Yes / In this paper, a stochastic method is proposed to assess the amount of active and reactive power that can be injected/absorbed to/from grid within a distribution market environment. Also, the impact of wind power penetration on the reactive and active distribution-locational marginal prices is investigated. Market-based active and reactive optimal power flow is used to maximize the social welfare considering uncertainties related to wind speed and load demand. The uncertainties are modeled by Scenario-based approach. The proposed model is examined with 16-bus UK generic distribution system. / Supported by the Higher Education Ministry of Iraqi government.

Yes / This paper proposes a novel method for distribution network reconfiguration considering security-constraints and multi-configuration of renewable distributed generators (DG). The objective of the proposed method is to minimize the total operational cost using security constrained optimal power flow (SCOPF). The impact of multi-configuration of renewable DGs in a meshed network is investigated. In this work, lines were added to the radial distribution network to analyse the network power flow in different network configurations. The added lines were connected to the closest generator bus which offered least operating cost. A 16-bus UK generic distribution system (UKGDS) was used to model the efficiency of the proposed method. The obtained results in multi-DG configuration ensure the security of the network in N-1 contingency criteria.

Massive infrastructure networks such as electric power, natural gas, or water systems play a pivotal role in everyday human lives. Development and operation of these networks is extremely capital-intensive. Moreover, security and reliability of these networks is critical. This work identifies and addresses a diverse class of computationally challenging and time-critical problems pertaining to these networks. This dissertation extends the state of the art on three fronts. First, general proofs of uniqueness for network flow problems are presented, thus addressing open problems. Efficient network flow solvers based on energy function minimizations, convex relaxations, and mixed-integer programming are proposed with performance guarantees. Second, a novel approach is developed for sample-efficient training of deep neural networks (DNN) aimed at solving optimal network dispatch problems. The novel feature here is that the DNNs are trained to match not only the minimizers, but also their sensitivities with respect to the optimization problem parameters. Third, control mechanisms are designed that ensure resilient and stable network operation. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks. / Doctor of Philosophy / Massive infrastructure networks play a pivotal role in everyday human lives. A minor service disruption occurring locally in electric power, natural gas, or water networks is considered a significant loss. Uncertain demands, equipment failures, regulatory stipulations, and most importantly complicated physical laws render managing these networks an arduous task. Oftentimes, the first principle mathematical models for these networks are well known. Nevertheless, the computations needed in real-time to make spontaneous decisions frequently surpass the available resources. Explicitly identifying such problems, this dissertation extends the state of the art on three fronts: First, efficient models enabling the operators to tractably solve some routinely encountered problems are developed using fundamental and diverse mathematical tools; Second, quickly trainable machine learning based solutions are developed that enable spontaneous decision making while learning offline from sophisticated mathematical programs; and Third, control mechanisms are designed that ensure a safe and autonomous network operation without human intervention. These novel solutions are bolstered by mathematical guarantees and extensive simulations on benchmark power, water, and natural gas networks.

Submitted by Marina Teixeira Costa null (marinateixeiracosta@gmail.com) on 2017-02-14T14:27:15Z No. of bitstreams: 1 Dissertação MARINA 12.pdf: 1807218 bytes, checksum: 95bc28b832360cf51847512b47b234d8 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-02-14T15:29:56Z (GMT) No. of bitstreams: 1 costa_mt_me_bauru.pdf: 1807218 bytes, checksum: 95bc28b832360cf51847512b47b234d8 (MD5) / Made available in DSpace on 2017-02-14T15:29:56Z (GMT). No. of bitstreams: 1 costa_mt_me_bauru.pdf: 1807218 bytes, checksum: 95bc28b832360cf51847512b47b234d8 (MD5) Previous issue date: 2016-12-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O problema de Fluxo de Potência Ótimo determina a melhor condição de operação de um sistema elétrico de potência. Há diferentes classes de problemas de Fluxo de Potência Ótimo de acordo com os tipos de funções a serem otimizadas, e os conjuntos de controles e de restrições utilizados. Dentre elas, dá-se destaque ao problema de Fluxo de Potência Ótimo com Restrição de Segurança, o qual é uma importante ferramenta para os Operadores dos Sistemas de Transmissão, tanto para o planejamento operacional, quanto para a precificação da energia. Seu objetivo é minimizar os custos operacionais de geração de energia levando em consideração as restrições decorrentes da operação do sistema sob um conjunto de contingências. Ele é formulado como um problema de otimização não linear, não-convexo de grande porte, com variáveis contínuas e discretas. Neste trabalho investiga-se este problema em relação à sua formulação, dificuldades computacionais e método de solução. Para um tratamento do problema mais próximo à realidade adotam-se alguns controles como variáveis discretas, ou seja, os taps dos transformadores. Estes são tratados através de um método que penaliza a função objetivo quando as variáveis discretas assumem valores não discretos. Desta forma, o problema não linear discreto é transformado em um problema contínuo e o método Primal-Dual Barreira Logarítmica é utilizado em sua resolução. Testes computacionais são apresentados com o problema de Fluxo de Potência Ótimo com Restrição de Segurança associado ao sistema teste IEEE 14 barras em três etapas de teste. Os resultados obtidos e as comparações realizadas comprovam a eficiência do método de resolução escolhido / The Optimum Power Flow problem determines the best operating condition of an electric power system. There are different classes of Optimal Power Flow problems according to the types of functions to be optimized, and the sets of controls and constraints used. Among them, the problem of Optimal Power Flow with Security Constraint is highlighted, which is an important tool for the Transmission System operators, both for operational planning and for energy pricing. Its objective is to minimize the operational costs of power generation taking into account the constraints arising from the operation of the system under a set of contingencies. It is formulated as a nonlinear, nonconvex large optimization problem, of continuous and discrete variables. In this work, the problem in relation to its formulation, computational difficulties and solution method is investigated. For a treatment of the problem closest to the reality, some controls such as discrete variables, i.e. the taps of the transformers, are used. These are treated by a method that penalizes the objective function when the discrete variables assume non-discrete values. Thus, the discrete nonlinear problem is transformed into a continuous problem and the Primal-Dual Logarithmic Barrier method is used in its resolution. Computational tests are performed with the optimal power flow problem with security constraint associated with the test system of IEEE 14 bars in three test stages. The obtained results and the realized comparisons prove the efficiency of the chosen resolution method.

