Operation of the electricity markets is subject to a number of strict and specific constraints such as continuous load-generation balance, security of supply, and generation technology related limitations. Contributions have been made to two important problems of the Electricity Markets, in the context of this study. In this study, Price Based Unit Commitment problem in the literature, which is a tool for the GENCO for operations planning, is extended considering the interdependencies between the Natural Gas (NG) and Electricity infrastructures and the uncertainty of Wind Power generation. The effect of the NG infrastructure physical limitations is considered via linearized NG transmission system equations, and the Wind energy sources and conventional generation resource uncertainties are simulated by Monte-Carlo simulations. The contribution of the forward energy Bilateral Contracts (BC), as a financial risk hedging tool is also included by modeling these in the proposed PBUC framework. In the case studies , it is observed that a GENCO could prevent its financial losses due to NG interruptions, by depositing only a portion of the midterm interrupted NG in the storage facilities. The Security Constrained Unit Commitment (SCUC) Problem is widely accepted tool in the industry which models the market clearing process. This study integrates two novelties to the SCUC problem / &bull / A discrete demand response model to consider active participation of the consumers, &bull / A hybrid deterministic/stochastic contingency model to represent the N-1 contingencies together with the uncertainties related with the wind power generation and system load. It is observed that the curtailment of available wind power capacity would enable the TSO to take corrective actions against occurrence of the contingencies and realization of the uncertainties in the most possible economical manner.

Dans ce manuscrit de thèse, mes travaux portent sur la combinaison de méthodes pour la prise de décision séquentielle (plusieurs étapes de décision corrélées) dans des environnements complexes et incertains. Les méthodes mises au point sont essentiellement appliquées à des problèmes de gestion et de production d'électricité tels que l'optimisation de la gestion des stocks d'énergie dans un parc de production pour anticiper au mieux la fluctuation de la consommation des clients.Le manuscrit comporte 7 chapitres regroupés en 4 parties : Partie I, « Introduction générale », Partie II, « État de l'art », Partie III, « Contributions » et Partie IV, « Conclusion générale ».Le premier chapitre (Partie I) introduit le contexte et les motivations de mes travaux, à savoir la résolution de problèmes d' « Unit commitment », c'est à dire l'optimisation des stratégies de gestion de stocks d'énergie dans les parcs de production d'énergie. Les particularités et les difficultés sous-jacentes à ces problèmes sont décrites ainsi que le cadre de travail et les notations utilisées dans la suite du manuscrit.Le second chapitre (Partie II) dresse un état de l'art des méthodes les plus classiques utilisées pour la résolution de problèmes de prise de décision séquentielle dans des environnements incertains. Ce chapitre introduit des concepts nécessaires à la bonne compréhension des chapitres suivants (notamment le chapitre 4). Les méthodes de programmation dynamique classiques et les méthodes de recherche de politique directe y sont présentées.Le 3e chapitre (Partie II) prolonge le précédent en dressant un état de l'art des principales méthodes d’optimisation spécifiquement adaptées à la gestion des parcs de production d'énergie et à leurs subtilités. Ce chapitre présente entre autre les méthodes MPC (Model Predictive Control), SDP (Stochastic Dynamic Programming) et SDDP (Stochastic Dual Dynamic Programming) avec pour chacune leurs particularités, leurs avantages et leurs limites. Ce chapitre complète le précédent en introduisant d'autres concepts nécessaires à la bonne compréhension de la suite du manuscrit.Le 4e chapitre (Partie III) contient la principale contribution de ma thèse : un nouvel algorithme appelé « Direct Value Search » (DVS) créé pour résoudre des problèmes de prise de décision séquentielle de grande échelle en milieu incertain avec une application directe aux problèmes d' « Unit commitment ». Ce chapitre décrit en quoi ce nouvel algorithme dépasse les méthodes classiques présentées dans le 3e chapitre. Cet algorithme innove notamment par sa capacité à traiter des grands espaces d'actions contraints dans un cadre non-linéaire, avec un grand nombre de variables d'état et sans hypothèse particulière quant aux aléas du système optimisé (c'est à dire applicable sur des problèmes où les aléas ne sont pas nécessairement