With Power System being restructured in the vision of Smart Grid, it is important now more than ever to find suitable locations to place Distributed Generators (DG). Distributed generators, which may be renewable, are not limited to specific locations as in the case of conventional generators. Several papers have been published that make suggestions on where the optimal location of DG should be in a system. Objectives ranging from loss minimization to total cost minimization have been the factor for such studies. In this study, a new method is introduced that hopes to improve a current system in three ways by maximizing load, minimizing the locational marginal price and improving line contingency scenarios. The proposed methodology is simulated using MATPOWER’s Optimal Power Flow on the IEEE 14 bus test system.

Neste trabalho abordamos o equipamento UPFC, pertencente à família FACTS, adequado ao estudo do regime permanente de sistemas de potência, sendo descritos modelos com diferentes níveis de detalhamento, assim como sua associação com a rede elétrica. Uma proposição alternativa para o modelamento do UPFC foi apresentada, sendo discutidos seus aspectos e particularidades por meio da análise nodal modificada (ANM), cujo tratamento permite obter a corrente nos conversores série de maneira direta. Desenvolvemos um programa de otimização de fluxo de potência, utilizando o método dos pontos interiores, sendo também elaborado um programa de otimização baseado no método Quase-Newton, permitindo uma análise comparativa de métodos e de modelos. Várias condições operativas de uma rede de pequeno porte, com apenas quatro barras e de uma rede de médio porte, com 39 barras, foram estudadas do ponto de vista de otimização e de limites de tensões e fluxos, observando-se a influência do UPFC no controle de variáveis da rede elétrica. / In this work we deal with the UPFC device which belongs to the FACTS family and is suitable to study the steady state of power systems. We describe models with different levels of detail, as well as their association with the electric network. One alternative proposition to the model of the UPFC is shown. The aspects and particularities are discussed by means of modified nodal analysis (ANM), which treatment allows us to get directly the current in series converters. We developed an optimization program of load flow, using the interior points method, and also worked out an optimization program based on the Quasi-Newton method, allowing a comparative analysis of methods and models. Several operating conditions of a small size network, with only 4 bars and of a medium size network, with 39 bars, were studied from the point of view of the optimization and of the voltage and of the flow limits, observing the influence of the UPFC on control of variables of the electric network.

Neste trabalho abordamos o equipamento UPFC, pertencente à família FACTS, adequado ao estudo do regime permanente de sistemas de potência, sendo descritos modelos com diferentes níveis de detalhamento, assim como sua associação com a rede elétrica. Uma proposição alternativa para o modelamento do UPFC foi apresentada, sendo discutidos seus aspectos e particularidades por meio da análise nodal modificada (ANM), cujo tratamento permite obter a corrente nos conversores série de maneira direta. Desenvolvemos um programa de otimização de fluxo de potência, utilizando o método dos pontos interiores, sendo também elaborado um programa de otimização baseado no método Quase-Newton, permitindo uma análise comparativa de métodos e de modelos. Várias condições operativas de uma rede de pequeno porte, com apenas quatro barras e de uma rede de médio porte, com 39 barras, foram estudadas do ponto de vista de otimização e de limites de tensões e fluxos, observando-se a influência do UPFC no controle de variáveis da rede elétrica. / In this work we deal with the UPFC device which belongs to the FACTS family and is suitable to study the steady state of power systems. We describe models with different levels of detail, as well as their association with the electric network. One alternative proposition to the model of the UPFC is shown. The aspects and particularities are discussed by means of modified nodal analysis (ANM), which treatment allows us to get directly the current in series converters. We developed an optimization program of load flow, using the interior points method, and also worked out an optimization program based on the Quasi-Newton method, allowing a comparative analysis of methods and models. Several operating conditions of a small size network, with only 4 bars and of a medium size network, with 39 bars, were studied from the point of view of the optimization and of the voltage and of the flow limits, observing the influence of the UPFC on control of variables of the electric network.

