The conceptual evolution of smart cities is highly motivated by the advancement of information and communication technologies (ICTs). The purpose of a smart city is to facilitate the best quality of life to its inhabitants. Its implementation has to be supported by the compliant utilities and networked infrastructures. In the current world, it can only be achieved by applying ICTs in an extensive manner. The move towards the smart city's seamless connectivity widens the scope of cyber security concerns. Smart city infrastructures to face a high risk of targeted attacks due to extended cyber-physical vulnerabilities. This creates many challenging research issues relevant to the design and implementation of cyber security solutions. Networks associated with city infrastructures vary from a small indoor one to a large geographically distributed one. The context of a network is an essential consideration for security solutions. This thesis investigates a set of optimal security placement problems for enhancing monitoring in smart city infrastructures. It develops solutions to such placement problems from a resource management perspective. Economy and quality-of-security service (QoSS) are two major design goals. Such goals are translated into three basic performance metrics: (i) coverage, (ii) tolerance, and (iii) latency. This thesis studies security placement problems pertaining to three different types of networks: (i) wireless sensor network (WSN), (ii) supervisory control and data acquisition (SCADA) backbone, and (iii) advanced metering infrastructure (AMI) wide area network (WAN). In a smart city, WSNs are deployed to support real time monitoring and safety alert (RTMSA) applications. They are highly resource constrained networks. For WSNs, placement problems for an internally configured security monitor named watchdog have been studied. On the other hand, a smart grid is a key driver for smart cities. SCADA and AMI are two major components of a smart grid. They are associated with two different types of geographically distributed networks. For SCADA backbones, placement problems for a specially designed security device named trust system have been studied. For AMI-WANs, placement problems for a cloud-based managed security service have been studied. This thesis proposes a number of promising solution schemes to such placement problems. It includes evaluation results that demonstrate the enhancements of the proposed schemes.

<div>The problem of optimally placing sensors can often be formulated as a facility location problem. In the literature of operations research, facility location problems are mathematical optimization problems where one or more facilities must be placed in relation to a given number of demand points or customers. Within the context of sensor placement, for example, this translates to placing wireless communication nodes that connect to a set of users or placing smoke detectors to adequately cover a region for safety assurances. However, while the classical facility location problem has been extensively studied, its direct applicability to and effectiveness for the optimal sensor placement problem can be diminished when real-world uncertainties are considered. In addition, the physics of the underlying systems in optimal sensor placement problems can directly impact the effectiveness of facility location formulations. Extensions to existing location formulations that are tailored for the system of interest are necessary to ensure optimal sensor network design.</div><div><br></div><div>This dissertation focuses on developing and applying problem-specific optimal sensor placement methods under uncertainty in sensor performance. With the classical discrete facility location problems as a basis, our models are formulated as mixed-integer linear and nonlinear programs that, depending on the specific application, can also be in the form of a stochastic program, a robust optimization framework, or require probability distributions for uncertain parameters. We consider optimal placement problems from three different areas, particularly the optimal placement of data concentrators in Smart Grid communications networks, the optimal placement of flame detectors within petrochemical facilities, and the optimal selection of infectious disease detection sites across a nation. For each application, we carefully consider the underlying physics of the system and the uncertainties and then develop extensions of previous sensor placement formulations that effectively handle these qualities. In addition, depending on the degree of nonlinear complexity of the problem, specific relaxations and iterative solution strategies are developed to improve the ability to find tractable solutions. All proposed models are implemented in Pyomo, a Python-based optimization modeling language, and solved with state-of-the-art optimization solvers, including IPOPT, Gurobi, and BARON for nonlinear, mixed-integer, and mixed-integer nonlinear programs, respectively. Numerical results show that our tailored formulations for the problems of interest are effective in handling uncertainties and provide valuable sensor placement design frameworks for their respective industries. Furthermore, our extensions for placement of sensors under probabilistic failure are appropriately general for application in other areas.<br><b></b><i></i><u></u><br></div>

