Power systems are critical infrastructures for the society. They are therefore planned and operated to provide a reliable eletricity delivery. The set of tools and methods to do so are gathered under security management and are designed to ensure that all operating constraints are fulfilled at all times. During the past decade, raising awareness about issues such as climate change, depletion of fossil fuels and energy security has triggered large investments in wind power. The limited predictability of wind power, in the form of forecast errors, pose a number of challenges for integrating wind power in power systems. This limited predictability increases the uncertainty already existing in power systems in the form of random occurrences of contingencies and load forecast errors. It is widely acknowledged that this added uncertainty due to wind power and other variable renewable energy sources will require new tools for security management as the penetration levels of these energy sources become significant. In this thesis, a set of tools for security management under uncertainty is developed. The key novelty in the proposed tools is that they build upon probabilistic descriptions, in terms of distribution functions, of the uncertainty. By considering the distribution functions of the uncertainty, the proposed tools can consider all possible future operating conditions captured in the probabilistic forecasts, as well as the likeliness of these operating conditions. By contrast, today's tools are based on the deterministic N-1 criterion that only considers one future operating condition and disregards its likelihood. Given a list of contingencies selected by the system operator and probabilitistic forecasts for the load and wind power, an operating risk is defined in this thesis as the sum of the probabilities of the pre- and post-contingency violations of the operating constraints, weighted by the probability of occurrence of the contingencies. For security assessment, this thesis proposes efficient Monte-Carlo methods to estimate the operating risk. Importance sampling is used to substantially reduce the computational time. In addition, sample-free analytical approximations are developed to quickly estimate the operating risk. For security enhancement, the analytical approximations are further embedded in an optimization problem that aims at obtaining the cheapest generation re-dispatch that ensures that the operating risk remains below a certain threshold. The proposed tools build upon approximations, developed in this thesis, of the stable feasible domain where all operating constraints are fulfilled. / <p>QC 20150508</p>

An increased penetration of renewable energy into the electric power grid is desirable from an environmental standpoint as well as an economical one. However, renewable sources such as wind and solar energy are often variable and intermittent, and additionally, are non-dispatchable. Also, the locations with the highest amount of available wind or solar may be located in areas that are far from areas with high levels of demand, and these areas may be under the control of separate, individual entities. In this dissertation, a method that coordinates these areas, accounts for the variability and intermittency, reduces the impact of renewable energy forecast errors, and increases the overall social benefit in the system is developed. The approach for the purpose of integrating intermittent energy sources into the electric power grid is considered from both the planning and operations stages. In the planning stage, two-stage stochastic optimization is employed to find the optimal size and location for a storage device in a transmission system with the goal of reducing generation costs, increasing the penetration of wind energy, alleviating line congestions, and decreasing the impact of errors in wind forecasts. The size of this problem grows dramatically with respect to the number of variables and constraints considered. Thus, a scenario reduction approach is developed which makes this stochastic problem computationally feasible. This scenario reduction technique is derived from observations about the relationship between the variance of locational marginal prices corresponding to the power balance equations and the optimal storage size. Additionally, a probabilistic, or chance, constrained model predictive control (MPC) problem is formulated to take into account wind forecast errors in the optimal storage sizing problem. A probability distribution of wind forecast errors is formed and incorporated into the original storage sizing problem. An analytical form of this constraint is derived to directly solve the optimization problem without having to use Monte-Carlo simulations or other techniques that sample the probability distribution of forecast errors. In the operations stage, a MPC AC Optimal Power Flow problem is decomposed with respect to physical control areas. Each area performs an independent optimization and variable values on the border buses between areas are exchanged at each Newton-Raphson iteration. Two modifications to the Approximate Newton Directions (AND) method are presented and used to solve the distributed MPC optimization problem, both with the intention of improving the original AND method by improving upon the convergence rate. Methods are developed to account for numerical difficulties encountered by these formula- tions, specifically with regards to Jacobian singularities introduced due to the intertemporal constraints. Simulation results show convergence of the decomposed optimization problem to the centralized result, demonstrating the benefits of coordinating control areas in the IEEE 57- bus test system. The benefit of energy storage in MPC formulations is also demonstrated in the simulations, reducing the impact of the fluctuations in the power supply introduced by intermittent sources by coordinating resources across control areas. An overall reduction of generation costs and increase in renewable penetration in the system is observed, with promising results to effectively and efficiently integrate renewable resources into the electric power grid on a large scale.