O objetivo de um problema de Fluxo de Potência Ótimo é determinar o estado de um sistema de transmissão de energia elétrica que otimize um dado desempenho do sistema, e satisfaça suas restrições físicas e operacionais. O problema de Fluxo de Potência Ótimo é modelado como um problema de programação não linear com variáveis discretas e contínuas. Na maioria das abordagens da literatura para a resolução de problemas de Fluxo de Potência Ótimo, os controles discretos são modelados como variáveis contínuas. Estas formulações estão longe da realidade de um sistema elétrico pois alguns controles podem somente ser ajustados por passos discretos. Este trabalho apresenta um método para tratar as variáveis discretas do problema de Fluxo de Potência Ótimo. Uma função, que penaliza a função objetivo quando as variáveis discretas assumem valores não discretos, é apresentada. Ao incorporar esta função na função objetivo, um problema de programação não linear com somente variáveis contínuas é obtido e a solução desse problema é equivalente à solução do problema original, que contém variáveis discretas e contínuas. O problema de programação não linear é resolvido pelo Método de Pontos Interiores com Filtro. Experimentos numéricos com os sistemas elétricos IEEE 14, 30, 118 e 300 Barras comprovam que a abordagem proposta é eficiente na resolução de problemas de Fluxo de Potência Ótimo. / The aim of solving the Optimal Power Flow problem is to determine the state of an electric power transmission system that optimizes a given system performance, while satisfying its physical and operating constraints. The Optimal Power Flow problem is modeled as a large-scale mixed-discrete nonlinear programming problem. In most techniques existing in the literature to solve the Optimal Power Flow problems, the discrete controls are modeled as continuous variables. These formulations are unrealistic, as some controls can be set only to values taken from a given set of discrete values. This study proposes a method for handling the discrete variables of the Optimal Power Flow problem. A function, which penalizes the objective function when discrete variables assume non-discrete values, is presented. By including this penalty function into the objective function, a nonlinear programming problem with only continuous variables is obtained and the solution of this problem is equivalent to the solution of the initial problem that contains discrete and continuous variables. The nonlinear programming problem is solved by a Interior-Point Method with filter line-search. Numerical tests using the IEEE 14, 30, 118 and 300-Bus test systems indicate that the proposed approach is efficient in the resolution of OPF problems.

O objetivo de um problema de Fluxo de Potência Ótimo é determinar o estado de um sistema de transmissão de energia elétrica que otimize um dado desempenho do sistema, e satisfaça suas restrições físicas e operacionais. O problema de Fluxo de Potência Ótimo é modelado como um problema de programação não linear com variáveis discretas e contínuas. Na maioria das abordagens da literatura para a resolução de problemas de Fluxo de Potência Ótimo, os controles discretos são modelados como variáveis contínuas. Estas formulações estão longe da realidade de um sistema elétrico pois alguns controles podem somente ser ajustados por passos discretos. Este trabalho apresenta um método para tratar as variáveis discretas do problema de Fluxo de Potência Ótimo. Uma função, que penaliza a função objetivo quando as variáveis discretas assumem valores não discretos, é apresentada. Ao incorporar esta função na função objetivo, um problema de programação não linear com somente variáveis contínuas é obtido e a solução desse problema é equivalente à solução do problema original, que contém variáveis discretas e contínuas. O problema de programação não linear é resolvido pelo Método de Pontos Interiores com Filtro. Experimentos numéricos com os sistemas elétricos IEEE 14, 30, 118 e 300 Barras comprovam que a abordagem proposta é eficiente na resolução de problemas de Fluxo de Potência Ótimo. / The aim of solving the Optimal Power Flow problem is to determine the state of an electric power transmission system that optimizes a given system performance, while satisfying its physical and operating constraints. The Optimal Power Flow problem is modeled as a large-scale mixed-discrete nonlinear programming problem. In most techniques existing in the literature to solve the Optimal Power Flow problems, the discrete controls are modeled as continuous variables. These formulations are unrealistic, as some controls can be set only to values taken from a given set of discrete values. This study proposes a method for handling the discrete variables of the Optimal Power Flow problem. A function, which penalizes the objective function when discrete variables assume non-discrete values, is presented. By including this penalty function into the objective function, a nonlinear programming problem with only continuous variables is obtained and the solution of this problem is equivalent to the solution of the initial problem that contains discrete and continuous variables. The nonlinear programming problem is solved by a Interior-Point Method with filter line-search. Numerical tests using the IEEE 14, 30, 118 and 300-Bus test systems indicate that the proposed approach is efficient in the resolution of OPF problems.