State estimation is widely used as a tool to evaluate the real time power system prevailing conditions. State estimation algorithms could suffer divergence under stressed system conditions. This dissertation first investigates impacts of variations of load levels and topology errors on the convergence property of the commonly used weighted least square (WLS) state estimator. The influence of topology errors on the condition number of the gain matrix in the state estimator is also analyzed. The minimum singular value of gain matrix is proposed to measure the distance between the operating point and state estimation divergence. To study the impact of the load increment on the convergence property of WLS state estimator, two types of load increment are utilized: one is the load increment of all load buses, and the other is a single load increment. In addition, phasor measurement unit (PMU) measurements are applied in state estimation to verify if they could solve the divergence problem and improve state estimation accuracy. The dissertation investigates the impacts of variations of line power flow increment and topology errors on convergence property of the WLS state estimator. A simple 3-bus system and the IEEE 118-bus system are used as the test cases to verify the common rule. Furthermore, the simulation results show that adding PMU measurements could generally improve the robustness of state estimation. Two new approaches for improving the robustness of the state estimation with PMU measurements are proposed. One is the equality-constrained state estimation with PMU measurements, and the other is Hachtel's matrix state estimation with PMU measurements approach. The dissertation also proposed a new heuristic approach for optimal placement of phasor measurement units (PMUs) in power system for improving state estimation accuracy. In the problem of adding PMU measurements into the estimator, two methods are investigated. Method I is to mix PMU measurements with conventional measurements in the estimator, and method II is to add PMU measurements through a post-processing step. These two methods can achieve very similar state estimation results, but method II is a more time-efficient approach which does not modify the existing state estimation software.

The pressing needs for cost effective electric power that provides both high reliability and high quality is creating an opportunity for alternative energy distributed generation (DG). To determine the economic and technical feasibility of such alternative energy distributed generation facilities, electric power customers must understand their electric usage patterns, economic considerations, local alternative fuel supplies and available DG technologies. This thesis discusses the economic and technical feasibility of establishing a distributed generation installation. As a part of technical feasibility, an evaluation has been done to compare DG size and location impact on the operation of the I 13 node test distribution systems. This evaluation was carried out by performing the distribution power flow that provides the information about voltage profile, losses in the system and feeder power factor. This information was used to determine the optimal location of DG in the test distribution system. Additionally, this part focuses on the importance of power utilization assessment in distributed generation planning. It also discussed the load utilization assessment that focus on step-by-step analysis of load profiles of different facilities such as Choctaw Laundromat, Choctaw Geyser Falls (water park) and Golden Moon Casino. The second part of this thesis?s work resulted in an informative and useful economic analysis tool, DG-ECON with which the user can document the study results and analyze them for economic feasibility with minimal effort. The economic feasibility of a biomass-based renewable energy installation is clearly shown by developing a user interface spreadsheet in Microsoft Excel. The spreadsheet calculates project-screening information in the form of a 20-year life cycle cost analysis. This cost analysis that enables users to define projects that are most energy efficient and offer the greatest financial benefit. The emphasis is on the user interface features of the application to make the application as user friendly as possible. The application has both numerical and graphical data representation using some of the features of Microsoft Visual Basic.

No description available.

The world of power systems is ever changing; ever evolving. One such evolution was the advent of Phasor Measurement Units (PMUs). With the introduction of PMUs in the field, power system monitoring and control changed for the better. Innovative and efficient algorithms that used synchrophasors came to be written. To make these algorithms robust, it became necessary to remove errors that crept into the power system with time and usage. Thus the process of calibration became essential when practical decisions started being made based on PMU measurements. In the context of this thesis ‘calibration’ is the method used to estimate a correction factor which, when multiplied with the respective measurement, negates the effect of any errors that might have crept into them due to the instrument transformers located at the inputs of a PMU or the PMU device itself. Though this thesis mainly deals with the calibration of instrument transformers, work has been done previously for calibrating other components of a power system. A brief description of those methods have been provided along with a history on instrument transformer calibration. Three new methodologies for instrument transformer calibration have been discussed in details in this thesis. The first method describes how only voltage transformers can be calibrated by placing optimal number of good quality voltage measurements at strategic locations in the grid, in presence of ratio errors in the instrument transformers and Gaussian errors in the PMUs. The second method provides a way to calibrate all instrument transformers (both current and voltage) in presence of only one good quality voltage measurement located at the end of a tie-line. This method assumes that all the instrument transformers have ratio errors and the PMUs have quantization errors. The third method attains the same objective as the second one, with the additional constraint that the data obtained from the field may be contaminated. Thus, the third method shows how calibration of all the instrument transformers can be done with data that is intermittent and is therefore, the most practical approach (of the three) for instrument transformer calibration. / Master of Science

