Thesis (Ph. D.)--George Washington University, 2000. / Includes bibliographical references.

Ionospheric scintillation is a phenomenon caused by varying concentrations of charged particles in the upper atmosphere that induces deep fades and rapid phase rotations in satellite signals, including GPS. During periods of scintillation, carrier tracking loops often lose lock on the signal because the rapid phase rotations generate cycle slips in the PLL. One solution to mitigating this problem is by employing decision-directed carrier recovery algorithms that achieve data wipe-off using differential bit detection techniques. Other techniques involve PLLs with variable bandwidth and variable integration times. Since nearly 60% of the GPS signal repeats between frames, this thesis explores PLLs utilizing variable integration times and decision-directed algorithms that exploit the repeating data as a training sequence to aid in phase error estimation. Experiments conducted using a GPS signal generator, software radio, and MATLAB scintillation testbed compare the bit error rate of each of the receiver models. Training-based methods utilizing variable integration times show significant reductions in the likelihood of total loss of lock.

Thesis deals with design and integration of enterprise information system in a manufacturing company. Main part of this work is to design a detailed plan of implementation including identification of critical factors and ensure the prerequisites for system operation. The outcome of this work is to create the asset for both sides of contractual relationship.

Die Erreichung der ehrgeizigen Dekarbonisierungsziele Deutschlands erfordert eine massive Ausweitung der Onshore-Windenergie. In den letzten Jahren sahen sich Onshore-Wind Projektentwickler zunehmend mit sozialen und Umweltbedenken aufgrund von Landnutzungskonflikten konfrontiert. Aus regulatorischer Sicht stellen die weitere Integration von Onshore-Windkapazitäten in das deutsche Energiesystem besondere Herausforderungen in Bezug auf geografische und zeitliche Aspekte der Stromerzeugung dar. Die hohen Windgeschwindigkeiten und die vergleichsweise geringe Bevölkerungsdichte haben dazu geführt, dass Investoren in der Vergangenheit überproportional in den nördlichen Bundesländern Windparks entwickelten. Eine starke gleichzeitige Einspeisung von Strom an nahegelegenen Windstandorten führt jedoch zu einem Druck auf die Großhandelsstrompreise, was die Markterträge der Entwickler reduziert. Diese Arbeit zielt daher darauf ab, einen Beitrag zum zukünftigen Design des deutschen Energiesystems zu leisten und insbesondere den weiteren Ausbau der Onshore-Windenergie in Deutschland unter Berücksichtigung sozialer, Umwelt- und wirtschaftlicher Einschränkungen zu untersuchen. Dabei werden GIS-Software und ein neues inverses Zeitreihenmodellierungsverfahren genutzt, um das Windpotenzial und Landnutzungskonflikte zu analysieren. Zukünftige Marktszenarien werden mit Hilfe eines dekomposierten Kraftwerkseinsatz und -investitionsmodells hinsichtlich ihrer Wirkungen auf die ökonomische Effizienz der Marktintegration von Onshore-Windenergie bewertet, wobei Preisentwicklungen für CO2-Emissionszertifikate eine entscheidende Rolle spielen. Die Ergebnisse deuten auf eine abnehmende Rentabilität der Onshore-Windenergie in Deutschland hin, während der Süden Deutschlands aus ganzheitlicher Perspektive einen größeren Beitrag zur Windenergie leisten könnte.:I Analysis framework 1 1 Introduction 3 1.1 Research motivation . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Research objective, aims and questions . . . . . . . . . . . . . 5 1.3 Scientific contribution . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.1 Research focus specification . . . . . . . . . . . . . . . 7 1.3.2 Contribution regarding renewable energy potentials and levelised generation cost . . . . . . . . . . . . . . . 10 1.3.3 Contribution regarding generic wind time series modelling 12 1.3.4 Contribution regarding electricity market modelling and model decomposition . . . . . . . . . . . . . . . . . 14 1.3.5 Contribution regarding evaluating the market integration of wind energy . . . . . . . . . . . . . . . . . . 17 1.4 Organisation of thesis and software tools applied . . . . . . . 20 2 Basics of electricity economics 23 2.1 Pricing and investments in electricity markets . . . . . . . . . 23 2.1.1 Long-term market equilibrium . . . . . . . . . . . . . . 23 2.1.2 Short-term market equilibrium . . . . . . . . . . . . . . 25 2.2 Interplay of price formation and renewable support . . . . . . 