Determining the maximum power system loadability, as well as preventing the system from being operated close to the stability limits is very important in power systems planning and operation. The application of optimization techniques to power systems security and electricity markets is a rather relevant research area in power engineering. The study of optimization models to determine critical operating conditions of a power system to obtain secure power dispatches in an electricity market has gained particular attention. This thesis studies and develops optimization models and techniques to detect or avoid voltage instability points in a power system in the context of a competitive electricity market. A thorough analysis of an optimization model to determine the maximum power loadability points is first presented, demonstrating that a solution of this model corresponds to either Saddle-node Bifurcation (SNB) or Limit-induced Bifurcation (LIB) points of a power flow model. The analysis consists of showing that the transversality conditions that characterize these bifurcations can be derived from the optimality conditions at the solution of the optimization model. The study also includes a numerical comparison between the optimization and a continuation power flow method to show that these techniques converge to the same maximum loading point. It is shown that the optimization method is a very versatile technique to determine the maximum loading point, since it can be readily implemented and solved. Furthermore, this model is very flexible, as it can be reformulated to optimize different system parameters so that the loading margin is maximized. The Optimal Power Flow (OPF) problem with voltage stability (VS) constraints is a highly nonlinear optimization problem which demands robust and efficient solution techniques. Furthermore, the proper formulation of the VS constraints plays a significant role not only from the practical point of view, but also from the market/system perspective. Thus, a novel and practical OPF-based auction model is proposed that includes a VS constraint based on the singular value decomposition (SVD) of the power flow Jacobian. The newly developed model is tested using realistic systems of up to 1211 buses to demonstrate its practical application. The results show that the proposed model better represents power system security in the OPF and yields better market signals. Furthermore, the corresponding solution technique outperforms previous approaches for the same problem. Other solution techniques for this OPF problem are also investigated. One makes use of a cutting planes (CP) technique to handle the VS constraint using a primal-dual Interior-point Method (IPM) scheme. Another tries to reformulate the OPF and VS constraint as a semidefinite programming (SDP) problem, since SDP has proven to work well for certain power system optimization problems; however, it is demonstrated that this technique cannot be used to solve this particular optimization problem.

Determining the maximum power system loadability, as well as preventing the system from being operated close to the stability limits is very important in power systems planning and operation. The application of optimization techniques to power systems security and electricity markets is a rather relevant research area in power engineering. The study of optimization models to determine critical operating conditions of a power system to obtain secure power dispatches in an electricity market has gained particular attention. This thesis studies and develops optimization models and techniques to detect or avoid voltage instability points in a power system in the context of a competitive electricity market. A thorough analysis of an optimization model to determine the maximum power loadability points is first presented, demonstrating that a solution of this model corresponds to either Saddle-node Bifurcation (SNB) or Limit-induced Bifurcation (LIB) points of a power flow model. The analysis consists of showing that the transversality conditions that characterize these bifurcations can be derived from the optimality conditions at the solution of the optimization model. The study also includes a numerical comparison between the optimization and a continuation power flow method to show that these techniques converge to the same maximum loading point. It is shown that the optimization method is a very versatile technique to determine the maximum loading point, since it can be readily implemented and solved. Furthermore, this model is very flexible, as it can be reformulated to optimize different system parameters so that the loading margin is maximized. The Optimal Power Flow (OPF) problem with voltage stability (VS) constraints is a highly nonlinear optimization problem which demands robust and efficient solution techniques. Furthermore, the proper formulation of the VS constraints plays a significant role not only from the practical point of view, but also from the market/system perspective. Thus, a novel and practical OPF-based auction model is proposed that includes a VS constraint based on the singular value decomposition (SVD) of the power flow Jacobian. The newly developed model is tested using realistic systems of up to 1211 buses to demonstrate its practical application. The results show that the proposed model better represents power system security in the OPF and yields better market signals. Furthermore, the corresponding solution technique outperforms previous approaches for the same problem. Other solution techniques for this OPF problem are also investigated. One makes use of a cutting planes (CP) technique to handle the VS constraint using a primal-dual Interior-point Method (IPM) scheme. Another tries to reformulate the OPF and VS constraint as a semidefinite programming (SDP) problem, since SDP has proven to work well for certain power system optimization problems; however, it is demonstrated that this technique cannot be used to solve this particular optimization problem.

