Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-07-01T11:43:52Z No. of bitstreams: 1 germandavidyagimoromisato.pdf: 4216499 bytes, checksum: a1b6dec404f94fd91a0a919755636775 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-13T16:00:06Z (GMT) No. of bitstreams: 1 germandavidyagimoromisato.pdf: 4216499 bytes, checksum: a1b6dec404f94fd91a0a919755636775 (MD5) / Made available in DSpace on 2016-07-13T16:00:06Z (GMT). No. of bitstreams: 1 germandavidyagimoromisato.pdf: 4216499 bytes, checksum: a1b6dec404f94fd91a0a919755636775 (MD5) Previous issue date: 2012-08-02 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho tem como objetivo apresentar uma nova metodologia baseada em Programação Dinâmica Dual Determinística (PDDD) para o cálculo da Energia Firme de sistemas energéticos. A Energia Firme tem uma relação direta com os certificados de energia garantida atribuídos às usinas hidráulicas, os quais representam o limite superior para os contratos de energia estabelecidos com os consumidores (distribuidores e consumidores livres). Neste contexto, este trabalho possui uma importância relevante para o cenário atual do Setor Elétrico Brasileiro (SEB). Os resultados são comparados com aqueles obtidos pela metodologia em vigor no SEB, o qual é baseado em métodos heurísticos. / The objective of this work is to introduce a new methodology based in The Deterministic Dual Dynamic Programming (DDDP) to calculate the firm energy of energetic systems. The firm energy is directly related to the guaranteed energy certificates assigned to hydraulic power plants. These energy certificates represent the limits of energy contracts that can be established with consumers (energy distributors and free consumers). In this context, this work has a relevant importance to the current scenario of the Brazilian Electric Sector (BES). The results are compared to those obtained by the BES approved computational model based in heuristic methods.

Les réseaux optiques sont aujourd’hui l’élément de base des systèmes de communica- tions modernes, en particulier l’Internet. Grâce au multiplexage en longueurs d’onde et au groupage du tra?c, la bande passante disponible sur une ?bre optique est supérieure à plusieurs térabits par seconde. Cependant les équipements opto-électroniques qui permettent d’opérer ces réseaux sont très coûteux car il doivent fonctionner à un débit très important. Le problème de groupage et du routage d’un ensemble de requêtes couplé avec l’affectation des longueurs d’onde (GRWA) est donc un problème stratégique de première importance. L’objectif est de minimiser le coût du réseau, évalué comme le nombre de ports optiques installés aux nœuds. Il peut être modélisé sous la forme d’un problème d’allocation de ressources dans un réseau à capacité multi-niveaux avec multi-?ots non bifurqués. Cette catégorie de problème est connue pour être très dif?cile compte tenu de la faiblesse de la relaxation linéaire des formulations associées. Les travaux réalisés durant cette thèse ont consisté en le développement de méthodes de résolution pour ce problème à partir de multiples techniques de recherche opéra- tionnelle : méta-heuristique de type recherche avec tabous, décomposition de Dantzig- Wolfe, décomposition de Benders, reformulation en variables binaires, méthode de plans coupants, heuristique d’arrondi. Les méthodes résultantes, dont certaines sont hybrides, permettent d’avoir un aperçu des méthodes ef?caces pour ce type de problème. En partic- ulier, les méthodes basées sur la décomposition de Benders, qui donnent lieu à des procédures d’optimisation hiérarchique dans lesquelles l’affectation de longueurs d’onde est placée au dernier niveau, sont les méthodes les plus ef?caces car elles permettent de séparer le routage optique du routage physique. En?n, nous utilisons la meilleure méthode de résolution pour observer l’impact des contraintes de délais sur la qualité des solutions. / Optical networks are the core element of modern communication systems and in particu- lar Internet. With wavelength multiplexing and grooming capability, terabits per second bandwidth can be reached. However, opto-electronic equipment used to operate these networks are very expensive as their bit rate must be very large. The grooming, routing and wavelength assignment (GRWA) problem, which consists in minimizing the net- work cost, evaluated by the number of required optical ports, while guaranteeing that each request is granted, is of great interest. The GRWA problem can be modeled as a multi-layer capacitated network design problem with non-bifurcated multi-?ows. This type of problem is known to be hard to solve as their linear relaxation is weak. The objective of this work was to develop solution methods based on multiple oper- ations research techniques : Tabu search based meta-heuristic, Dantzig-Wolfe decompo- sition, Benders decomposition, 0 1 reformulation, cutting-planes, rounding heuristic. The resulting solution tools, some of them hybrid, give a perspective on the effective solution approaches for this type of problem. From the experiments, it turns out that the methods based on Benders’ decomposition, which lead to hierarchical optimization procedures, are the most ef?cient as they allow to separate the optical routing from the physical routing with the wavelength assignment decisions taken in the lower stage sub- problem. In addition to the approach comparison, we use the most effective method to evaluate the impact of the delay constraints on the solution quality.

