5). Three climate change scenarios (RCP 2.6, RCP 6.0, RCP 8.5), corresponding to low, moderate, and high climate change possibilities, were used to generate these projections for known Harp Seal whelping locations. The projections were splined and statistically downscaled via the CCAFS Delta method using satellite-derived observations from the National Sea Ice Data Center (NSIDC) to prepare a spatial representation of sea ice decline through the year 2100. Multi-Model Ensemble projections of the mean sea ice concentration anomaly for Harp Seal whelping locations under the moderate and high climate change scenarios (RCP 6.0 and RCP 8.5) show a decline of 10% to 40% by 2100 from a modern baseline climatology (average of SIC, 1988 - 2005) while sea ice concentrations under the low climate change scenario remain fairly stable. Projected year-over-year sea ice concentration variability decreases with time through 2100, but uncertainty in the prediction (model spread) increases.
    [Show full text]
  • Downscaling Geopotential Height Using Lapse Rate
    KMITL Sci. Tech. J. Vol. 12 No. 2 Jul.-Dec. 2012 Downscaling Geopotential Height Using Lapse Rate Chiranya Surawut1 and Dusadee Sukawat2* 1Earth System Science, King Mongkut ’s University of Technology Thonburi, Bangkok, Thailand 2*Department of Mathematics, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand Abstract In order to utilize the output from a global climate model, relevant information for the area of interest must be extracted. This is called "downscaling". In this paper, the derivation of a local geopotential height in terms of lapse rate is presented. The main assumptions are hydrostatic balance, perfect gas, constant gravity, and constant lapse rate. Two sets of data are required for this method, simulation outputs from the Education Global Climate Model (EdGCM) and the observed data at the points of interest (Chiangmai, Bangkok, Ubon Ratchathani, Phuket and Songkla). The results show that downscaling of the geopotential heights by using lapse rate dynamic equation are closer to the observed data than the geopotential heights from EdGCM. Keywords: Downscaling, Geopotential height, Lapse rate 1. Introduction Climate change data are simulation outputs from global climate models (GCMs). Outputs of GCMs are coarse resolution, so it must be downscaled for use in regional applications by downscaling [1]. The starting point for downscaling is a larger scale atmospheric or coupled oceanic atmospheric model run from a GCM [2]. There are two major kinds of downscaling, statistical and dynamical methods. Statistical downscaling methods use historical data and archived forecasts to produce downscaled information from large scale forecasts. Dynamical downscaling methods involve dynamical models of the atmosphere nested within the grids of the large scale forecast models [1].
    [Show full text]
  • Electric Vehicles As Distributed Energy Resources
    ELECTRIC VEHICLES AS DISTRIBUTED ENERGY RESOURCES BY GARRETT FITZGERALD, CHRIS NELDER, AND JAMES NEWCOMB O Y M UN ARBON K T C C A I O N R I W N E A M STIT U T R R O O AUTHORS & ACKNOWLEDGMENTS AUTHORS ACKNOWLEDGMENTS Garrett Fitzgerald, Chris Nelder, and James Newcomb The authors thank the following individuals and e-Lab * Authors listed alphabetically. All authors are from member organizations for offering their insights and Rocky Mountain Institute unless otherwise noted. perspectives on this work, which does not necessarily reflect their views. ADDITIONAL CONTRIBUTORS Jim Lazar, Regulatory Assistance Project Rich Sedano, Regulatory Assistance Project Riley Allen, Regulatory Assistance Project Sarah Keay-Bright, Regulatory Assistance Project Jim Avery, San Diego Gas & Electric CONTACTS Greg Haddow, San Diego Gas & Electric Chris Nelder ([email protected]) San Diego Gas & Electric Load Analysis Group James Newcomb ([email protected]) Noel Crisostomo, California Public Utilities Commission Jonathan Walker, Rocky Mountain Institute SUGGESTED CITATION Chris Nelder, James Newcomb, and Garrett Fitzgerald, The authors also thank the following additional Electric Vehicles as Distributed Energy Resources individuals and organizations for offering their insights (Rocky Mountain Institute, 2016), and perspectives on this work: http://www.rmi.org/pdf_evs_as_DERs. Joel R. Pointon, JRP Charge Joyce McLaren, National Renewable Energy Laboratory DISCLAIMER e-Lab is a joint collaboration, convened by RMI, with participationfrom stakeholders across the electricity Editorial Director: Cindie Baker industry. e-Lab is not a consensus organization, and Editor: David Labrador the views expressed in this document are not intended Art Director: Romy Purshouse to represent those of any individual e-Lab member or Images courtesy of iStock unless otherwise noted.
    [Show full text]