[Truncated abstract] The present thesis is devoted to the development of new methods for transient stability-constrained optimal power flow, probabilistic transient stability assessment and security-constrained ancillary services allocation. The key objective of the thesis is to develop novel dispatch and assessment methods for power systems operation in the new environment of electricity markets to ensure power systems security, particularly transient stability. A new method for economic dispatch together with nodal price calculations which includes transient stability constraints and, at the same time, optimises the reference inputs to the Flexible AC Transmission System (FACTS) devices for maintaining power systems transient stability and reducing nodal prices is developed. The method draws on the sensitivity analysis of time-domain transient stability simulation results to derive a set of linearised stability constraints expressed in terms of generator active powers and FACTS devices input references. '...' The low computing time requirement of the two-point estimate method allows online applications, and the use of detailed power systems dynamic model for time-domain simulation which offers high accuracy. The two-point estimate method is integrated in a straightforward manner with the existing transient stability analysis tools. The integrated software facility has potential applications in control rooms to assist the system operator in decision making process based on instability risks. The software system when implemented on a cluster of processors also makes it feasible to re-assess online transient stability for any change in system configuration arising from switching control. The method proposed has been tested on a representative power system and validated using the Monte Carlo simulation. In conjunction with the energy market, by which forecasted load demand is met by generator dispatch, ancillary services are required in relation to control for secure system operation and power quality. The final part of the thesis has a focus on the key aspect of allocating these ancillary services, subject to an important constraint that the dispatch of the ancillary services will not impair the system security achieved in the load dispatch. With this focus and requirement, the thesis develops a new dispatch formulation in which the network security constraints are represented in the optimal determination of generator active power schedule and allocation of ancillary services. Contingencies considered include power demand variations at individual load nodes from the values specified for the current dispatch calculation. The required changes in generator active powers to meet the new load demands are represented by additional control variables in the new dispatch formulation which augment those variables in the traditional OPF dispatch calculation. Based on the Lagrange function which includes the extended set of security constraints, the formulation derives the optimality condition to be satisfied by the dispatch solution, together with the marginal prices for individual ancillary service providers and LMPs. The effects of the security constraints are investigated and discussed. Case studies for representative power systems are presented to verify the new dispatch calculation procedure.

Optimal power flow (OPF) is the choice tool for determining the optimal operating status of the power system by managing controllable devices. The importance of the OPF approach has increased due to increasing energy prices and availability of more control devices. Existing OPF approaches exhibit shortcomings. Current OPF algorithms can be classified into (a) nonlinear programming, (b) intelligent search methods, and (c) sequential algorithms. Nonlinear programming algorithms focus on the solution of the Kuhn-Tucker conditions; they require a starting feasible solution and the model includes all constraints; these characteristics limit the robustness and efficiency of these methods. Intelligent search methods are first-order methods and are totally inefficient for large-scale systems. Traditional sequential algorithms require a starting feasible solution, a requirement that limits their robustness. Present implementations of sequential algorithms use traditional modeling that result in inefficient algorithms. The research described in this thesis has overcome the shortcomings by developing a robust and highly efficient algorithm. Robustness is defined as the ability to provide a solution for any system; the proposed approach achieves robustness by operating on suboptimal points and moving toward feasible, it stops at a suboptimal solution if an optimum does not exist. Efficiency is achieved by (a) converting the nonlinear OPF problem to a quadratic problem (b) and limiting the size of the model; the quadratic model enables fast convergence and the algorithm that identifies the active constraints, limits the size of the model by only including the active constraints. A concise description of the method is as follows: The proposed method starts from an arbitrary state which may be infeasible; model equations and system constraints are satisfied by introducing artificial mismatch variables at each bus. Mathematically this is an optimal but infeasible point. At each iteration, the artificial mismatches are reduced while the solution point maintains optimality. When mismatches reach zero, the solution becomes feasible and the optimum has been found; otherwise, the mismatch residuals are converted to load shedding and the algorithm provides a suboptimal but feasible solution. Therefore, the algorithm operates on infeasible but optimal points and moves towards feasibility. The proposed algorithm maximizes efficiency with two innovations: (a) quadratization that converts the nonlinear model to quadratic with excellent convergence properties and (b) minimization of model size by identifying active constraints, which are the only constraints included in the model. Finally sparsity technique is utilized that provide the best computational efficiency for large systems. This dissertation work demonstrates the proposed OPF algorithm using various systems up to three hundred buses and compares it with several well-known OPF software packages. The results show that the proposed algorithm converges fast and its runtime is competitive. Furthermore, the proposed method is extended to a three-phase OPF (TOPF) algorithm for unbalanced networks using the quadratized three-phase power system model. An example application of the TOPF is presented. Specifically, TOPF is utilized to address the problem of fault induced delayed voltage recovery (FIDVR) phenomena, which lead to unwanted relay operations, stalling of motors and load disruptions. This thesis presents a methodology that will optimally enhance the distribution system to mitigate/eliminate the onset of FIDVR. The time domain simulation method has been integrated with a TOPF model and a dynamic programming optimization algorithm to provide the optimal reinforcing strategy for the circuits.