Cotutelle avec l'Université de Toulouse 3 - Paul Sabatier, France. Laboratoire d'accueil: Laboratoire de recherche de l'École Nationale de l'Aviation Civile (ENAC), équipe OPTIM, Toulouse, France. / Dans le contexte d'une augmentation soutenue du trafic aérien et d'une faible marge d'expansion des capacités aéroportuaires, la pression s'accroît sur les aéroports les plus fréquentés pour une utilisation optimale de leur infrastructure, telle que les pistes, reconnues comme le goulot d'étranglement des opérations aériennes. De ce besoin opérationnel est né le problème d'ordonnancement des atterrissages d'avions, consistant à trouver pour les avions se présentant à un aéroport la séquence et les heures d'atterrissage optimales par rapport à certains critères (utilisation des pistes, coût total des retards, etc) tout en respectant des contraintes opérationnelles et de sécurité. En réponse à ce besoin également, depuis les années 1990 aux États-Unis et en Europe, des outils d'aide à la décision ont été mis à la disposition des contrôleurs aériens, afin de les assister dans leur tâche d'assurer la sécurité et surtout la performance des flux d'arrivée. Un certain nombre de travaux de recherche se sont focalisés sur le cas déterministe et statique du problème d'atterrissage d'avions. Cependant, le problème plus réaliste, de nature stochastique et dynamique, a reçu une attention moindre dans la littérature. De plus, dans le cadre du projet européen de modernisation des systèmes de gestion de trafic aérien, il a été proposé d’étendre l’horizon opérationnel des outils d’aide à la décision de manière à prendre en compte les avions plus loin de l'aéroport de destination. Cette extension de l'horizon opérationnel promet une meilleure gestion des flux d'arrivées via un ordonnancement précoce plus efficient. Néanmoins, elle est inévitablement accompagnée d'une détérioration de la qualité des données d'entrée, rendant indispensable la prise en compte de leur stochasticité. L’objectif de cette thèse est l’ordonnancement des arrivées d’avions, dans le cadre d'un horizon opérationnel étendu, où les heures effectives d'arrivée des avions sont incertaines. Plus précisément, nous proposons une approche basée sur la programmation stochastique à deux étapes. En première étape, les avions sont pris en considération à 2-3 heures de leur atterrissage prévu à l'aéroport de destination. Il s'agit de les ordonnancer à un point de l'espace aérien aéroportuaire, appelé IAF (Initial Approach Fix). Les heures effectives de passage à ce point sont supposées suivre des distributions de probabilité connues. En pratique, cette incertitude peut engendrer un risque à la bonne séparation des avions nécessitant l'intervention des contrôleurs. Afin de limiter la charge de contrôle conséquente, nous introduisons des contraintes en probabilité traduisant le niveau de tolérance aux risques de sécurité à l'IAF après révélation de l'incertitude. La deuxième étape correspond au passage effectif des avions considérés à l'IAF. Comme l'incertitude est révélée, une décision de recours est prise afin d'ordonnancer les avions au seuil de piste en minimisant un critère de deuxième étape (charge de travail des contrôleurs, coût du retard, etc). La démonstration de faisabilité et une étude numérique de ce problème d'ordonnancement des arrivées d'avions en présence d'incertitude constituent la première contribution de la thèse. La modélisation de ce problème sous la forme d’un problème de programmation stochastique à deux étapes et sa résolution par décomposition de Benders constituent la deuxième contribution. Finalement, la troisième contribution étend le modèle proposé au cas opérationnel, plus réaliste où nous considérons plusieurs points d’approche initiale. / Airport operations are well known to be a bottleneck in the air traffic system, which puts more and more pressure on the world busiest airports to optimally schedule landings, in particular, and also – but to a smaller extent – departures. The Aircraft Landing Problem (ALP) has arisen from this operational need. ALP consists in finding for aircraft heading to a given airport a landing sequence and landing times so as to optimize some given criteria (optimizing runway utilization, minimizing delays, etc) while satisfying operational constraints (safety constraints mainly). As a reply to this operational need, decision support tools have been designed and put on service for air traffic controllers since the early nineties in the US as well as in Europe. A considerable number of publications dealing with