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..................................................................................................................................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