Made available in DSpace on 2014-06-11T19:30:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-07-21Bitstream added on 2014-06-13T19:00:51Z : No. of bitstreams: 1 amorim_ea_dr_ilha.pdf: 1200042 bytes, checksum: 598a8d060889d642964ac8c022c167e1 (MD5) / Esta pesquisa tem por objetivo o desenvolvimento de uma ferramenta computacional para a solução do problema de Fluxo de Potência Ótimo Multimercado (FPOM). O problema de fluxo de potência ótimo mutimercado é decomposto em vários subproblemas, uma para cada, submercado que compõe o sistema de potência interconectado. O modelo de decomposição utilizado permite resolver o problema de FPO considerando-se os modelos de mercado desverticalizados e centralizados e os desverticalizados e descentralizados. Neste contexto, a pesquisa desenvolvida considera o novo esquema de funcionamento dos mercados de energia elétrica, no qual é vi freqüentemente desejável preservar a autonomia de cada um dos submercados que compõem o sistema de potência multimercado. O problema de FPO proposto é modelado como um problema de otimização não-linear inteiro misto, com variáveis de controle contínuas e discretas e têm ênfase no despacho econômico da geração de potência ativa e nos ajustes dos controles de tensão. Além disso, este modelo de FPO trata os subproblemas ativo e reativo simultaneamente. Para a sua solução é apresentado um algoritmo evolutivo multiobjetivo, baseado no NSGA (Nondominated Sorting Genetic Algorithm), pois características do problema abordado dificultam a sua solução através das técnicas baseadas em programação matemática e justificam a escolha da metaheurística multiobjetivo. / This research is aimed at developing a computational tool for the solution of the Multimarket Optimal Power Flow (MOPF) problem. The multimarket optimal power flow problem is decomposed in various subproblems, one for each submarket that is part of the interconnected power system. The decomposition model used here allows solving the OPF problem considering the deregulated and centralized, and the deregulated and decentralized market models. In this context, the developed research takes into account the new functioning scheme of the electric power markets, viii where it is frequently desirable to preserve the autonomy of each one of those submarkets that compose the multimarket power system. The proposed OPF problem is modeled as a mixed integer non-linear optimization problem with continuous and discrete control variables, emphasizing the economic dispatch of the active power generation and the voltage control adjustments. In addition, this model of OPF deals simultaneously with the active and reactive subproblems. For its solution, a multiobjective evolutionary algorithm based on the NSGA (Nondominated Sorting Genetic Algorithm) is presented. The characteristics of the problem make difficult the utilization of techniques based on mathematical programming, justifying the adoption of a multiobjective metaheuristic.

Orientador: José Roberto Sanches Mantovani / Resumo: Neste trabalho é proposto o desenvolvimento de uma ferramenta computacional para o planeja-mento e despacho ótimo de fontes de potência ativa, considerando as incertezas das cargas (le-ve, nominal e pesada) e fontes de energia renováveis não despacháveis através de uma aborda-gem probabilística. O modelo matemático é um problema de programação não linear inteiro misto, multiobjetivo, não convexo e probabilístico na sua forma original sem a necessidade de realizar qualquer tipo de simplificação ou linearização tanto das funções objetivo como das res-trições. Um algoritmo baseado na meta-heurística Non-dominated Sorting Genetic Algorithm (NSGA-II) é proposto para resolver o problema de maneira eficaz. Os resultados obtidos com as simulações realizadas usando a implementação computacional nos sistemas de testes IEEE30 barras e IEEE118 barras mostram a eficiência e robustez da metodologia proposta. / Abstract: This work proposes the development of a computational tool for the planning and optimal dispatch of active power sources, considering the uncertainties of the loads (light, nominal and heavy) and non-dispatchable renewable energy sources through a probabilistic approach. The mathematical model is a multi-objective mixed-integer nonlinear programing problem, that is nonconvex and probabilistic in its original form, without the need to perform any kind of simplification or linearization of both objective functions and constraints. An algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) meta-heuristic is pro-posed to solve the problem effectively. The results obtained with the simulations performed using the computational implementation in the IEEE30 bus and IEEE118 bus test systems show the efficiency and robustness of the proposed methodology. / Doutor

