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The present work aims to obtain a ranking of hydro generating units using the analytic hierarchy process (AHP) method, based on the maintenance indicators of the Santo Antonio Hydroelectric Plant (SAHP). The purpose of the generated classification is to provide subsidies to assist maintenance planning, determining the most critical turbines for performing preventive maintenance, in order to increase the availability of equipment, in addition to reducing the probabilities of forced shutdowns for corrective maintenance, impacting factors the plant’s availability factor. Thus, the main contribution of this work is the application of AHP to classify the SAHP generating units, using parameters and actual historical data from the plant and hydraulic turbines.

The maintenance schedule planning of hydro plant generating units is a subject of great interest to several agents in the energy industry. A correct approach for this problem can prevent the degradation of physical assets and minimize the probability of forced shutdowns of their equipment. In addition to these factors, due particularities of the Brazilian system, the operational strategies of its agents are also affected by the maintenance schedule of the generating units. This occurs due to the Availability Factor (AFA), which is directly influenced by the hours of maintenance performed at the plant and, in case of a performance below the stipulated in the concession contracts, it can lead to financial losses or administrative sanctions applied by the regulatory agent. With this motivation in mind, the present work proposes a methodology for Generator Maintenance Scheduling (GMS) of a hydroelectric plant, developing a mathematical model to determine the ideal moment to perform maintenance, considering operational restrictions and regulatory aspects of hydroelectric plants. The optimization methodology proposed for this problem is done through mixed-integer linear programming, where the integer variables consist of the operating state and start date of maintenance of each generating unit. In the end, to validate the proposed modeling, a case study is carried out for a real large plant in the Brazilian system.

This paper presents a modified stochastic dual dynamic programming methodology in order to include the wind energy scenarios in the mid-term hydrothermal systems operation planning with an hourly representation of the demand. As the wind power intermittence adds more complexity for this class of problem, the conventional stochastic dual dynamic programming (SDDP) is not suitable to solve it due the excessive computational effort. Then the proposed approach includes in a conventional SDDP an analytical representation of immediate cost function for each stage and inflow/wind scenarios. Analysis results considering different cases of the South Brazilian system will be used to show the effectiveness and robustness of the proposed methodology.

Recentemente, o impacto das GD's nos sistemas de distribuição tornou-se significativo objeto de estudo devido ao seu crescimento ao longo dos anos. Técnicas de otimização são ferramentas que podem ser usadas para localizar e dimensionar as unidades GD no sistema, de modo a utilizar essas unidades dentro de certos limites e restrições estabelecidos. Neste sentido, o objetivo deste trabalho consiste em aplicar a Meta-heurística baseada no BAT Algorithm para solucionar o problema Não-Linear Inteiro Misto para alocação e dimensionamento de GD's, a fim de melhorar a margem de estabilidade de tensão. A natureza probabilística das GD's são consideradas neste estudo. A metodologia será aplicada em um sistema teste de distribuição de energia elétrica, sendo que a solução obtida pela Meta-heurística BAT será comparada com enumeração exaustiva.

This article presents a methodology for including wind power generation in the medium-term planning ofhydrothermal systems, where Stochastic Dual Dynamic Programming (SDDP) is widely applied in the literature to solve this class of problem. To assess the impact of the intermittent generation, wind power scenarios were generated through Weibull Distribution, which were applied to reduce the load, generating several demand scenarios. Thus, the aim of this paper is to improve the computational eort of the conventional SDDP with demand scenarios, where the main contribution of the work consists of applying the Immediate Cost Function (ICF) to accelerate the SDDP convergence process. The proposed methodology was analyzed using part of the Brazilian system, considering a wind farm.

Este artigo tem como objetivo avaliar o desempenho de modelos representativos das incertezas de curto prazo associadas à geração eólica nos estudos do fluxo de potência ótimo (FPO). A geração eólica é representada por séries temporais de potência ativa agrupadas a partir de três diferentes técnicas de clusterização: k-means, ISODATA e métodos hierárquicos. Essas técnicas têm como objetivo reduzir a quantidade de estados necessários para modelar o comportamento do vento, sem que haja perda de representatividade da série original, a fim de reduzir o esforço computacional nos estudos do FPO com inclusão da geração eólica. A fim de verificar o desempenho de cada técnica, foram realizadas simulações no sistema IEEE 118 barras. A Simulação Monte Carlo, com alto custo computacional, é utilizada como benchmark.

O presente trabalho propõe a obtenção de um ranking de unidades geradoras com a utilização do método Analytic Hierarchy Process (AHP), baseado nos indicadores de manutenção da Usina Hidrelétrica Santo Antônio (SAE). O intuito da classificação gerada é fornecer subsídio para auxiliar o planejamento das manutenções, determinando as turbinas mais críticas para a realização da manutenção preventiva, de forma a aumentar a disponibilidade dos equipamentos, além de reduzir as probabilidades de paradas forçadas para manutenções corretivas, fatores impactantes no Fator de Disponibilidade (FID) da usina. Dessa forma, a principal contribuição deste trabalho é a aplicação do AHP para classificar as unidades geradores da SAE, utilizando parâmetros e dados históricos reais da usina e das turbinas hidráulicas.

Este artigo apresenta uma abordagem para incluir restrições de segurança no Fluxo de Potência Ótimo DC Não-Linear (FPODCNL) a fim de indicar ações de restauração para o operador. A região de segurança é formada pelos limites dos ângulos das tensões nas barras obtidos com medições através de PMU que incluem estados operacionais do sistema avaliado como seguro. O histórico operativo inclui fontes de geração eólica e diversos cenários de carga. Após o mapeamento das regiões seguras elas são incorporadas no FPODCNL para indicar ações de restauração quando o sistema estiver em contingência. A metodologia proposta é testada no sistema IEEE 14 barras. Os resultados mostram que as restrições de segurança são eficazes em fornecer tomadas de decisão para manter o sistema elétrico em uma região segura.

This paper proposes an alternative approach to analyze the convergence of multistage deterministic dual dynamic programming (DDDP). A new convergence stop criteria is proposed and it is based on a measure of gain produced when a new cut is inserted in cost-to-go function, during the backward phase of the algorithm. To do so, an additional linear programming problem is solved to decide if new cut (hyper plan) will be inserted or not on cost-to-go function. The results presented have shown that this approach is an alternative stop criterion when the interval of convergence is difficult to estimate. In this paper the methodology described was used towards DDDP for learning purposes. However, the same methodology can be easily extended for multi-objective problems and also for stochastic dual dynamic programming (SDDP). To prove the method effectiveness, results of a Brazilian study case will be shown, in which the methodology proposed was applied by using SDDP to minimize operative cost and deficit risks.

Due to the high importance of economic dispatch in planning and operating electric power systems, new methods have been researched to minimize the costs of power generation. To calculate these costs, the power generation of each thermal unit must be evaluated. When a thermal unit is modelled considering real world constraints, such as multiple fuels and valve point effect, traditional optimization methods are inefficient due to the nature of the cost function. This paper shows a study of a metaheuristic method, based on flower pollination to search for satisfactory results for economic dispatch. The results obtained are compared with results from other authors, with the purpose of evaluating how efficient the technique presented here is.