ALP focus on the deterministic and static case. However, the aircraft landing problem arising in practice has a dynamic nature riddled with uncertainties. In addition, operational horizon of current decision support tools are to be extended so that aircraft are captured at larger distances from the airport to hopefully start the scheduling process earlier. Such a horizon extension affects the quality of input data which enlarges the uncertainty effect. In this thesis, we aim at scheduling aircraft arrivals under uncertainty. For that purpose, we propose an approach based on two-stage stochastic programming. In the first stage, aircraft are captured at a large distance from the destination airport. They are to be scheduled on the same initial approach fix (IAF), a reference point in the near-to-airport area where aircraft start their approach phase preparing for landing. Actual IAF arrival times are assumed to be random variables with known probability distributions. In practice, such an uncertainty may cause loss of safety separations between aircraft. In such situations, air traffic controllers are expected to intervene to ensure air traffic safety. In order to alleviate the consequent air traffic control workload, chance constraints are introduced so that the safety risks around the IAF are limited to an acceptable level once the uncertainty is revealed. The second stage corresponds to the situation where aircraft are actually close to the IAF. In this stage, the uncertainty is revealed and a recourse decision is made in order to schedule aircraft on the runway threshold so that a second-stage cost function is minimized (e.g., air traffic control workload, delay cost, etc). Our first contribution is a proof of concept of the extended aircraft arrival management under uncertainty and a computational study on optimization parameters and problem characteristics. Modeling this problem as a two-stage stochastic programming model and solving it by a Benders decomposition is our second contribution. Finally, our third contribution focuses on extending our model to the more realistic case, where aircraft in the first stage are scheduled on several IAFs.

[pt] Este trabalho explora a estrutura de decomposição de um problema de planejamento da expansão da geração hidrotérmica, utilizando uma integração entre uma Decomposição de Benders modificada e um Progressive Hedging. Consideramos uma representação detalhada das restrições cronológicas de curto prazo, com resolução horária, baseando-se em dias típicos para cada etapa. Além disso, representamos a natureza estocástica de uma política operacional hidrotérmica multiestágio por meio de uma Regra de Decisão Linear otimizada, garantindo decisões de investimento compatíveis com uma política operacional não antecipativa. Para resolver este problema de otimização em grande escala, propomos um método de decomposição de Benders aprimorado com várias instâncias do problema mestre, onde cada uma delas é reforçada por cortes primários além dos cortes de Benders gerados a cada candidato a solução do mestre. Nossa nova abordagem permite o uso de termos de penalização de Progressive Hedging para fins de regularização. Mostramos que o algoritmo proposto é 60 porcento mais rápido que os tradicionais e que a consideração de uma política operacional não antecipativa pode economizar, em média, 8.27porcento do custo total em testes fora da amostra. / [en] This paper exploits the decomposition structure of the hydrothermal generation expansion planning problem with an integrated modified Benders Decomposition and Progressive Hedging approach. We consider a detailed representation of hourly chronological short-term constraints based on typical days per month and year. Also, we represent the multistage stochastic nature of the hydrothermal operational policy through an optimized linear decision rule, thereby ensuring investment decisions compatible with a nonanticipative implementable operational policy. To solve the resulting large-scale optimization problem, we propose an improved Benders Decomposition method with multiple instances of the master problem, each of which strengthened by primal cuts and new Benders cuts generated by each master s trial solution. Additionally, our new approach allows using Progressive Hedging penalization terms for regularization purposes. We show that our method is 60 percent faster than the traditional ones and also that the consideration of a nonanticipative operational policy can save, on average, 8.27 percent of the total cost in out-of-sample tests.