27 2.2.1 Definitions and concepts . . . . . . . . . . . . . . . . . 27 2.2.2 Quantity and price effect of environmental policies and implications for geographic deployment pathways 29 II Regionalisation of data inputs 33 3 GIS-based windenergy potential analysis 35 3.1 Framing the approach . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.1 Taxonomy of renewable potentials . . . . . . . . . . . 35 3.1.2 GIS-based analysis procedure . . . . . . . . . . . . . . 36 3.1.3 Three-stage sensitivity analysis . . . . . . . . . . . . . 37 3.2 Land assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Land characteristics . . . . . . . . . . . . . . . . . . . . 37 3.2.2 Results on the land availability . . . . . . . . . . . . . . 41 3.3 Technical potential . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.1 Technical wind turbine configuration . . . . . . . . . . 44 3.3.2 Electrical energy conversion . . . . . . . . . . . . . . . 45 3.3.3 Wind-farm design . . . . . . . . . . . . . . . . . . . . . 46 3.3.4 Results on the technical potential . . . . . . . . . . . . 47 3.4 Economic potential . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.4.1 Cost-potential curves at a country level . . . . . . . . 49 3.4.2 Cost-potential curves at a regional level . . . . . . . . 52 4 Generic wind energy feed-in time series 55 4.1 Generic wind speed data in energy systems analysis . . . . . . 55 4.1.1 Motivation of generic time series . . . . . . . . . . . . 55 4.1.2 Incorporation of time series generation into modelling setup . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.2 Dynamic adjustment of model size via clustering . . . . . . . 56 4.2.1 Introduction to hierarchical and partitional cluster methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.2.2 Euclidean distance as proximity measure . . . . . . . . 57 4.2.3 Linkage of observations and cluster verification . . . 58 4.2.4 Specification of input data and data organisation . . . 59 4.2.5 Results on cluster algorithm selection and representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3 Vector autoregressive stochastic process with Normal-to- Weibull transformation . . . . . . . . . . . . . . . . . . . . . . . 61 4.3.1 Wind characteristics . . . . . . . . . . . . . . . . . . . . 61 4.3.2 Data description and handling . . . . . . . . . . . . . . 62 4.3.3 Additive modelling procedure . . . . . . . . . . . . . . 63 4.3.4 Standard Normal-to-Weibull transformation . . . . . . 64 4.3.5 Time series decomposition . . . . . . . . . . . . . . . . 67 4.3.6 (V)AR-Parameter estimation . . . . . . . . . . . . . . . 70 4.3.7 Statistical dependence between different locations . . 73 4.3.8 Time series simulation . . . . . . . . . . . . . . . . . . . 75 4.3.9 Results on time series simulation . . . . . . . . . . . . 77 III Market model-based investigation 81 5 Modelling investment decisions in power markets 83 5.1 Motivation for illustration of model decomposition . . . . . . 83 5.2 Simplified market model formulation . . . . . . . . . . . . . . . 83 5.2.1 Power plant dispatch problem . . . . . . . . . . . . . . 83 5.2.2 Capacity expansion extension . . . . . . . . . . . . . . 84 5.2.3 Constraint matrix structure . . . . . . . . . . . . . . . . 85 5.3 Complexity reduction via Benders decomposition . . . . . . . 87 5.3.1 Benders strategies . . . . . . . . . . . . . . . . . . . . . 87 5.3.2 Single-cut procedure . . . . . . . . . . . . . . . . . . . . 88 5.3.3 Multi-cut procedure . . . . . . . . . . . . . . . . . . . . 93 5.4 Acceleration strategies for decomposed market models . . . . 98 5.4.1 Scenario solver . . . . . . . . . . . . . . . . . . . . . . . 98 5.4.2 Distributed computing . . . . . . . . . . . . . . . . . . . 98 5.4.3 Regularisation . . . . . . . . . . . . . . . . . . . . . . . 98 5.5 Numerical testing of model formulation and solving strategy 99 5.5.1 Preliminary remarks . . . . . . . . . . . . . . . . . . . . 99 5.5.2 Effects of multiple cuts . . . . . . . . . . . . . . . . . . 100 5.5.3 Effects of scenario solver and parallelisation . . . . . . 101 5.5.4 Effects of regularisation . . . . . . . . . . . . . . . . . . 103 5.6 Implications for a large-scale application . . . . . . . . . . . . 105 6 ELTRAMOD-dec: A market model tailored for investigating the European electricity markets 107 6.1 Understanding the model design . . . . . . . . . . . . . . . . . 107 6.1.1 Market modelling fundamentals . . . . . . . . . . . . . 107 6.1.2 ELTRAMOD-dec’s model structure and solving conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.1.3 Central capacity planning assumptions . . . . . . . . . 