This thesis considers three separate but connected problems regarding energy networks: the load flow problem, the optimal power flow problem, and the islanding problem. All three problems are non-convex non linear problems, and so have the potential of returning local solutions. The goal of this thesis is to find solution methods to each of these problems that will minimize the chances of returning a local solution. The thesis first considers the load ow problem and looks into a novel approach to solving load flows, the Holomorphic Embedding Load Flow Method (HELM). The current literature does not provide any HELM models that can accurately handle general power networks containing PV and PQ buses of realistic sizes. This thesis expands upon previous work to present models of HELM capable of solving general networks efficiently, with computational results for the standard IEEE test cases provided for comparison. The thesis next considers the optimal power flow problem, and creates a framework for a load flow-based OPF solver. The OPF solver is designed with incorporating HELM as the load flow solver in mind, and is tested on IEEE test cases to compare it with other available OPF solvers. The OPF solvers are also tested with modified test cases known to have local solutions to show how a LF-OPF solver using HELM is more likely to find the global optimal solution than the other available OPF solvers. The thesis finally investigates solving a full AC-islanding problem, which can be considered as an extension of the transmission switching problem, using a standard MINLP solver and comparing the results to solutions obtained from approximations to the AC problem. Analysing in detail the results of the AC-islanding problem, alterations are made to the standard MINLP solver to allow better results to be obtained, all the while considering the trade-off between results and elapsed time.

AN ABSTRACT OF THE THESIS OF Mohamed A. Abuella, for the Master of Science degree in Electrical and Computer Engineering, presented on May 10, 2012, at Southern Illinois University Carbondale. TITLE:STUDY OF PARTICLE SWARM FOR OPTIMAL POWER FLOW IN IEEE BENCHMARK SYSTEMS INCLUDING WIND POWER GENERATORS MAJOR PROFESSOR: Dr. C. Hatziadoniu, The aim of this thesis is the optimal economic dispatch of real power in systems that include wind power. The economic dispatch of wind power units is quite different of conventional thermal units. In addition, the consideration should take the intermittency nature of wind speed and operating constraints as well. Therefore, this thesis uses a model that considers the aforementioned considerations in addition to whether the utility owns wind turbines or not. The optimal power flow (OPF) is solved by using one of the modern optimization algorithms: the particle swarm optimization algorithm (PSO). IEEE 30-bus test system has been adapted to study the implementation PSO algorithm in OPF of conventional-thermal generators. A small and simple 6-bus system has been used to study OPF of a system that includes wind-powered generators besides to thermal generators. The analysis of investigations on power systems is presented in tabulated and illustrative methods to lead to clear conclusions.

[pt] O sistema elétrico é planejado para operar na condição mais confiável possível de atendimento, sem que sejam violadas as restrições operativas a que está sujeito. Para a busca da região segura de operação utilizam-se vários programas de otimização e simulação. O Fluxo de Potência Ótimo (FPO) é uma ferramenta muito útil para a busca do ponto de operação seguro. Mas, devido a sua complexidade computacional para solução das restrições não-lineares, possui resposta relativamente lenta, impossibilitando a sua utilização em diversos casos práticos, tanto em tempo real, onde existe a necessidade de rapidez de resposta, como no planejamento da operação, quando é necessário um grande número de execuções do FPO. Em contrapartida, o Fluxo de Potência Ótimo Linearizado (FPO DC), devido a sua simplicidade computacional, possui grande rapidez de resposta. Mas, nas suas aproximações não são consideradas as restrições reativas, o que pode levar a uma solução em que estas sejam violadas. Este trabalho apresenta uma metodologia que incorpora ao Fluxo de Potência Ótimo Linearizado restrições reativas em função da potência ativa do geradores, de tal forma que a solução fornecida por este modelo leve em consideração as restrições de tensão e potência reativa. / [en] The electrical network is planned to stay in the most reliable operation conditions, without any violated operation constraints. Many optimization and simulation models are used to define the safe operation region. The Optimal Power Flow (OPF) is a useful tool for the search of the best safe operation point. However, the computational complexity associated to its non-linear constraints implies in a heavy computation time and makes it difficult to be used whenever a fast response is required, as in on-line oporation or even most operation planning problems. On the other hand, the Linearized Optimal Power Flow (LOPF), due to its simplicity, is a very fast tool. However, it does not consider reactive (voltage) constraints, and is not able to detect any violation. This work presents a model to incorporate voltage constraints in the Linearized Power Flow.

