[pt] Para simular, prever e controlar os sistemas de energia elétrica, engenheiros precisam de ferramentas computacionais para modelar os componentes dessa rede interconectada altamente complexa. Muitos esforços ao longo do século passado foram dedicados a desenvolver modelos matemáticos para geradores, linhas de transmissão, compensadores de potência reativa, transformadores e assim por diante. Os principais componentes dos sistemas de potência são representados precisamente através de modelos matemáticos, mas as cargas ainda são uma fonte de incerteza nas simulações, devido à sua característica de aleatoriedade. Modelos de carga conservadores superestimam a resposta de potência a desvios de tensão, enquanto modelos de carga excessivamente otimistas podem subestimar as margens de estabilidade, deixando o sistema muito próximo do seu limite operacional. É preciso estabelecer representações de cargas tão próximas da realidade quanto possível, a fim de explorar os recursos de rede de modo mais eficiente. Este trabalho fornece uma metodologia para modelagem de carga, investigando e resumindo as etapas do processo, que podem ser implementadas de diversas maneiras. O tratamento de dados, a escolha de uma representação matemática do modelo de carga e sua estimação de parâmetros são apresentados através de estudos de caso reais, tanto para uma aplicação focada na dinâmica do sistema elétrica, quanto para uma aplicação em regime permanente. Discute-se como otimização e conceitos de inferência estatística podem ser ferramentas efetivas para alcançar melhores aproximações sobre como a carga responderá a perturbações causadas por variações de tensão, sejam estas variações espontâneas, ou devido a ações de controle, ou causadas por curtos-circuitos. / [en] To simulate, predict and control Electric Power Systems (EPS), engineers need tools to model the components of this highly complex interconnected network. Many efforts over the past century were dedicated to develop mathematical models for generators, transmission lines, reactive power compensators, transformers and so on. The main components of the power systems are precisely represented by mathematical models, but the loads are still a source of uncertainty in the simulations, due to their random characteristics. It is well known that conservative load models super estimate power response to voltage deviations, and, on the other hand, over-optimistic load models may underestimate stability margins, leading a system to operate too close to its limit. It is necessary to stablish load representations as close to reality as possible, in order to fully exploit grid resources. This work provides a methodology for load modeling, investigating and summarizing the steps of the process, whose can be implemented in a wide variety of ways. Data treatment, the choice of a load model representation and their parameters estimation are presented through real case studies, both for dynamic simulation and a steady state application. It is discussed how optimization and statistical inference concepts can be effective tools to reach better approximations on how load will respond to disturbances caused by voltage variations, whether these were spontaneous, due to control actions, or caused by short-circuits.

Voltage instability is a major concern in operation of power systems and it is well known that voltage instability and collapse have led to blackout or abnormally low voltages in a significant part of the power system. Consequently, tracking the proximity of the power system to an insecure voltage condition has become an important element of any protection and control scheme. The expected time until instability is a critical aspect. There are a few energy management systems including voltage stability analysis function in the real-time environment of control centres, these are based on assumptions (such as off-line models of the system loads) that may lead the system to an insecure operation and/or poor utilization of the resources. Voltage instability is driven by the load dynamics, and investigations have shown that load restoration due to the on-load tap changer (OLTC) action is the main cause of the voltage instability. However, the aggregate loads seen from bulk power delivery transformers are still the most uncertain power system components, due to the uncertainty of the participation of individual loads and shortcomings of the present approaches in the load modeling. In order to develop and implement a true on-line voltage stability analysis method, the on-line accurate modeling of the higher voltage (supply system) and the lower voltage level (aggregate load) based on the local measurements is required. In this research, using the changes in the load bus measured voltage and current, novel methods are developed to estimate the supply system equivalent and to identify load parameters. Random changes in the load voltage and current are processed to estimate the supply system Thevenin impedance and the composite load components are identified in a peeling process using the load bus data changes during a large disturbance in the system. The results are then used to anticipate a possible long-term voltage instability caused by the on-load tap changer operation following the disturbance. Work on the standard test system is provided to validate the proposed methods. The findings in this research are expected to provide a better understanding of the load dynamics role in the voltage stability, and improve the reliability and economy of the system operation by making it possible to decrease uncertainty in security margins and determine accurately the transfer limits.