Markovien).Le 5e chapitre (Partie III) est consacré à un concept clé de DVS : l'optimisation bruitée. Ce chapitre expose une nouvelle borne théorique sur la vitesse de convergence des algorithmes d'optimisation appliqués à des problèmes bruités vérifiant certaines hypothèses données. Des méthodes de réduction de variance sont également étudiées et appliquées à DVS pour accélérer sensiblement sa vitesse de convergence.Le 6e chapitre (Partie III) décrit un résultat mathématique sur la vitesse de convergence linéaire d’un algorithme évolutionnaire appliqué à une famille de fonctions non quasi-convexes. Dans ce chapitres, il est prouvé que sous certaines hypothèses peu restrictives sur la famille de fonctions considérée, l'algorithme présenté atteint une vitesse de convergence linéaire.Le 7e chapitre (Partie IV) conclut ce manuscrit en résumant mes contributions et en dressant quelques pistes de recherche intéressantes à explorer. / This thesis is dedicated to sequential decision making (also known as multistage optimization) in uncertain complex environments. Studied algorithms are essentially applied to electricity production ("Unit Commitment" problems) and energy stock management (hydropower), in front of stochastic demand and water inflows. The manuscript is divided in 7 chapters and 4 parts: Part I, "General Introduction", Part II, "Background Review", Part III, "Contributions" and Part IV, "General Conclusion". This first chapter (Part I) introduces the context and motivation of our work, namely energy stock management. "Unit Commitment" (UC) problems are a classical example of "Sequential Decision Making" problem (SDM) applied to energy stock management. They are the central application of our work and in this chapter we explain main challenges arising with them (e.g. stochasticity, constraints, curse of dimensionality, ...). Classical frameworks for SDM problems are also introduced and common mistakes arising with them are be discussed. We also emphasize the consequences of these - too often neglected - mistakes and the importance of not underestimating their effects. Along this chapter, fundamental definitions commonly used with SDM problems are described. An overview of our main contributions concludes this first chapter. The second chapter (Part II) is a background review of the most classical algorithms used to solve SDM problems. Since the applications we try to solve are stochastic, we there focus on resolution methods for stochastic problems. We begin our study with classical Dynamic Programming methods to solve "Markov Decision Processes" (a special kind of SDM problems with Markovian random processes). We then introduce "Direct Policy Search", a widely used method in the Reinforcement Learning community. A distinction is be made between "Value Based" and "Policy Based" exploration methods. The third chapter (Part II) extends the previous one by covering the most classical algorithms used to solve UC's subtleties. It contains a state of the art of algorithms commonly used for energy stock management, mainly "Model Predictive Control", "Stochastic Dynamic Programming" and "Stochastic Dual Dynamic Programming". We briefly overview distinctive features and limitations of these methods. The fourth chapter (Part III) presents our main contribution: a new algorithm named "Direct Value Search" (DVS), designed to solve large scale unit commitment problems. We describe how it outperforms classical methods presented in the third chapter. We show that DVS is an "anytime" algorithm (users immediately get approximate results) which can handle large state spaces and large action spaces with non convexity constraints, and without assumption on the random process. Moreover, we explain how DVS can reduce modelling errors and can tackle challenges described in the first chapter, working on the "real" detailed problem without "cast" into a simplified model. Noisy optimisation is a key component of DVS algorithm; the fifth chapter (Part III) is dedicated to it. In this chapter, some theoretical convergence rate are studied and new convergence bounds are proved - under some assumptions and for given families of objective functions. Some variance reduction techniques aimed at improving the convergence rate of graybox noisy optimization problems are studied too in the last part of this chapter. Chapter sixth (Part III) is devoted to non-quasi-convex optimization. We prove that a variant of evolution strategy can reach a log-linear convergence rate with non-quasi-convex objective functions. Finally, the seventh chapter (Part IV) concludes and suggests some directions for future work.