109 6.1.4 Central market clearing assumptions . . . . . . . . . . 110 6.2 Mathematical formulation of ELTRAMOD-dec . . . . . . . . . 111 6.2.1 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . 111 6.2.2 Master problem equations . . . . . . . . . . . . . . . . 114 6.2.3 Subproblem equations . . . . . . . . . . . . . . . . . . . 117 6.2.4 Program termination . . . . . . . . . . . . . . . . . . . 123 6.2.5 Research-specific extensions . . . . . . . . . . . . . . . 124 6.3 Data description and model calibration . . . . . . . . . . . . . 126 6.3.1 Base year modelling data . . . . . . . . . . . . . . . . . 126 6.3.2 Model performance validation . . . . . . . . . . . . . . 131 6.3.3 Target year modelling data . . . . . . . . . . . . . . . . 133 6.4 Determination of ELTRAMOD-dec’s solving conventions and tuning parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.4.1 Framing some modelling experiments . . . . . . . . . 137 6.4.2 Effects of regularisation on convergence behaviour . 138 6.4.3 Effects of time slicing on solution accuracy . . . . . . 142 6.4.4 Effects of decomposition on solving speed . . . . . . . 145 7 Model-based investigation of onshore wind deployment pathways in Germany 149 7.1 Scenario framework and key assumptions . . . . . . . . . . . . 149 7.1.1 Scenario creation . . . . . . . . . . . . . . . . . . . . . . 149 7.1.2 Definition of market configuration . . . . . . . . . . . 152 7.1.3 Summary on scenario key assumptions . . . . . . . . . 154 7.2 Results on market integration at a market zone level . . . . . 155 7.2.1 Introducing market integration indicators . . . . . . . 155 7.2.2 Market integration indicators for baseline calculation 156 7.2.3 Market integration indicators for increased renewable uptake calculation . . . . . . . . . . . . . . . . . . 157 7.2.4 Market integration indicators for ultimate renewable uptake calculation . . . . . . . . . . . . . . . . . . . . . 159 7.3 Results on market integration at a detailed regional level . . . 160 7.3.1 Introducing regional market integration indicators . . 160 7.3.2 Regional market integration indicators for baseline calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 161 7.3.3 Regional market integration indicators for increased renewable uptake calculation . . . . . . . . . . . . . . . 163 7.3.4 Regional market integration indicators for ultimate renewable uptake calculation . . . . . . . . . . . . . . . 164 8 Summary and conclusions 169 8.1 Findings regarding the market integration . . . . . . . . . . . . 169 8.1.1 Onshore wind resources constitute a limiting factor for achieving Germany’s energy transition . . . . . . 169 8.1.2 Distribution of wind farm fleet has a strong impact on market premia in the centre and south of Germany 170 8.2 Findings regarding the technical underpinning . . . . . . . . . 173 8.2.1 Generic wind speed velocities can be a powerful tool for power system modellers . . . . . . . . . . . . . . . 173 8.2.2 Decomposition enables efficient solving of large-scale power system investment and dispatch models . . . . 174 8.3 Implications for policymakers . . . . . . . . . . . . . . . . . . . 176 IV Appendix 179 A Additional tables and figures 181 B Code listings 187 Bibliography 199 / Achieving Germany's ambitious decarbonisation goals requires a massive expansion of onshore wind energy. In recent years, onshore wind project developers have increasingly faced social and environmental concerns due to land use conflicts. From a regulatory perspective, further integrating onshore wind capacity into the German energy system poses particular challenges regarding geographical and temporal aspects of electricity generation. High wind speeds and comparatively low population density have led investors to disproportionately develop wind farms in the northern states in the past. However, a strong simultaneous electricity feed-in at nearby wind sites suppresses wholesale electricity prices, reducing developers' market returns. This study aims to contribute to the future design of the German energy system and, in particular, to examine the further expansion of onshore wind energy in Germany, considering social, environmental, and economic constraints. GIS software and a new inverse time series modelling approach are utilised to investigate wind potential and land use conflicts. Future market scenarios are evaluated using a decomposed power plant dispatch and investment model regarding their effects on the economic efficiency of onshore wind energy market integration, with price developments for carbon emission certificates playing a crucial role. The results indicate a decreasing profitability of onshore wind energy in Germany, while from a holistic perspective, southern Germany could make a more significant contribution to wind energy at reasonable increases in support requirements.:I Analysis framework 1 1 Introduction 3 1.1 Research motivation . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Research objective, aims and questions . . . . . . . . . . . . . 5 1.3 Scientific contribution . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.1 Research focus specification . . . . . . . . . . . . . . . 7 1.3.2 Contribution regarding renewable energy potentials and levelised generation cost . . . . . . . . . . . . . . . 10 1.3.3 Contribution regarding generic wind time series modelling 12 1.3.4 Contribution regarding electricity market modelling and model decomposition . . . . . . . . . . . . . . . . . 14 1.3.5 Contribution regarding evaluating the market integration of wind energy . . . . . . . . . . . . . . . . . . 17 1.4 Organisation of thesis and software tools applied . . . . . . . 20 2 Basics of electricity economics 23 2.1 Pricing and investments in electricity markets . . . . . . . . . 23 2.1.1 Long-term market equilibrium . . . . . . . . . . . . . . 23 2.1.2 Short-term market equilibrium . . . . . . . . . . . . . . 25 2.2 Interplay of price formation and renewable support . . . . . . 27 2.2.1 Definitions and concepts . . . . . . . . . . . . . . . . . 27 2.2.2 Quantity and price effect of environmental policies and implications for geographic deployment pathways 29 II Regionalisation of data inputs 33 3 GIS-based windenergy potential analysis 35 3.1 Framing the approach . . . . . . . . . . . . . . . . . . . . . . . . 35 3.1.1 Taxonomy of renewable potentials . . . . . . . . . . . 35 3.1.2 GIS-based analysis procedure . . . . . . . . . . . . . . 36 3.1.3 Three-stage sensitivity analysis . . . . . . . . . . . . . 37 3.2 Land assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Land characteristics . . . . . . . . . . . . . . . . . . . . 37 3.2.2 Results on the land availability . . . . . . . . . . . . . . 41 3.3 Technical potential . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.1 Technical wind turbine configuration . . . . . . . . . . 44 3.3.2 Electrical energy conversion . . . . . . . . . . . . . . . 45 3.3.3 Wind-farm design . . . . . . . . . . . . . . . . . . . . . 46 3.3.4 Results on the technical potential . . . . . . . . . . . . 47 3.4 Economic potential . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.4.1 Cost-potential curves at a country level . . . . . . . . 49 3.4.2 Cost-potential curves at a regional level . . . . . . . . 52 4 Generic wind energy feed-in time series 55 4.1 Generic wind speed data in energy systems analysis . . . . . . 55 4.1.1 Motivation of generic time series . . . . . . . . . . . . 55 4.1.2 Incorporation of time series generation into modelling setup . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.2 Dynamic adjustment of model size via clustering . . . . . . . 56 4.2.1 Introduction to hierarchical and partitional cluster methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.2.2 Euclidean distance as proximity measure . . . . . . . . 57 4.2.3 Linkage of observations and cluster verification . . . 58 4.2.4 Specification of input data and data organisation . . . 59 4.2.5 Results on cluster algorithm selection and representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3 Vector autoregressive stochastic process with Normal-to- Weibull transformation . . . . . . . . . . . . . . . . . . . . . . . 61 4.3.1 Wind characteristics . . . . . . . . . . . . . . . . . . . . 61 4.3.2 Data description and handling . . . . . . . . . . . . . . 62 4.3.3 Additive modelling procedure . . . . . . . . . . . . . . 63 4.3.4 Standard Normal-to-Weibull transformation . . . . . . 64 4.3.5 Time series decomposition . . . . . . . . . . . . . . . . 67 4.3.6 (V)AR-Parameter estimation . . . . . . . . . . . . . . . 70 4.3.7 Statistical dependence between different locations . . 73 4.3.8 Time series simulation . . . . . . . . . . . . . . . . . . . 75 4.3.9 Results on time series simulation . . . . . . . . . . . . 77 III Market model-based investigation 81 5 Modelling investment decisions in power markets 83 5.1 Motivation for illustration of model decomposition . . . . . . 83 5.2 Simplified market model formulation . . . . . . . . . . . . . . . 83 5.2.1 Power plant dispatch problem . . . . . . . . . . . . . . 83 5.2.2 Capacity expansion extension . . . . . . . . . . . . . . 84 5.2.3 Constraint matrix structure . . . . . . . . . . . . . . . . 