The air transportation market has been growing steadily for the past three decades since the airline deregulation in 1978. With competition also becoming more intense, airline companies have been trying to enhance their market shares and profit margins by composing favorable flight schedules and by efficiently allocating their resources of aircraft and crews so as to reduce operational costs. In practice, this is achieved based on demand forecasts and resource availabilities through a structured airline scheduling process that is comprised of four decision stages: schedule planning, fleet assignment, aircraft routing, and crew scheduling. The outputs of this process are flight schedules along with associated assignments of aircraft and crews that maximize the total expected profit. Traditionally, airlines deal with these four operational scheduling stages in a sequential manner. However, there exist obvious interdependencies among these stages so that restrictive solutions from preceding stages are likely to limit the scope of decisions for succeeding stages, thus leading to suboptimal results and even infeasibilities. To overcome this drawback, we first study the aircraft routing problem, and develop some novel modeling foundations based on which we construct and analyze an integrated model that incorporates fleet assignment, aircraft routing, and crew pairing within a single framework. Given a set of flights to be covered by a specific fleet type, the aircraft routing problem (ARP) determines a flight sequence for each individual aircraft in this fleet, while incorporating specific considerations of minimum turn-time and maintenance checks, as well as restrictions on the total accumulated flying time, the total number of takeoffs, and the total number of days between two consecutive maintenance operations. This stage is significant to airline companies as it directly assigns routes and maintenance breaks for each aircraft in service. Most approaches for solving this problem adopt set partitioning formulations that include exponentially many variables, thus requiring the design of specialized column generation or branch-and-price algorithms. In this dissertation, however, we present a novel compact polynomially sized representation for the ARP, which is then linearized and lifted using the Reformulation-Linearization Technique (RLT). The resulting formulation remains polynomial in size, and we show that it can be solved very efficiently by commercial software without complicated algorithmic implementations. Our numerical experiments using real data obtained from United Airlines demonstrate significant savings in computational effort; for example, for a daily network involving 344 flights, our approach required only about 10 CPU seconds for deriving an optimal solution. We next extend Model ARP to incorporate its preceding and succeeding decision stages, i.e., fleet assignment and crew pairing, within an integrated framework. We formulate a suitable representation for the integrated fleeting, routing, and crew pairing problem (FRC), which accommodates a set of fleet types in a compact manner similar to that used for constructing the aforementioned aircraft routing model, and we generate eligible crew pairings on-the-fly within a set partitioning framework. Furthermore, to better represent industrial practice, we incorporate itinerary-based passenger demands for different fare-classes. The large size of the resulting model obviates a direct solution using off-the-shelf software; hence, we design a solution approach based on Benders decomposition and column generation using several acceleration techniques along with a branch-and-price heuristic for effectively deriving a solution to this model. In order to demonstrate the efficacy of the proposed model and solution approach and to provide insights for the airline industry, we generated several test instances using historical data obtained from United Airlines. Computational results reveal that the massively-sized integrated model can be effectively solved in reasonable times ranging from several minutes to about ten hours, depending on the size and structure of the instance. Moreover, our benchmark results demonstrate an average of 2.73% improvement in total profit (which translates to about 43 million dollars per year) over a partially integrated approach that combines the fleeting and routing decisions, but solves the crew pairing problem sequentially. This improvement is observed to accrue due to the fact that the fully integrated model effectively explores alternative fleet assignment decisions that better utilize available resources and yield significantly lower crew costs. / Ph. D.

