Maria Elvira Piñeiro Maceira

Bundle relaxation and primal recovery in Unit Commitment problems. The Brazilian case.

We consider the inclusion of commitment of thermal generation units in the optimal management of the Brazilian power system. By means of Lagrangian relaxation we decompose the problem and obtain a nondifferentiable dual function that is separable. We solve the dual problem with a bundle method. Our purpose is twofold: first, bundle methods are the methods of choice in nonsmooth optimization when it comes to solve large-scale problems with high precision. Second, they give good starting points for recovering primal solutions. We use an inexact augmented Lagrangian technique to find a near-optimal primal feasible solution. We assess our approach with numerical results.

Optimal scenario tree reduction for stochastic streamflows in power generation planning problems

The mid-term operation planning of hydro-thermal power systems needs a large number of synthetic sequences to represent accurately stochastic streamflows. These sequences are generated by a periodic autoregressive model. If the number of synthetic sequences is too big, the optimization planning problem may be too difficult to solve. To select a small set of sequences representing the stochastic process well enough, this work employs two variants of the Scenario Optimal Reduction technique. The first variant applies such a technique at the last stage of a tree defined a priori for the whole planning horizon while the second variant combines a stage-wise reduction, preserving the periodic autoregressive structure, with resampling. Both approaches are assessed numerically on hydrological sequences generated for real configurations of the Brazilian power system.

Climatological and hydrological patterns and verified trends in precipitation and streamflow in the basins of Brazilian hydroelectric plants

This study focuses on investigating possible changes in hydrometeorological behavior on important Brazilian river basins for power generation purposes. Thereby, this research analyzes the historical averages and observed trends regarding rainfall and streamflow and their impact on the hydrological regime. Ten river basins were selected for the assessment of alterations in the precipitation and streamflow series throughout descriptive measures and statistical significance tests (Mann-Kendall and Sen’s slope). These data are available over different time spans, but most of the records include information from 1961 to 2006. As long as these river basins are subjected to different climate types, their corresponding rainfall and streamflow patterns vary accordingly the basin location in the country, in addition to the season. Most of the country has a predominantly tropical climate, with a wet period concentrated between October and March and a dry period between April and September. Some exceptions are the Northeastern region, where the climate is semi-arid, besides the Southern region, where there is abundant precipitation throughout the year. According to the obtained results, it could be noted that the streamflow response to precipitation is faster in the smaller river basins, what can be due especially to the water travel time. It was found that Belo Monte basin, in Northern Brazil, presents a statistically significant reduction in the annual rainfall. Furthermore, a significant decrease in the annual streamflow was also identified in Xingó and Sobradinho basins, in the Northeastern region. In contrast, Itaipu basin, located in Southern Brazil, showed increasing statistically significant trends in annual rainfall and streamflow during the second half of the twentieth century. Relevant decreasing trends were also identified in the minimum streamflows of the Brazilian Northern and Northeastern basins and increasing ones in the maximum streamflows in the Southern region basins. The results obtained in this work will support the assessment of the impact of rainfall and streamflow future scenarios in regulating capacity of the hydroelectric power plant reservoirs.

Incorporating multi-area reliability concepts to the evaluation of the Brazilian power system capacity to supply the peak load

This paper discusses the adoption of multi-area reliability concepts in the evaluation of Brazilian power system capacity to supply the peak load. In the assessment of the interconnected hydrothermal power systems reliability it is adopted a computational model that takes into account uncertainties on the streamflows into the hydro plants reservoirs, random forced outages of generating units and interconnections among subsystems, system load curve and programmed outage of generating units. The model allows both single area and multi-area analysis. The calculated reliability indices can help the identification of possible power system deficiencies to supply the forecasted maximum peak load as well as the computation of power marginal costs in libelarized electricity markets. In addition, it is discussed an approach to estimate the maximum peak load that a hydrothermal generation system can supply considering a multi-area reliability criterion based on adequacy analysis. The obtained results encourage the continuity of studies seeking the establishment of such criterion for hydrothermal interconnected systems.

Application of stochastic programming and probabilistic analyses as key parameters for real decision making regarding implementing or not energy rationing - a case study for the Brazilian hydrothermal interconnected system

During 2014 and 2015, the Brazilian hydrothermal interconnected system faced critical hydrological conditions, such as extremely low multivariate inflow values on February 2014 and January 2015. Therefore, an issue that arose in early 2014 was whether the Brazilian government would have to implement or not an energy rationing. In this sense, this paper summarizes a proposed approach to technically support this decision, based on dual stochastic dynamic programming, multivariate inflows scenarios generation and probabilistic analyses, and that utilized the chain of optimization models developed by CEPEL and real configurations of the Brazilian large scale interconnected hydrothermal system. These studies, inserted in a very comprehensive and detailed technical analyses carried out by the Brazilian Monitoring Committee of the Electrical Sector, led to the decision of not implementing an energy rationing in 2014, and to continue to closely monitoring the electric power system performance.

A Methodology For Data Cleaning Of Wind Speed Time Series

Abstract – The prediction of wind resources is a key item for the safe and economic integration of wind farms in the operation of electrical systems. The accuracy of such predictions depends on the quality of the data. This article presents a methodology for filtering wind speed time series by using fuzzy clustering method (FCM) and local regression (LOESS). The goal is to improve the data quality for the time series modeling.

Uma Metodologia para Filtragem de Medições Anemométricas

ResumoA previsão dos recursos eólicos é um requisito fundamental para a integração segura e econômica dos parques eólicos à operação do sistema elétrico. A precisão da previsão depende da qualidade dos registros anemométricos, em geral caracterizados por lacunas de dados e observações aberrantes decorrentes de falhas no sistema de medição. Tais características comprometem a modelagem da série temporal de velocidade de vento e a precisão das previsões. Neste artigo é apresentada uma metodologia baseada em técnicas estatísticas e de mineração de dados para o preenchimento de lacunas e filtragem dos registros de velocidade do vento. A metodologia proposta tem como objetivo melhorar a qualidade dos dados para a modelagem da velocidade de vento.