The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems.<p> A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities.<p> There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator.

The development and utilization of wind energy for satisfying electrical demand has received considerable attention in recent years due to its tremendous environmental, social and economic benefits, together with public support and government incentives. Electric power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of wind energy facilities therefore affect power system reliability in a different manner than those of conventional systems. The reliability impact of such a highly variable energy source is an important aspect that must be assessed when the wind power penetration is significant. The focus of the research described in this thesis is on the utilization of state sampling Monte Carlo simulation in wind integrated bulk electric system reliability analysis and the application of these concepts in system planning and decision making. Load forecast uncertainty is an important factor in long range planning and system development. This thesis describes two approximate approaches developed to reduce the number of steps in a load duration curve which includes load forecast uncertainty, and to provide reasonably accurate generating and bulk system reliability index predictions. The developed approaches are illustrated by application to two composite test systems.<p> A method of generating correlated random numbers with uniform distributions and a specified correlation coefficient in the state sampling method is proposed and used to conduct adequacy assessment in generating systems and in bulk electric systems containing correlated wind farms in this thesis. The studies described show that it is possible to use the state sampling Monte Carlo simulation technique to quantitatively assess the reliability implications associated with adding wind power to a composite generation and transmission system including the effects of multiple correlated wind sites. This is an important development as it permits correlated wind farms to be incorporated in large practical system studies without requiring excessive increases in computer solution time. The procedures described in this thesis for creating monthly and seasonal wind farm models should prove useful in situations where time period models are required to incorporate scheduled maintenance of generation and transmission facilities.<p> There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning. A relatively new approach that incorporates deterministic and probabilistic considerations in a single risk assessment framework has been designated as the joint deterministic-probabilistic approach. The research work described in this thesis illustrates that the joint deterministic-probabilistic approach can be effectively used to integrate wind power in bulk electric system planning. The studies described in this thesis show that the application of the joint deterministic-probabilistic method provides more stringent results for a system with wind power than the traditional deterministic N-1 method because the joint deterministic-probabilistic technique is driven by the deterministic N-1 criterion with an added probabilistic perspective which recognizes the power output characteristics of a wind turbine generator.

[pt] A avaliação probabilística da confiabilidade de um sistema elétrico de potên-cia visa quantificar, em índices, as estatísticas do risco do mesmo não atender seus clientes em plenitude. Na prática, os critérios determinísticos (e.g., N-1) são ainda os mais empregados. Na literatura, porém, a análise probabilística é uma área ex-tensa de pesquisa, podendo ser dividida em duas vertentes: as baseadas em simula-ção Monte Carlo (SMC) e aquelas fundamentadas na enumeração de estados (EE). Apesar de ser reconhecidamente inferior, a técnica EE é a que se assemelha mais aos critérios determinísticos, e, muito provavelmente por esta razão, possui extensa gama de trabalhos relacionados. Contudo, tais trabalhos apresentam limitações, pois, ou se restringem a sistemas de pequeno porte, ou desconsideram contingências de maior ordem quando abordam sistemas reais (médio-grande porte). De qualquer maneira, existe um grande apego do setor elétrico por técnicas de confiabilidade que se assemelhem às práticas dos operadores e planejadores. Isso motivou o de-senvolvimento de um método baseado em EE, o qual seja capaz de avaliar a confi-abilidade de sistemas de geração e transmissão com desempenho comparável ao da SMC. De forma heterodoxa, os conceitos de amostragem por importância (IS - Im-portance Sampling), uma técnica de redução de variância (VRT - Variance Reduc-tion Techniques) tipicamente empregada na SMC, serviram de inspiração para apri-morar a EE. Assim, o método proposto nesta dissertação é o resultado da combina-ção de uma ferramenta do tipo IS-VRT com técnicas de EE. Para análise e validação do método proposto, são utilizados dois sistemas teste comumente empregados neste tópico de pesquisa, sendo um deles de médio porte e capaz de reproduzir caracterís-ticas típicas de sistemas reais. / [en] The probabilistic reliability assessment of an electric power system aims to quantify, in terms of risk indices, its inability to fully serve its customers. In prac-tice, deterministic criteria (e.g., N-1) are still the most widely used. In the literature, however, probabilistic analysis is an extensive area of research, which can be di-vided into two evaluation categories: those based on Monte Carlo simulation (MCS) and those based on the state enumeration (SE). Despite being admittedly inferior, the SE technique is the one that most closely resembles the deterministic criteria, and, most likely for this reason, has a wide range of technical publications. How-ever, such works have limitations, because they are either restricted to small sys-tems, or they disregard higher contingency orders, when addressing real systems (medium-large). In any case, there is a strong attachment of the electric sector to reliability techniques that are similar to the practices of operators and planners. This motivated the development of a method based on SE, which is capable of assessing the reliability of generation and transmission systems with performance comparable to that of MCS. In a heterodox way, importance sampling (IS) concepts used in variance reduction techniques (VRT), typically employed by MCS, have served as inspiration to improve SE. Thus, the method proposed in this dissertation is the combination result of an IS-VRT type tool with SE techniques. For the analysis and validation of the proposed method, two test systems commonly used in this research topic are used, one of which is medium-sized and capable of reproducing typical characteristics of real systems.