Due to power system deregulations, transmission expansion not keeping up with the load growth, and higher frequency of natural hazards resulting from climate change, major blackouts are becoming more frequent and are spreading over larger regions, entailing higher losses and costs to the economy and the society of many countries in the world. Large-scale blackouts typically result from cascading failure originating from a local event, as typified by the 2003 U.S.-Canada blackout. Their mitigation in power system planning calls for the development of methods and algorithms that assess the risk of cascading failures due to relay over-tripping, short-circuits induced by overgrown vegetation, voltage sags, line and transformer overloading, transient instabilities, voltage collapse, to cite a few. How to control the economic losses of blackouts is gaining a lot of attention among power researchers. In this research work, we develop new Monte Carlo methods and algorithms that assess and manage the risk of cascading failure in composite reliability of deregulated power systems. To reduce the large computational burden involved by the simulations, we make use of importance sampling techniques utilizing the Weibull distribution when modeling power generator outages. Another computing time reduction is achieved by applying importance sampling together with antithetic variates. It is shown that both methods noticeably reduce the number of samples that need to be investigated while maintaining the accuracy of the results at a desirable level. With the advent of microgrids, the assessment of their benefits in power systems is becoming a prominent research topic. In this research work, we investigate their potential positive impact on power system reliability while performing an optimal coordination among three energy sources within microgrids, namely renewable energy conversion, energy storage and micro-turbine generation. This coordination is modeled when applying sequential Monte Carlo simulations, which seek the best placement and sizing of microgrids in composite reliability of a deregulated power system that minimize the risk of cascading failure leading to blackouts subject to fixed investment budget. The performance of the approach is evaluated on the Roy Billinton Test System (RBTS) and the IEEE Reliability Test System (RTS). Simulation results show that in both power systems, microgrids contribute to the improvement of system reliability and the decrease of the risk of cascading failure. / Ph. D.

Sistemas de Distribuição de Energia Elétrica (SDEE) estão, pelas próprias condições de funcionamento, sujeitos a perdas de energia e quedas nos níveis de tensão. Uma das formas de reduzir as perdas e ao mesmo tempo melhorar o perfil de tensão do SDEE consiste na utilização de bancos de capacitores e reguladores de tensão ao longo dos alimentadores. Para isto a alocação ótima destes equipamentos torna-se de fundamental importância, logo este trabalho tem por objetivo a utilização de um método quadrático de programação por metas multiobjetivo para a ótima alocação desses equipamentos visando a redução de perdas (meta 1) com o menor custo de investimento (meta 2) sujeito a restrições do fluxo de potência, limites técnicos de operação, mudança no controle dos capacitores chaveados e dos taps dos reguladores de tensão. O constante aumento na penetração da geração distribuída traz novos desafios para o controle e a operação dos SDEE. A metodologia proposta considera os efeitos destas fontes de geração e os seus impactos na alocações ótima dos bancos de capacitores e reguladores de tensão. Com o intuito de resolver o problema de otimização em programas comerciais como Gurobi e Cplex uma metodologia linear de cálculo de fluxo de potência é utilizada. Os algoritmos foram desenvolvidos no software Matlab, e posteriormente foi realizada a conversão para linguagem GAMS (Generic Algebraic Modeling System) para utilização dos solvers na plataforma NEOS (Network Enabled Optimization System). / Power distribution systems (DS) are susceptible to energy loss and voltage drops, mainly because of the way that they are operated and built. One way to minimize system loss and improve the voltage profile of DS is with the use of capacitor banks and voltage regulators (VRs) along the distribution feeders. In this scenario, the optimal placement of these devices becomes crucial. In this paper, a quadratic multi objective goal programming method is proposed to obtain the optimal placement of these devices, where the goals are energy loss (goal 1) and the total investment cost (goal 2). The model is subject to system operation constraints, such as power flow demands, limits of voltages and currents, number of stages of switched capacitors and the tap control of VRs. The increase of distributed generation penetration brings new challenges for control and operation of DS. The proposed methodology considers the effects of these power sources and their impact on optimal allocations of capacitors and VRs. With the aim of solving the optimization problem in commercial programs, a linear power flow is used. The algorithms were developed with Matlab software and the conversion was made to a General Algebraic Modeling System (GAMS) language and solved in the NEOS (Network Enabled Optimization System) platform.