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-11T19:29:47Z No. of bitstreams: 1 SidneyCerqueiraJunior.pdf: 2395617 bytes, checksum: cbf8e82ed5431d78b69640d4a6b7d511 (MD5) / Made available in DSpace on 2017-08-11T19:29:47Z (GMT). No. of bitstreams: 1 SidneyCerqueiraJunior.pdf: 2395617 bytes, checksum: cbf8e82ed5431d78b69640d4a6b7d511 (MD5) Previous issue date: 2012-06-01 / In the last years, the process of restructuring of the electricity market, brought several changes in the operational e regulatory aspects. The main idea was the separation of the generation, transmission and distribution activities in order to insert the competition among them, aimed to increase the e ciency, safety and quality of supply of electrical energy. The hourly schedule, usually called a Unit Commitment has as objective the de - nition of which generators should be online/o ine and their respective operation points. In some markets based on this new model, the determination of the optimal scheduling of generators (thermal and hydro) is made by the Agent Generator, which is largely responsible for the allocation of your portfolio. Given this, the aim of this work is to nd the operational policy that will maximize the pro t of Agent Generator, based on forecast price and respecting the thermal, hydro and market constrictions assigned to the problem. Thus, the optimal schedule found is an important factor in developing strategies to o ers of bids to auctions in which the Genco will participate. For the case study technique Particle Swarm Optimization is applied to solve the problem in plants belonging to the Brazilian electric system, which are also analyzed the in uence of the start-up cost to the optimal schedule. / Nos últimos anos, o processo de reestruturação da indústria da eletricidade, trouxe diversas mudanças nos aspectos operacionais e regulatórios. A ideia principal foi a separação das atividades de geração, transmissão e distribuição, de modo a inserir competição entre esses, visando o aumento da e ficiência, segurança e qualidade no fornecimento da energia elétrica. A programação horária, usualmente denominada de Pré-Despacho de Potência, tem como objetivo a defi nição de quais unidades devem estar ligadas/desligadas e seus respectivos pontos de operação. Em alguns mercados baseado neste novo modelo, a determinação da programação ótima dos geradores (termelétricas e hidrelétricas) é feita pelo próprio Agente Gerador, sendo este o maior responsável pela alocação de seu portfólio. Diante disto, o objetivo deste trabalho é encontrar a política operativa que irá maximizar o lucro desse Agente Gerador, baseado na previsão de preço horário e respeitando as restrições térmicas, hidráulicas e de mercado atribuídas ao problema. Assim, a programação ótima encontrada é um importante fator para elaboração das estratégias de ofertas de lances a leilões em que o Agente Gerador irá participar. Para estudo de caso, a técnica Otimização por Enxame de Partículas é aplicada para solucionar o problema em usinas que pertencem ao sistema elétrico brasileiro, onde é analisado também a influência do custo de partida na programação ótima horária.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-14T18:59:08Z No. of bitstreams: 1 MauroSergioSilvaPinto.pdf: 1648622 bytes, checksum: 71f809c341318a8660df7cdd2182a4f0 (MD5) / Made available in DSpace on 2017-08-14T18:59:08Z (GMT). No. of bitstreams: 1 MauroSergioSilvaPinto.pdf: 1648622 bytes, checksum: 71f809c341318a8660df7cdd2182a4f0 (MD5) Previous issue date: 2016-07-01 / The Unit Commitment Problem (UC) in power generation is a difficult problem, traditionally modeled with a mixed-integer optimization formulation. What makes it especially difficult is the time-dependency of the generation decisions, caused by ramping limitation constraints applied mostly to thermal generation, as well as minimum shut down and start up times. The main types of uncertainty are usually taken in account: in the actual load values and the (un)reliability of the generators. The uncertainty in generator availability has been met with a specification of operational reserve policy. The uncertainty in load, taking in account that its magnitude is usually small, is in many cases simply ignored. With the significant inclusion of wind power in the portfolio of a county or region, it is no longer adequate to deal with the UC problem in the traditional way. The uncertainty in wind generation is at least one order of magnitude higher than the uncertainty in load. Moreover, the wind behavior includes the possibility of strong ramping, with important stressing effect on thermal generation. Dealing with such challenges in a business-as-usual manner is doomed to produce sub-optimal solutions and to put the system in jeopardy or cause substantial financial loss with costly emergency actions. The transition to