The thesis use capacitors, Static Synchronous Compensator (STATCOM) and wind generator to get optimal voltage stability for twenty-four-hour dynamic load by compensating real/reactive power. In the thesis, Modified Orthogonal Particle Swarm Optimizer (MOPSO) is proposed to find the sitting and sizing of capacitors, STATCOM and wind generator, and integrate Equivalent Current Injection (ECI) algorithm to solve Optimal Power Flow (OPF) to achieve optimal voltage stability. The algorithm uses MOPSO to renew STATCOM and wind turbine sizing Gbest with multiple choices and Taguchi orthogonal array, which improves Particle Swarm Optimizer (PSO) without falling into the local optimal solution and searches optimal voltage stability of power system by load balancing equation and inequality constraints. Average Voltage Variation (AVV) and Average Voltage Drop Variation (AVDV) are proposed as objective function to calculate whole system voltage variations, and convergence test of MOPSO. The IEEE 33 Bus distribution system and Miaoli-Houlong distribution system were used for simulation to test the voltage control during peak and off-peak periods of Taipower. Compensation of real/reactive power was used to get optimal system voltage stability for each simulated case.

Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. Dispatchers are use the changed statuses of protection devices from the Supervisory Control and Data Acquisition (SCADA) system to identify the fault. To reduce the outage duration and promptly restore power services, fault section detection has to be done effectively and accurately with fault alarms. In this thesis, artificial neural networks (ANN) and Grey Relational Analysis (GRA) are used to develop the restoration schemes for emergency operation in a power system including fault section detection (FSD), restoration strategy(RS), and voltage correction(VC). The optimal power flow (OPF) is responsible for verifying the proposed schemes by off-line analysis. With a IEEE 30-Bus power system, computer simulations were conducted to show the effectiveness of the proposed restoration schemes.

In recent years, the awareness of environmental protection has made the power dispatch model no longer purely economical-oriented. This thesis proposed the application of particle swarm optimization (PSO) algorithm and interactive compromise programming method to solve the 24-hour two-objective power dispatch problem. Considering simultaneously the lowest generating cost and the lowest pollution emission, the two mutually-conflicting objectives will choose a compromised dispatch model. This thesis joined the mixed-integer programming problem of optimal power flow (MIOPF) with the dynamic economic dispatch (DED), making this dispatch solution more realistic without electrical violations; The MIOPF considers both continuous and discrete types of variables. The continuous variables are the generating unit real power output and the generator-bus voltage magnitudes; the discrete variables are the shunt capacitor banks and transformer tap setting. Simulation were run on the standard IEEE 30 Bus system. In order to avoid the PSO local optimality problem, this thesis proposed the utilization of the PSO algorithm with time-varying acceleration coefficients (PSO_TVAC) plus the local random search method (LRS), so it can quickly and effectively reach the optimal solution, without advantages of performance and accuracy of PSO. This thesis also proposed the consideration of the available transfer capability (ATC) on transmission lines of the existing dispatch model. Applying sensitivity factors to calculate each generator¡¦s available transfer capability that can be offered in the analyzed time interval, enables the creation of a new constraint. Joined with the dynamic economic dispatch problem, it will make possible that a load client wishes to raise its demand. Simultaneously taking care of the minimum cost and the limits of system security, better dispatch results could be expected.