85 5.3 Complexity reduction via Benders decomposition . . . . . . . 87 5.3.1 Benders strategies . . . . . . . . . . . . . . . . . . . . . 87 5.3.2 Single-cut procedure . . . . . . . . . . . . . . . . . . . . 88 5.3.3 Multi-cut procedure . . . . . . . . . . . . . . . . . . . . 93 5.4 Acceleration strategies for decomposed market models . . . . 98 5.4.1 Scenario solver . . . . . . . . . . . . . . . . . . . . . . . 98 5.4.2 Distributed computing . . . . . . . . . . . . . . . . . . . 98 5.4.3 Regularisation . . . . . . . . . . . . . . . . . . . . . . . 98 5.5 Numerical testing of model formulation and solving strategy 99 5.5.1 Preliminary remarks . . . . . . . . . . . . . . . . . . . . 99 5.5.2 Effects of multiple cuts . . . . . . . . . . . . . . . . . . 100 5.5.3 Effects of scenario solver and parallelisation . . . . . . 101 5.5.4 Effects of regularisation . . . . . . . . . . . . . . . . . . 103 5.6 Implications for a large-scale application . . . . . . . . . . . . 105 6 ELTRAMOD-dec: A market model tailored for investigating the European electricity markets 107 6.1 Understanding the model design . . . . . . . . . . . . . . . . . 107 6.1.1 Market modelling fundamentals . . . . . . . . . . . . . 107 6.1.2 ELTRAMOD-dec’s model structure and solving conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.1.3 Central capacity planning assumptions . . . . . . . . . 109 6.1.4 Central market clearing assumptions . . . . . . . . . . 110 6.2 Mathematical formulation of ELTRAMOD-dec . . . . . . . . . 111 6.2.1 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . 111 6.2.2 Master problem equations . . . . . . . . . . . . . . . . 114 6.2.3 Subproblem equations . . . . . . . . . . . . . . . . . . . 117 6.2.4 Program termination . . . . . . . . . . . . . . . . . . . 123 6.2.5 Research-specific extensions . . . . . . . . . . . . . . . 124 6.3 Data description and model calibration . . . . . . . . . . . . . 126 6.3.1 Base year modelling data . . . . . . . . . . . . . . . . . 126 6.3.2 Model performance validation . . . . . . . . . . . . . . 131 6.3.3 Target year modelling data . . . . . . . . . . . . . . . . 133 6.4 Determination of ELTRAMOD-dec’s solving conventions and tuning parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.4.1 Framing some modelling experiments . . . . . . . . . 137 6.4.2 Effects of regularisation on convergence behaviour . 138 6.4.3 Effects of time slicing on solution accuracy . . . . . . 142 6.4.4 Effects of decomposition on solving speed . . . . . . . 145 7 Model-based investigation of onshore wind deployment pathways in Germany 149 7.1 Scenario framework and key assumptions . . . . . . . . . . . . 149 7.1.1 Scenario creation . . . . . . . . . . . . . . . . . . . . . . 149 7.1.2 Definition of market configuration . . . . . . . . . . . 152 7.1.3 Summary on scenario key assumptions . . . . . . . . . 154 7.2 Results on market integration at a market zone level . . . . . 155 7.2.1 Introducing market integration indicators . . . . . . . 155 7.2.2 Market integration indicators for baseline calculation 156 7.2.3 Market integration indicators for increased renewable uptake calculation . . . . . . . . . . . . . . . . . . 157 7.2.4 Market integration indicators for ultimate renewable uptake calculation . . . . . . . . . . . . . . . . . . . . . 159 7.3 Results on market integration at a detailed regional level . . . 160 7.3.1 Introducing regional market integration indicators . . 160 7.3.2 Regional market integration indicators for baseline calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 161 7.3.3 Regional market integration indicators for increased renewable uptake calculation . . . . . . . . . . . . . . . 163 7.3.4 Regional market integration indicators for ultimate renewable uptake calculation . . . . . . . . . . . . . . . 164 8 Summary and conclusions 169 8.1 Findings regarding the market integration . . . . . . . . . . . . 169 8.1.1 Onshore wind resources constitute a limiting factor for achieving Germany’s energy transition . . . . . . 169 8.1.2 Distribution of wind farm fleet has a strong impact on market premia in the centre and south of Germany 170 8.2 Findings regarding the technical underpinning . . . . . . . . . 173 8.2.1 Generic wind speed velocities can be a powerful tool for power system modellers . . . . . . . . . . . . . . . 173 8.2.2 Decomposition enables efficient solving of large-scale power system investment and dispatch models . . . . 174 8.3 Implications for policymakers . . . . . . . . . . . . . . . . . . . 176 IV Appendix 179 A Additional tables and figures 181 B Code listings 187 Bibliography 199