models that take risk in account supposes a change in paradigm in the decisionmaking process in the UC process. Without clear guidelines, operators will tend to over-protect – while under commercial pressure, they may run excessive risks. To help in the transition to a UC decision-making process under uncertainty, this thesis contributes to the set of planning paradigms and makes an attempt to organize the comparative analysis and results and conclusions reached, from an illustrative case built around the IEEE RTS 30-bus system. The results show that the Pareto-optimal front, in a stochastic cost vs. risk space, may not be convex, which precludes the use of simplistic trade-off approaches. The conclusion, as a contribution from this thesis, is unmistakable: a stochastic programming approach is not adequately informative on the risks run as consequence of system operator decisions on unit commitment, in systems with a high penetration of wind power. Models that follow the Risk Analysis paradigm are necessary, in order to quantify the costs of hedging (protecting against adverse scenarios). Furthermore, by relying on an explicit multiple criteria representation, the thesis shows how this risk aversion perspective, in terms of undesired events, may be blended with a stochastic optimization perspective of average gain or expense On the planning matter, embedding the risk in the operating cost annually capitalized assists the decision-making in investments in system planning. / O problema do pré-despacho em sistemas de potência, conhecido na literatura como Unit Commitment -UC, é um problema não linear, tradicionalmente modelado como uma formulação de otimização inteira mista. Um dos pontos críticos deste processo é a sua interdependência temporal, além de restrições como tempos mínimos de parada e partida. Tradicionalmente, as fontes de incertezas no sistema são o valor atual da carga e a disponibilidade do fornecimento de potência por parte dos geradores, relacionando-se com a confiabilidade. A geração deve satisfazer a critérios que determinam um nível mínimo de reserva girante ou uma política de reserva operacional através de métricas determinísticas ou estocásticas. A incerteza da carga é menor e muitas vezes é desprezada no processo de pré-despacho. Em ambos os casos, o objetivo é transformar um problema com incerteza em um modelo determinístico. Devido à elevada integração de fontes eólicas na matriz energética, as abordagens tradicionais de pré-despacho se tornam inadequadas para lidar com as incertezas associadas a este tipo de fonte. O grau de incerteza das fontes eólicas é pelo menos maior em magnitude do que o grau de incertezas da carga. Além disso, o comportamento do vento inclui a possibilidade de fortes rajadas que podem se transformar em eventos de rampa não previstos. Diante disto, as formas usuais de tratar este problema podem produzir soluções sub-ótimas, colocando o sistema em risco ou causando perda financeira substancial por ações de correções técnicas dispendiosas. A transição para modelos que levam em conta o risco propõe uma mudança de paradigma no processo de tomada de decisão do problema do pré-desapcho. Desta forma, estes desafios exigem que novos modelos de decisão sejam elaborados levando em conta este novo quadro de incertezas e que forneçam soluções úteis para o planejamento da operação. Em particular, no que se refere à operação do sistema, são necessárias ferramentas que auxiliem os operadores na tomada de decisões levando em conta o risco decorrente do ambiente de incerteza presente no sistema. Esta tese vem contribuir no suporte ao processo de tomada de decisão, analisando um conjunto de paradigmas de planejamento quanto a sua capacidade/utilidade de fornecer soluções consistentes em sistemas com significativa integração eólica. Os resultados mostraram que a fronteira de Pareto das soluções ótimas não-dominadas em um espaço multicritério entre custo estocástico versus o risco pode não ser convexa, o que impede uma abordagem de análise simples de trade-off. Mostra-se, que o modelo tradicional estocástico pode não ser adequado para lidar com as incertezas geradas pelas fontes eólicas. Além disso, esta Tese mostra que eventos indesejados, sob uma perspectiva de risco em um espaço multicritério, podem ser negligenciados pela abordagem tradicional estocástica. No muito curto prazo, a abordagem de tomada de decisão com incertezas eólicas mostra que o simples despacho de mais reservas operacionais no sistema com alta penetração eólica pode ser insuficiente para lidar com as incertezas. Sob o aspecto do planejamento, a incorporação do risco nos custos de operação capitalizados anualmente auxilia a tomada de decisão de investimentos no planejamento do sistema.

