RESUMO NEWAVE Versus ODIN: Comparação entre Modelo Estocástico e Determinístico no Planejamento da Operação Energética do Sistema Interligado NacionalEste artigo compara o modelo NEWAVE, uma abordagem baseada em programação dinâmica dual estocástica usada no Brasil para o planejamento da operação de médio prazo, com o modelo ODIN (Otimização de Despacho Interligado Nacional), uma abordagem determinística baseada em modelo de controle preditivo. A primeira adota uma representação agregada do sistema e aproximações lineares para possibilitar a aplicação da técnica de programação dinâmica ao sistema brasileiro. A última usa um algoritmo de otimização não linear considerando vazões futuras previstas com uma representação detalhada das usinas individualmente. Dados de fontes oficiais foram usados para formular um caso de estudo sobre o planejamento mensal de Janeiro de 2011 que system and piecewise linear approximations to make the application of dynamic programming solution technique possible to the Brazilian system. The latter uses a nonlinear optimization algorithm considering predicted future inflows with a detailed representation of the individual power plants. Data from official sources were used to formulate a case study on the monthly operation planning of January 2011 that considers the projected expansion plans up to December 2015. Tests were performed by simulation using 75 historical inflow scenarios. In comparison to the scheduling provided by the stochastic approach, the proposed deterministic one was found to provide a superior performance due to the more efficient use of water resources, leading to a more secure and economic operation.
AbsRact-Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can he represented as a cumhination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problem, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: hinary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chmmomes.
Analysis of Forecast Error of Monthly Streamflow with Different Forecast HorizonsThis paper addresses the problem of forecasting for monthly mean streamflow series, in which we call the forecast horizon (h), the interval of time between the last observation used in fitting the model prediction and the future value being predicted. The analysis of the forecast error is made on the basis of the forecast horizon. These series have a periodic behavior on average, ariance and autocorrelation function. Therefore, we consider the widely used approach to modeling these series that initially consists of removing the periodicity in mean and variance of the streamflow series and then calculating a standardized series for which stochastic models are adjusted. In this study we consider the series to the standard periodic autoregressive models PAR (p m ). Orders p m of the adjusted models for each month are determined from the analysis of periodic partial autocorrelation function (PePACF), using the Bayesian Information Criterion (BIC) applied to PAR models, proposed in (MecLeod, 1994) and analysis of PePACF proposed in (Stedinger, 2001). The forecast errors are calculated on the basis of parameters adjusted and evaluated for forecasting horizons h, ranging from 1 to 12 months on the RESUMOEste trabalho aborda o problema de previsão para séries de vazões médias mensais, no qual denomina-se de horizonte de previsão (h), o intervalo de tempo que separa a última observação usada no ajuste do modelo de previsão e o valor futuro a ser previsto. A análise do erro de previsão é feita em função deste horizonte de previsão. Estas séries possuem um comportamento periódico na média, na variância e na função de autocorrelação. Portanto, considera-se a abordagem amplamente usada para a modelagem destas séries que consiste inicialmente em remover a periodicidade na média e na variância das séries de vazões e em seguida calcular uma série padronizada para a qual são ajustados modelos estocásticos. Neste estudo considera-se para a série padronizada os modelos autorregressivos periódicos PAR(p m ). As ordens p m dos modelos ajustados para cada mês são determinadas usando os seguintes critérios: a análise clássica da função de autocorrelação parcial periódica (FACPPe); usando-se o Bayesian
Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can be represented as a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problems, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: binary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chromosomes.
This paper is concerned with the application of Artificial Neural Networks (ANNs) techniques to the coupled operation of Hydroelectric Power Plants (HPPs). The optimal behavior of Hydroelectric Power Systems depends on both the relative position of each HPP along the cascade and the relationships among the hydro plants. The main purpose of this work is to apply ANNs to learn these relationships, aiming to use them to simulate the optimal operation of the hydroelectric system. The used ANN architecture is the Radial Basis Function network. The proposed methodology is applied to a real system: seven large HPPs located in the Brazilian Southeast System. The achieved results show that the methodology is highly promising, and studies with larger systems need to be carried out.
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