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Este artigo apresenta medidas de eficiência para 40 distribuidoras de energia elétrica que operam no setor elétrico brasileiro. As medidas foram obtidas por modelos de análise envoltória de dados (DEA) e modelos de fronteira estocástica (SFA), duas técnicas que podem mitigar a assimetria de informação e aprimorar a habilidade do agente regulador comparar os desempenhos das distribuidoras, requisitos fundamentais em esquemas de regulação incentivada. As duas abordagens são apresentadas e os resultados obtidos pelos diferentes modelos são comparados.

ResumoEntre os aspectos da qualidade do fornecimento de energia elétrica destaca-se a continuidade, avaliada com base nos indicadores DEC e FEC que expressam, respectivamente, a duração e a freqüência das interrupções do fornecimento. Propõe-se uma nova implementação da regulação por comparação de desempenho na definição dos níveis toleráveis de DEC/FEC (metas de continuidade) para as concessionárias de distribuição e seus conjuntos de unidades consumidoras. Na abordagem proposta combinam-se dois modelos de Análise Envoltória de Dados (DEA) em um processo com dois estágios: primeiro um modelo DEA clássico estabelece quanto cada distribuidora deve melhorar globalmente os seus indicadores de continuidade, em seguida, por meio de um modelo para alocação de recursos, baseado em DEA, comparam-se os desempenhos dos conjuntos em uma mesma distribuidora e definem-se as metas locais de continuidade para cada conjunto. Apresentam-se metas locais para os conjuntos das duas principais concessionárias que atendem o Estado do Rio de Janeiro.Palavras-chave: distribuição de energia elétrica; continuidade do fornecimento; análise envoltória de dados. AbstractThe main dimension of the electricity quality is the supply continuity. It is evaluated by indices SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index). This paper presents a new implementation of the yardstick competition that combines two Data Envelopment Analysis models (DEA) to set the continuity standards for the electricity distribution utilities and their groups of consumption units. The approach has two stages. First, a classical DEA model performs a comparative analysis between utilities to define global continuity standards for each utility; next, based on global standards, a model for resource allocation based on DEA establishes the local continuity standards for groups of consumption units in the same utility. Local standards for the consumption units groups of the main distribution utilities in the Rio de Janeiro State are presented.

ResumoNo setor elétrico brasileiro, as companhias de transmissão são remuneradas pela disponibilidade da capacidade de seus ativos, independentemente da quantidade de energia elétrica transmitida. Para induzir a operação eficiente das transmissoras, a ANEEL deve revisar periodicamente as receitas permitidas das transmissoras, considerando custos operacionais eficientes. Recentemente, a ANEEL publicou uma resolução em que descreve a metodologia utilizada no cálculo dos custos operacionais eficientes das transmissoras, a qual inclui um modelo de análise envoltória de dados (DEA). Neste trabalho propomos uma adaptação deste modelo DEA e apresentamos uma análise de sensibilidade dos resultados obtidos pelos dois modelos.Palavras-chave: análise envoltória de dados; transmissão de energia elétrica; revisão tarifária. AbstractIn the Brazilian power sector, the transmission companies (TRANSCOS) receive revenues by the availability of their transmission facilities, regardless of the amount of the electric power transmitted. To promote their efficient operation the regulator periodically revises the revenue caps. The calculation of efficient operational costs is the first stage of the tariff revision process. Recently, the regulator agent published a resolution describing the methodology used to set the revenues of the Brazilian TRANSCOS. This algorithm includes a data envelopment model (DEA) which is described in this paper. In this work we propose an alternative DEA model and present a comparison of the results obtained by the two models.

Dengue is a re-emerging disease, currently considered the most important mosquito-borne arbovirus infection affecting humankind, taking into account both its morbidity and mortality. Brazil is considered an endemic country for dengue, such that more than 1,544,987 confirmed cases were notified in 2019, which means an incidence rate of 735 for every 100 thousand inhabitants. Climate is an important factor in the temporal and spatial distribution of vector-borne diseases, such as dengue. Thus, rainfall and temperature are considered macro-factors determinants for dengue, since they directly influence the population density of Aedes aegypti, which is subject to seasonal fluctuations, mainly due to these variables. This study examined the incidence of dengue fever related to the climate influence by using temperature and rainfall variables data obtained from remote sensing via artificial satellites in the metropolitan region of Rio de Janeiro, Brazil. The mathematical model that best fits the data is based on an auto-regressive moving average with exogenous inputs (ARMAX). It reproduced the values of incidence rates in the study period and managed to predict with good precision in a one-year horizon. The approach described in present work may be replicated in cities around the world by the public health managers, to build auxiliary operational tools for control and prevention tasks of dengue, as well of other arbovirus diseases.

This work describes a methodology for long-term electricity demand forecast in the residential sector. The methodology has been used in the power market studies of some Brazilian distribution utilities. The methodology is based on decomposition of the total electricity residential consumption in three components: average consumption per consumer unit, electrification rate and number of households. Then, the forecast for the total electricity consumption in residential sector is the product of forecasts for these three components. The prediction for the number of households is based on demographic models while the future trajectory of the electrification rate is defined by the targets for achieving the universal access to electricity. The product of these two components provides a forecast to the number of residential customers. The average consumption per unit consumer depends on the macroeconomic scenarios for GDP, average household income and income distribution. The proposed methodology provides a framework to integrate macroeconomic scenario, demographic projection and assumptions for ownership and efficiency of electric appliances in a long-term demand forecast. In order to illustrate the application of the proposed methodology, this paper presents a ten-year demand forecasts for the residential sector in Brazil.

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