Sistemas de Distribuição de Energia Elétrica (SDEE) estão, pelas próprias condições de funcionamento, sujeitos a perdas de energia e quedas nos níveis de tensão. Uma das formas de reduzir as perdas e ao mesmo tempo melhorar o perfil de tensão do SDEE consiste na utilização de bancos de capacitores e reguladores de tensão ao longo dos alimentadores. Para isto a alocação ótima destes equipamentos torna-se de fundamental importância, logo este trabalho tem por objetivo a utilização de um método quadrático de programação por metas multiobjetivo para a ótima alocação desses equipamentos visando a redução de perdas (meta 1) com o menor custo de investimento (meta 2) sujeito a restrições do fluxo de potência, limites técnicos de operação, mudança no controle dos capacitores chaveados e dos taps dos reguladores de tensão. O constante aumento na penetração da geração distribuída traz novos desafios para o controle e a operação dos SDEE. A metodologia proposta considera os efeitos destas fontes de geração e os seus impactos na alocações ótima dos bancos de capacitores e reguladores de tensão. Com o intuito de resolver o problema de otimização em programas comerciais como Gurobi e Cplex uma metodologia linear de cálculo de fluxo de potência é utilizada. Os algoritmos foram desenvolvidos no software Matlab, e posteriormente foi realizada a conversão para linguagem GAMS (Generic Algebraic Modeling System) para utilização dos solvers na plataforma NEOS (Network Enabled Optimization System). / Power distribution systems (DS) are susceptible to energy loss and voltage drops, mainly because of the way that they are operated and built. One way to minimize system loss and improve the voltage profile of DS is with the use of capacitor banks and voltage regulators (VRs) along the distribution feeders. In this scenario, the optimal placement of these devices becomes crucial. In this paper, a quadratic multi objective goal programming method is proposed to obtain the optimal placement of these devices, where the goals are energy loss (goal 1) and the total investment cost (goal 2). The model is subject to system operation constraints, such as power flow demands, limits of voltages and currents, number of stages of switched capacitors and the tap control of VRs. The increase of distributed generation penetration brings new challenges for control and operation of DS. The proposed methodology considers the effects of these power sources and their impact on optimal allocations of capacitors and VRs. With the aim of solving the optimization problem in commercial programs, a linear power flow is used. The algorithms were developed with Matlab software and the conversion was made to a General Algebraic Modeling System (GAMS) language and solved in the NEOS (Network Enabled Optimization System) platform.

Sistemas de Distribuição de Energia Elétrica (SDEE) estão, pelas próprias condições de funcionamento, sujeitos a perdas de energia e quedas nos níveis de tensão. Uma das formas de reduzir as perdas e ao mesmo tempo melhorar o perfil de tensão do SDEE consiste na utilização de bancos de capacitores e reguladores de tensão ao longo dos alimentadores. Para isto a alocação ótima destes equipamentos torna-se de fundamental importância, logo este trabalho tem por objetivo a utilização de um método quadrático de programação por metas multiobjetivo para a ótima alocação desses equipamentos visando a redução de perdas (meta 1) com o menor custo de investimento (meta 2) sujeito a restrições do fluxo de potência, limites técnicos de operação, mudança no controle dos capacitores chaveados e dos taps dos reguladores de tensão. O constante aumento na penetração da geração distribuída traz novos desafios para o controle e a operação dos SDEE. A metodologia proposta considera os efeitos destas fontes de geração e os seus impactos na alocações ótima dos bancos de capacitores e reguladores de tensão. Com o intuito de resolver o problema de otimização em programas comerciais como Gurobi e Cplex uma metodologia linear de cálculo de fluxo de potência é utilizada. Os algoritmos foram desenvolvidos no software Matlab, e posteriormente foi realizada a conversão para linguagem GAMS (Generic Algebraic Modeling System) para utilização dos solvers na plataforma NEOS (Network Enabled Optimization System). / Power distribution systems (DS) are susceptible to energy loss and voltage drops, mainly because of the way that they are operated and built. One way to minimize system loss and improve the voltage profile of DS is with the use of capacitor banks and voltage regulators (VRs) along the distribution feeders. In this scenario, the optimal placement of these devices becomes crucial. In this paper, a quadratic multi objective goal programming method is proposed to obtain the optimal placement of these devices, where the goals are energy loss (goal 1) and the total investment cost (goal 2). The model is subject to system operation constraints, such as power flow demands, limits of voltages and currents, number of stages of switched capacitors and the tap control of VRs. The increase of distributed generation penetration brings new challenges for control and operation of DS. The proposed methodology considers the effects of these power sources and their impact on optimal allocations of capacitors and VRs. With the aim of solving the optimization problem in commercial programs, a linear power flow is used. The algorithms were developed with Matlab software and the conversion was made to a General Algebraic Modeling System (GAMS) language and solved in the NEOS (Network Enabled Optimization System) platform.