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

This thesis explores the use of a column generation method, a subgradient method, and a logic-based Benders decomposition method on a minimized makespan K-rural postman problem. The K-rural postman problem here describes a search and rescue mission using multiple identical unmanned aerial vehicles (UAVs) to cover an area, represented as a complete graph. Each decomposition method has a separate problem for each UAV. In the subgradient and column generation case, a heuristic is used to find an improved upper bound for the makespan. This upper bound can in turn be used to decrease the feasible regions of the subproblems. Moreover, because the subproblems are slow to solve, a maximum calculation time is used, resulting in a feasible solution and a lower bound for each subproblem. These two modifications to the decomposition methods result in a non-standard behaviour.  Multiple fictional problem instances of different sizes and numbers of UAVs were generated and used for evaluating the methods. A maximal time limit is used in these instances. We conclude that solving the original, non-decomposed, problem for smaller instances with a standard solver is faster and gives better results than the decomposition methods. For larger instances, solving the non-decomposed model led to memory issues on several occasions. However, the suggested subgradient and column generation methods can solve every problem. The logic-based Benders decomposition method performed best on instances with multiple UAVs, but had issues when fewer UAVs are utilized. / Den här masteruppsatsen utforskar användningen av en kolumngenereringsmetod, en subgradientmetod och en logikbaserad Benders dekompositionsmetod på en variant av lantbrevbärarproblemet. Vårat brevbärarprolem beskriver sök- och räddningsuppdrag där $K$ drönare används för att avsöka ett område med målfunktionen att minimera flygtiden för den långsammaste drönaren. Varje dekompositionsmetod använder sig av ett problem för varje drönare. I subgradient- och kolumngenereringsmetoden användes en heuristik för att hitta en bättre övre begränsning till drönarnas flygtid. Den förbättrade övre begränsningen kunde sedan användas för att minska det tillåtna området för de mindre problemen. Eftersom de mindre problem var svårlösta, användes en maximal beräkningstid vilket resulterade i att en tillåten lösning och undre gräns gavs för varje mindre problem. Dessa två modifikationer resulterade i icke typiska beteenden.  Metoderna utvärderades på flera fiktiva testinstanser av olika storlekar där antalet drönare varierar. En tidsbegränsning används på varje probleminstans. Slutsatserna från uppsatsen är de original brevbärare problemet ger bäst lösning och snabbast lösningstid i de mindre instanserna. Vid lösning av större probleminstanser, gav original problemet flerfaldiga gånger minnesproblem. Subgradient- och kolumngenereringsmetoden kunde däremot lösa varje probleminstans inom tidsbegränsningen, vilket gjorde de mer pålitliga. Logikbaserade Benders dekompositionsmetoden presterade bättre i instanser med flera drönare, men stötte på problem i instanser med färre drönare.

Dans des régions avec beaucoup d'électricité renouvelable, comme le Québec, une augmentation du nombre de Véhicules Électriques (VE) peut réduire les gaz à effet de serre. Par contre, l'autonomie réduite des VE et la présence limitée d'infrastructure publique pour recharger les véhicules peuvent contribuer à un phénomène nommé anxiété de l'autonomie, où les usagers n'achètent pas des VE par peur qu'ils tombent en panne. On peut alors planifier l'emplacement de l'infrastructure publique de recharge de manière stratégique pour combattre cet effet, menant alors à un taux d'adoption plus élevé pour les VE. En utilisant des modèles de choix discret, nous incorporons des modèles économétriques de demande avancés capturant les préférences hétérogènes des usagers à l'intérieur de l'optimisation. En particulier, comme nous le démontrerons, ceci permet l'inclusion de nouveaux facteurs importants, tels qu'une disponibilité de la recharge à domicile et des effets de distance plus granulaire. Par contre, la méthodologie existante pour ce processus crée un modèle de programmation linéaire mixte en nombres entiers qui ne peut pas être résolue, même pour des instances de taille modeste. Nous développons alors une reformulation efficace en problème de couverture maximum qui, comme nous le démontrerons, permet une amélioration de plusieurs ordres de magnitude pour le temps de calcul. Bien que cette reformulation dans un problème de couverture maximum améliore grandement la capacité à résoudre le modèle, celui-ci demeure difficile à résoudre pour des problèmes de grandes