Our work is driven by a class of practical problems of sequential decision making in the context of electric power generation under uncertainties. These problems are usually treated as receding horizon deterministic optimization problems, and/or as scenario-based stochastic programs. Stochastic programming allows to compute a first stage decision that is hedged against the possible futures and -- if a possibility of recourse exists -- this decision can then be particularized to possible future scenarios thanks to the information gathered until the recourse opportunity. Although many decomposition techniques exist, stochastic programming is currently not tractable in the context of day-ahead electric power generation and furthermore does not provide an explicit recourse strategy. The latter observation also makes this approach cumbersome when one wants to evaluate its value on independent scenarios. We propose a supervised learning methodology to learn an explicit recourse strategy for a given generation schedule, from optimal adjustments of the system under simulated perturbed conditions. This methodology may thus be complementary to a stochastic programming based approach. With respect to a receding horizon optimization, it has the advantages of transferring the heavy computation offline, while providing the ability to quickly infer decisions during online exploitation of the generation system. Furthermore the learned strategy can be validated offline on an independent set of scenarios. On a realistic instance of the intra-day electricity generation rescheduling problem, we explain how to generate disturbance scenarios, how to compute adjusted schedules, how to formulate the supervised learning problem to obtain a recourse strategy, how to restore feasibility of the predicted adjustments and how to evaluate the recourse strategy on independent scenarios. We analyze different settings, namely either to predict the detailed adjustment of all the generation units, or to predict more qualitative variables that allow to speed up the adjustment computation procedure by facilitating the ``classical' optimization problem. Our approach is intrinsically scalable to large-scale generation management problems, and may in principle handle all kinds of uncertainties and practical constraints. Our results show the feasibility of the approach and are also promising in terms of economic efficiency of the resulting strategies. The solutions of the optimization problem of generation (re)scheduling must satisfy many constraints. However, a classical learning algorithm that is (by nature) unaware of the constraints the data is subject to may indeed successfully capture the sensitivity of the solution to the model parameters. This has nevertheless raised our attention on one particular aspect of the relation between machine learning algorithms and optimization algorithms. When we apply a supervised learning algorithm to search in a hypothesis space based on data that satisfies a known set of constraints, can we guarantee that the hypothesis that we select will make predictions that satisfy the constraints? Can we at least benefit from our knowledge of the constraints to eliminate some hypotheses while learning and thus hope that the selected hypothesis has a better generalization error? In the second part of this thesis, where we try to answer these questions, we propose a generic extension of tree-based ensemble methods that allows incorporating incomplete data but also prior knowledge about the problem. The framework is based on a convex optimization problem allowing to regularize a tree-based ensemble model by adjusting either (or both) the labels attached to the leaves of an ensemble of regression trees or the outputs of the observations of the training sample. It allows to incorporate weak additional information in the form of partial information about output labels (like in censored data or semi-supervised learning) or -- more generally -- to cope with observations of varying degree of precision, or strong priors in the form of structural knowledge about the sought model. In addition to enhancing the precision by exploiting information that cannot be used by classical supervised learning algorithms, the proposed approach may be used to produce models which naturally comply with feasibility constraints that must be satisfied in many practical decision making problems, especially in contexts where the output space is of high-dimension and/or structured by invariances, symmetries and other kinds of constraints.

In electricity markets of modern electric power systems, many generation companies, as major market participants, aim to maximize their profits by supplying the electrical load in a competitive manner. This thesis is devoted to investigate the price based unit commitment problem which is used to optimize generation schedules of these companies in deregulated electricity markets. The solution algorithm developed is based on Dynamic Programming and Lagrange Relaxation methods and solves the optimization problem for a generation company having many generating units with different cost characteristics. Moreover, unit constraints including ramp-rate limits, minimum ON/OFF times, generation capacities of individual units and system constraints such as total energy limits, reserve requirements are taken into account in the problem formulation. The verification of the algorithm has been carried out by comparing the results of some sample cases with those in the literature. The effectiveness of the algorithm has been tested on several test systems. Finally, the possible utilization of the method by a generation company in Turkish Electricity Market to develop bidding strategies is also examined based on some case studies.