Orientador: Prof. Dr. Edmarcio Antônio Belati / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Elétrica, 2014. / Esse trabalho apresenta uma análise da introdução de controladores FACTS no sistema elétrico através do fluxo de potência ótimo. O fluxo de potência ótimo foi modelado utilizando as ferramentas AMPL e o solver Knitro. Nos estudos feitos buscouse determinar a quantidade de reativos e também o ponto ótimo de instalação desses dispositivos. Para realizar a análise, os estudos foram feitos em três cenários de carga diferentes e com três funções objetivos diferentes. Com análises feitas nos sistemas IEEE 14 e 118 barras, foi possível notar uma melhora no sistema quando temos os controladores FACTS conectados. Observou-se que o ponto ideal de instalação de fonte de reativos depende da função objetivo e da carga do sistema. Com isso, demonstra-se que a introdução desse tipo de dispositivo pode aumentar a capacidade do sistema elétrico, suprindo o aumento da demanda de potência reativa. / This study presents an analysis of the introduction of different FACTS controllers in the electrical system by optimal power flow. For the analysis we used an optimal power flow in the system using the tools AMPL and solver Knitro. In studies of the electrical system were made seeking not only determine the amount of reactive, but also determine the optimum installation location of these devices. To perform the analysis studies were performed in three different load scenarios and with three different objectives functions. With analyzes the IEEE systems 14 and 118 bars, it was possible to notice an improvement in the system when we have the FACTS controllers connected. It was observed that the ideal location of reactive depends of the objective function and system load. Thus it is shown that the introduction of this type of device can be made to meet the increased demand of reactive power.

Submitted by ELAYNNE XAVIER SOUZA ARAÚJO null (elaynnearaujo@hotmail.com) on 2018-03-13T18:51:38Z No. of bitstreams: 1 Tese_Final.pdf: 5331631 bytes, checksum: 60e1011da397d7e88cc9d80319169d76 (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-03-14T12:06:56Z (GMT) No. of bitstreams: 1 araujo_exs_dr_ilha.pdf: 5331631 bytes, checksum: 60e1011da397d7e88cc9d80319169d76 (MD5) / Made available in DSpace on 2018-03-14T12:06:56Z (GMT). No. of bitstreams: 1 araujo_exs_dr_ilha.pdf: 5331631 bytes, checksum: 60e1011da397d7e88cc9d80319169d76 (MD5) Previous issue date: 2018-02-23 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Neste trabalho é proposto o desenvolvimento de uma ferramenta computacional para o planeja-mento e despacho ótimo de fontes de potência ativa, considerando as incertezas das cargas (le-ve, nominal e pesada) e fontes de energia renováveis não despacháveis através de uma aborda-gem probabilística. O modelo matemático é um problema de programação não linear inteiro misto, multiobjetivo, não convexo e probabilístico na sua forma original sem a necessidade de realizar qualquer tipo de simplificação ou linearização tanto das funções objetivo como das res-trições. Um algoritmo baseado na meta-heurística Non-dominated Sorting Genetic Algorithm (NSGA-II) é proposto para resolver o problema de maneira eficaz. Os resultados obtidos com as simulações realizadas usando a implementação computacional nos sistemas de testes IEEE30 barras e IEEE118 barras mostram a eficiência e robustez da metodologia proposta. / This work proposes the development of a computational tool for the planning and optimal dispatch of active power sources, considering the uncertainties of the loads (light, nominal and heavy) and non-dispatchable renewable energy sources through a probabilistic approach. The mathematical model is a multi-objective mixed-integer nonlinear programing problem, that is nonconvex and probabilistic in its original form, without the need to perform any kind of simplification or linearization of both objective functions and constraints. An algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) meta-heuristic is pro-posed to solve the problem effectively. The results obtained with the simulations performed using the computational implementation in the IEEE30 bus and IEEE118 bus test systems show the efficiency and robustness of the proposed methodology. / 167761/2014-5

abstract: Due to great challenges from aggressive environmental regulations, increased demand due to new technologies and the integration of renewable energy sources, the energy industry may radically change the way the power system is operated and designed. With the motivation of studying and planning the future power system under these new challenges, the development of the new tools is required. A network equivalent that can be used in such planning tools needs to be generated based on an accurate power flow model and an equivalencing procedure that preserves the key characteristics of the original system. Considering the pervasive use of the dc power flow models, their accuracy is of great concern. The industry seems to be sanguine about the performance of dc power flow models, but recent research has shown that the performance of different formulations is highly variable. In this thesis, several dc power-flow models are analyzed theoretically and evaluated numerically in IEEE 118-bus system and Eastern Interconnection 62,000-bus system. As shown in the numerical example, the alpha-matching dc power flow model performs best in matching the original ac power flow solution. Also, the possibility of applying these dc models in the various applications has been explored and demonstrated. Furthermore, a novel hot-start optimal dc power-flow model based on ac power transfer distribution factors (PTDFs) is proposed, implemented and tested. This optimal-reactance-only dc model not only matches the original ac PF solution well, but also preserves the congestion pattern obtain from the OPF results of the original ac model. Three improved strategies were proposed for applying the bus-aggregation technique to the large-scale systems, like EI and ERCOT, to improve the execution time, and memory requirements when building a reduced equivalent model. Speed improvements of up to a factor of 200 were observed. / Dissertation/Thesis / M.S. Engineering 2013