tailles, nécessitant des heuristiques pour obtenir des solutions de haute qualité. Nous développons alors deux méthodes de décomposition de Benders spécialisées pour cette application. La première est une méthode de décomposition de Benders accélérée, qui se spécialise à réduire l'écart d'optimalité et à la résolution de problèmes de petite taille ou de taille modeste. La deuxième approche rajoute un branchement local à la méthode de décomposition de Benders accélérée, qui sacrifie de l'efficacité lors de la résolution de problèmes de plus petite taille pour une capacité augmentée afin d'obtenir des solutions réalisables de haute qualité. Finalement, nous présentons une méthode pour dériver des valeurs de paramètres autrement difficiles à obtenir pour le modèle de choix discrets dans le modèle d'optimisation. Ces paramètres dictent les effets de l'infrastructure publique de recharge sur l'adoption des VE. Pour ce processus, nous regardons les facteurs qui encouragent les usagers courants des VE à utiliser l'infrastructure existante. De manière plus précise, nous utilisons des données de recharge réelles de la ville de Montréal (Québec) pour estimer les impacts des caractéristiques des stations, tels que la distance des usagers, le nombre de bornes de recharge, et les installations à proximité. Différents types d'infrastructure sont considérés, de manière parallèle avec des modèles de choix discrets qui peuvent tenir compte de plusieurs observations pour chaque individu. Les contributions de cette thèse sont plus générales que simplement l'adoption de VE, étant applicable, par exemple, au problème de capture maximum, au problème de couverture maximum à multiples périodes, et à la prédiction de la station de recharge choisie par les conducteurs de VE. / In areas with large amounts of clean renewable electricity, such as Quebec, an increase to the number of electric vehicles (EVs) can reduce greenhouse gas emissions. However, the reduced range of EVs and the limited public charging infrastructure can contribute to a phenomenon known as range anxiety, where users do not purchase EVs out of concern they run out of charge while driving. We can strategically optimise the placement of public EV charging infrastructure to combat this effect, thus leading to increased EV adoption. By utilising discrete choice models, we incorporate advanced econometric demand models capturing heterogeneous user preferences within the optimisation framework. In particular, as we demonstrate, this allows for the inclusion of new, important attributes, such as a more granular home charging availability and a continuous degradation of quality based on the distance. However, existing methodologies for this optimisation framework result in a mixed-integer linear program which cannot be solved for even moderately sized instances. We thus develop an efficient reformulation into a maximum covering location problem which, as we show experimentally, allows for multiple orders of magnitude of improved solving time. While the reformulation into a maximum covering location problem greatly improves the solving capabilities for the model, it remains intractable for large-scale instances, relying on heuristics to obtain high-quality solutions. As such, we then develop two specialised Benders decomposition methods for this application. The first is an accelerated branch-and-Benders-cut method, which excels at solving small or medium-scale instances and at decreasing the optimality gap. The second approach incorporates a local branching scheme to the accelerated branch-and-Benders-cut method, which sacrifices some efficiency in solving smaller instances for an increased ability to obtain high-quality feasible solutions. Finally, we discuss a method for deriving difficult-to-obtain parameter values of the discrete choice model in the optimisation framework. These parameter values dictate the effects of the public charging infrastructure on EV adoption and, as such, play a crucial role in the optimisation model. For this process, we investigate the attributes that encourage current EV owners to utilise existing infrastructure. More specifically, we use real charging session data from the city of Montreal (Quebec) to determine the impacts of station characteristics such as the distance to the users, the number of outlets, and the nearby amenities. Different types of charging infrastructure are considered alongside discrete choice models which take into account multiple observations from individual users. The contributions of this thesis lie more broadly than simply EV adoption, being applicable to, e.g., the maximum capture problem, the multi-period maximum covering location problem, and the prediction of the charging station selected by EV drivers.