The growing interest in small-scale electricity generation located near customers, known as Distributed Generation (DG), is driven primarily by emerging technologies, environmental regulations and concerns, electricity market restructuring, and growing customer demand for increased quality and reliability of the electricity supply. Wind turbines are one of the renewable DG technologies that have become an important source of electricity in many parts of the world. Wind power can be used in many places to provide a viable solution to rising demand, energy security and independence, and climate change mitigation. This research aims broadly at facilitating the integration of wind-based DG without jeopardizing the system’s economics and reliability. To achieve this goal, the thesis tackles wind power from three perspectives: those of the policy maker, the investor, and the system operator. Generally, the economic viability of a project is determined within the framework of relevant policies. Therefore, these policies influence the decisions of potential investors in wind power. From this perspective, chapters 3 and 4 investigate the influence of policies on the economic viability of wind-based DG projects. In chapter 3, the role of Ontario’s taxation and incentive policies in the economic viability of wind-based DG projects is investigated. In this study, the effects of provincial income taxes, capital cost allowances, property taxes, and relevant federal incentives are considered. Net Present Value (NPV) and Internal Rate of Return (IRR) for different scenarios are used to assess the project’s viability under the Ontario Standard Offer Program (SOP) for wind power. In chapter 4, the thesis proposes the use of wind power as a source of electricity in a new city being developed in the Duqm area of Oman, where no policies supporting renewable energy exist. The study shows that the cost of electricity produced by wind turbines is higher than that of the existing generation system, due to the subsidized prices of domestically available natural gas. However, given high international natural gas prices, the country’s long-term Liquefied Natural Gas (LNG) export obligations, and the expansion of natural gas-based industries, investments in wind power in Duqm can be justified. A feed-in tariff and capital cost allowance policies are recommended to facilitate investments in this sector. From a wind-based DG investor’s perspective, the optimal selection of wind turbines can make wind power more economical, as illustrated in chapters 5 and 6. In chapter 5, the thesis presents a new generic model for Capacity Factor (CF) estimation using wind speed characteristics at any site and the power performance curve parameters of any pitch-regulated wind turbine. Compared to the existing model, the proposed formulation is simpler and results in more accurate CF estimation. CF models can be used by wind-based DG investors for optimal turbine-site matching applications. However, in chapter 6, the thesis demonstrates that using CF models as the sole basis for turbine-site matching applications tends to produce results that are biased towards higher towers but do not include the associated costs. Therefore, a novel formulation for the turbine-site matching problem, based on a modified CF formulation that does include turbine tower height, is introduced in chapter 6. The proposed universal Turbine-Site Matching Index (TSMI) also includes the effects of turbine rated power and tower height on the initial capital cost of wind turbines. Chapter 7 tackles wind power from a power system operator’s perspective. Despite wind power benefits, the effects of its intermittent nature on power systems need to be carefully examined as penetration levels increase. In this chapter, the thesis investigates the effects of different temporal wind profiles on the scheduling costs of thermal generation units. Two profiles are considered: synoptic-dominated and diurnal-dominated variations of aggregated wind power. To simulate wind profile impacts, a linear mixed-integer unit commitment problem is formulated in a GAMS environment. The uncertainty associated with wind power is represented using a chance constrained formulation. The simulation results illustrate the significant impacts of different wind profiles on fuel saving benefits, startup costs, and wind power curtailments. In addition, the results demonstrate the importance of the wide geographical dispersion of wind power production facilities to minimize the impacts of network constraints on the value of the harvested wind energy and the amount of curtailed energy.