An approach to utilize Distributed Generation (DG) to minimize the total load shedding by analyzing the power system in Transactive energy framework is proposed. An algorithm to optimize power system in forward and spot markets to maximize an electric utility’s profit by optimizing purchase of power from DG is developed. The proposed algorithm is a multi-objective optimization with the main objective to maximize a utility’s profit by minimizing overall cost of production, load shedding, and purchase of power from distributed generators. This work also proposes a method to price power in forward and spot markets using existing LMP techniques. Transactive accounting has been performed to quantify the consumer payments in both markets. The algorithm is tested in two test systems; a 6-bus system and modified IEEE 14-bus system. The results show that by investing in DG, utility benefits from profit increase, load shedding reduction, and transmission line loading improvement.

Neste trabalho, as restrições de estabilidade transitória são incorporadas ao problema de Fluxo de Potência Ótimo (FPO) por meio da aproximação de equações diferenciais do problema de estabilidade por um conjunto de equações algébricas provenientes de procedimentos de integração numérica. Uma contribuição original desta dissertação é a proposição de um procedimento de otimização multi-passos que minimiza problemas de convergência e acelera o processo computacional. O procedimento de otimização proposto foi testado com sucesso num sistema pequeno de 3 geradores, tendo as potências geradas como variáveis de controle. / In this work, transient stability constraints are incorporated into the Optimal Power Flow (OPF) problem by approximating differential equations constraints by a set of equivalent algebraic equations originated from numerical integration procedures. A contribution of this dissertation is the proposal of a multi-step optimization procedure, which minimizes convergence problems and speeds up computation. The proposed optimization procedure was successfully tested on a small 3-machine power system, having the generated powers as control variables.

Neste trabalho propomos uma abordagem para a resolução do problema de fluxo de potência ótimo. Para isso, foram obtidos dados teóricos, a partir de um levantamento bibliográfico, que explicitaram os métodos de penalidade, de barreira, de Newton-Lagrangiano, da função Lagrangiana aumentada e dual-Lagrangiano. Nesta abordagem, as restrições de igualdade são tratadas pelo método de Newton, as restrições canalizadas, de tensão e tap, pelo método da função barreira logarítmica, e as restrições de desigualdade e demais restrições canalizadas, pelo método da função Lagrangiana aumentada. A motivação para este estudo foi a necessidade de manter as variáveis - tensão e tap - dentro de seus limites. Os resultados numéricos apresentados evidenciam o potencial desta metodologia para a resolução de problemas de programação não-linear e, em particular, do problema de fluxo de potência ótimo. / A new approach to solving the optimal power flow problem is proposed in this study. The first step in developing this method was to obtain theoretical material from bibliographic survey, which described in detail the penalty method, the barrier method, Newton\'s method, the augmented Lagrangian method end the dual-Lagrangian method. In the new approach, equality constraints are handled by Newton\'s method, the voltage end tap box inequality constraints by the logarithmic barrier method and the inequality constraints and the other box inequality constraints by the augmented Lagrangian method. The motivation for this research was the necessity to keep the voltage and tap variables within their limits. The numerical results demonstrate the potential of this methodology for the solution of nonlinear problems and, in particular, of the optimal power flow problem.

The focus of this thesis is formulation and development of a mathematical framework for the solution of the contingency constrained optimal power flow (OPF) based on sequential quadratic programming. The contingency constrained optimal power flow minimizes the total cost of a base case operating state as well as the expected cost of recovery from contingencies such as line or generation outages. The sequential quadratic programming (SCP) OPF formulation has been expanded in order to recognize contingency conditions and the problem is solved as a single entity by an efficient interior point method. The new formulation takes into account the system corrective capabilities in response to contingencies introduced through ramp-rate constraints. Contingency constrained OPF is a very challenging problem, because each contingency considered introduces a new problem as large as the base case problem. By proper system reduction and benefits of constraint relaxation (active set) methods, in which transmission constraints are not introduced until they are violated, the size of the system can be reduced significantly Therefore, restricting our attention to the active set constraint set makes this large problem significantly smaller and computationally feasible.