Le concept de Ville Intelligent concerne l’interconnectivité totale de plusieurs industries vers l’amélioration des modes de vie des résidents. Ceci est rendu possible par la croissance et l'utilisation généralisée de l'Internet des objets (IoT), un vaste réseau de dispositifs de collecte de données répartis dans de multiples applications. Cependant, la plupart des appareils IoT disposent de peu de ressources et s'appuient sur des serveurs externes pour traiter et stocker les données collectées. En raison de la congestion et de la distance élevées, les centres de données Nuage (Cloud) peuvent entraîner une latence élevée dans leur réponse IoT, ce qui peut être inacceptable dans certaines applications IoT. Au lieu de cela, l'informatique Brouillard (fog-computing) a été proposé comme une couche hétérogène hautement virtualisée de serveurs à la périphérie du réseau, ce qui permet un traitement des données IoT à faible latence. Les contributions actuelles au brouillard informatique supposent qu'une infrastructure de brouillard est déjà en place. De plus, chaque contribution nécessite des caractéristiques différentes sur l’infrastructure du brouillard. Cette thèse formule un schéma de conception et de dimensionnement évolutif et modifiable pour une infrastructure de brouillard généralisée. Ceci est modélisé et résolu sous la forme d'un programme linéaire à nombres entiers mixtes (MILP), et détendu à l'aide de plusieurs techniques telles que la génération de colonnes et la décomposition de Benders. De nombreuses préoccupations concernant les performances du réseau brouillard sont prises en compte et résolues, telles que le trafic IoT élevé, la congestion du réseau et les dysfonctionnements des nœuds brouillard. Les nœuds de brouillard dynamiques, tels que les nœuds de brouillard à la demande et les véhicules aériens sans pilote mobiles (UAV-brouillard) sont intégrés dans les modèles de conception et de dimensionnement actuels pour ajouter de la flexibilité et de la robustesse au réseau. Un système basé sur la blockchain et des preuves de connaissance nulle est introduit pour renforcer l'intégrité des nœuds de brouillard. Le résultat est un schéma de conception et de dimensionnement évolutif pour une infrastructure de brouillard robuste, flexible et fiable dans un environnement de brouillard-IoT dynamique et malveillant. / The concept of a Smart City relies on the full interconnectivity of several industries towards the amelioration of resident lifestyles. This is made possible by the growth and wide-spread use of the Internet of Things (IoT) -- a large network of data collection devices throughout multiple applications. However, most IoT devices have few resources, and rely on external servers to process and store the collected data. Due to high congestion and distance, Cloud data centres may cause high latency in their IoT response, which may be unacceptable in certain IoT applications. Instead, fog-computing has been proposed as a highly-virtualized heterogeneous layer of servers on the network edge, resulting in low-latency IoT data processing. Current contributions in fog-computing assume a fog infrastructure is already in-place. Furthermore, each contribution requires different characteristics on the fog infrastructure. This thesis formulates a scalable and modifiable design & dimensioning scheme for a generalized fog infrastructure. This is modeled and solved as a mixed-integer linear program (MILP), and relaxed using several techniques such as Column Generation and Benders Decomposition. Many concerns on the fog network performance are considered and addressed, such as high IoT traffic, network congestion, and fog node malfunctions. Dynamic fog nodes, such as on-demand fog nodes and mobile fog-enabled unmanned aerial vehicles (fog-UAVs) are integrated into current design & dimensioning models to add flexibility and robustness to the network. A system based on blockchain and zero-knowledge proofs is introduced to enforce integrity on the fog nodes. The result is a scalable design & dimensioning scheme for a robust, flexible, and reliable fog infrastructure in a dynamic and malicious IoT-fog environment.

This dissertation focuses on selected issues in regard to the mathematical modeling of electricity markets. In a first step the interrelations of electric power market modeling are highlighted a crossroad between operations research, applied economics, and engineering. In a second step the development of a large-scale continental European economic engineering model named ELMOD is described and the model is applied to the issue of wind integration. It is concluded that enabling the integration of low-carbon technologies appears feasible for wind energy. In a third step algorithmic work is carried out regarding a game theoretic model. Two approaches in order to solve a discretely-constrained mathematical program with equilibrium constraints using disjunctive constraints are presented. The first one reformulates the problem as a mixed-integer linear program and the second one applies the Benders decomposition technique. Selected numerical results are reported.