The growing interest in small-scale electricity generation located near customers, known as Distributed Generation (DG), is driven primarily by emerging technologies, environmental regulations and concerns, electricity market restructuring, and growing customer demand for increased quality and reliability of the electricity supply. Wind turbines are one of the renewable DG technologies that have become an important source of electricity in many parts of the world. Wind power can be used in many places to provide a viable solution to rising demand, energy security and independence, and climate change mitigation. This research aims broadly at facilitating the integration of wind-based DG without jeopardizing the system’s economics and reliability. To achieve this goal, the thesis tackles wind power from three perspectives: those of the policy maker, the investor, and the system operator. Generally, the economic viability of a project is determined within the framework of relevant policies. Therefore, these policies influence the decisions of potential investors in wind power. From this perspective, chapters 3 and 4 investigate the influence of policies on the economic viability of wind-based DG projects. In chapter 3, the role of Ontario’s taxation and incentive policies in the economic viability of wind-based DG projects is investigated. In this study, the effects of provincial income taxes, capital cost allowances, property taxes, and relevant federal incentives are considered. Net Present Value (NPV) and Internal Rate of Return (IRR) for different scenarios are used to assess the project’s viability under the Ontario Standard Offer Program (SOP) for wind power. In chapter 4, the thesis proposes the use of wind power as a source of electricity in a new city being developed in the Duqm area of Oman, where no policies supporting renewable energy exist. The study shows that the cost of electricity produced by wind turbines is higher than that of the existing generation system, due to the subsidized prices of domestically available natural gas. However, given high international natural gas prices, the country’s long-term Liquefied Natural Gas (LNG) export obligations, and the expansion of natural gas-based industries, investments in wind power in Duqm can be justified. A feed-in tariff and capital cost allowance policies are recommended to facilitate investments in this sector. From a wind-based DG investor’s perspective, the optimal selection of wind turbines can make wind power more economical, as illustrated in chapters 5 and 6. In chapter 5, the thesis presents a new generic model for Capacity Factor (CF) estimation using wind speed characteristics at any site and the power performance curve parameters of any pitch-regulated wind turbine. Compared to the existing model, the proposed formulation is simpler and results in more accurate CF estimation. CF models can be used by wind-based DG investors for optimal turbine-site matching applications. However, in chapter 6, the thesis demonstrates that using CF models as the sole basis for turbine-site matching applications tends to produce results that are biased towards higher towers but do not include the associated costs. Therefore, a novel formulation for the turbine-site matching problem, based on a modified CF formulation that does include turbine tower height, is introduced in chapter 6. The proposed universal Turbine-Site Matching Index (TSMI) also includes the effects of turbine rated power and tower height on the initial capital cost of wind turbines. Chapter 7 tackles wind power from a power system operator’s perspective. Despite wind power benefits, the effects of its intermittent nature on power systems need to be carefully examined as penetration levels increase. In this chapter, the thesis investigates the effects of different temporal wind profiles on the scheduling costs of thermal generation units. Two profiles are considered: synoptic-dominated and diurnal-dominated variations of aggregated wind power. To simulate wind profile impacts, a linear mixed-integer unit commitment problem is formulated in a GAMS environment. The uncertainty associated with wind power is represented using a chance constrained formulation. The simulation results illustrate the significant impacts of different wind profiles on fuel saving benefits, startup costs, and wind power curtailments. In addition, the results demonstrate the importance of the wide geographical dispersion of wind power production facilities to minimize the impacts of network constraints on the value of the harvested wind energy and the amount of curtailed energy.

In recent year, the awareness of environmental protection has made the power dispatch problem not necessarily economy-oriented. This thesis proposed the application of Particle Swarm Optimization (PSO) algorithm to solve the Unit Commitment (UC) problem for 24 hours with maximum profit in the power and carbon market. Optimal Power Flow (OPF) is used to solve the UC problem for the interconnected power network that is comprised of three independent areas to optimize the dispatching strategy. The UC problem must satisfy the constraints of the load demand, generating limits, minimum up/down time, ramp rate limits, and also the limits of power flow, buses voltage and transmission line capacity. The other objective of this thesis is to employ the Static Synchronous Series Compensator (SSSC) to integrate with OPF based on Equivalent Current Injection (ECI) power flow model, and install it at interconnected lines between each independent area controlling the power flow to reduce emission. In order to avoid the local optimality problem, this thesis proposed the utilization of the Multiple Particle Swarm Optimization (MPSO), which can quickly reach the optimal solution with a better performance and accuracy. The Independent Power Producer (IPP) can get the maximum profit with installed SSSC from the power and carbon trading with the calculation of power wheeling expense and carbon forecasting data. Furthermore, it can also assess the need of participating in the trading market or not.

This dissertation is a collection of previously-published manuscript and conference papers. In this dissertation, we will deal with a stochastic unit commitment problem with cooling systems for gas generators, a robust unit commitment problem with demand response and uncertain wind generation, and a power grid vulnerability analysis with transmission line switching. The latter two problems correspond to our theoretical contributions in two-stage robust optimization, i.e., how to efficiently solve a two-stage robust optimization, and how to deal with mixed-integer recourse in robust optimization. Due to copyright issue, this dissertation does not include any methodology papers written by the author during his PhD study. Readers are referred to the author's website for a complete list of publications.