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The regulatory power sector model in several countries determines tariff review forms based, among other things, on sharing efficiency gains with consumers. As these reviews have an important impact on consumers and distribution utilities, it is necessary that the adopted methodologies always be improved. To this end, this article assessed a Bayesian inference application in order to estimate a stochastic cost frontier considering temporal efficiency dynamics. Taking this point into consideration is essential, since studies carried out to assess power sector efficiency have neglected the fact that part of efficiency increases originate from scale gain due to market expansion, which occurs over time. The sample assessed herein is composed of panel data from 61 electric power utilities between 2003 and 2016. The results demonstrate that the tariff review is positively affected by distributor efficiency.

Energy models consider technical and economic criteria to define the optimal energy mix to produce electricity. However, the results are sub-optimal from the social point of view, since they do not include socio-environmental factors. The purpose of this study is to access the sustainability of power plants considering technical-economic, socio-environmental and institutional aspects so that to improve the expansion planning. Thus, the efficiencies calculated by slacks based measure approach of Data Envelopment Analysis methodology can be implemented as constraints in the models, signalizing that the socio-environmental impact cannot exceed a limit, which can be calculated by the energy model itself. The results show that renewable power plants contribute more to allocative efficiency than fossil fuel ones, both in terms of social and environmental aspects, which are of utmost importance in ranking power plants to model power capacity expansion. Considering just technical and economic factors has proved to be insufficient to optimize the system in terms of allocative efficiency.

The energy companies are always facing the challenge of producing more accurate load forecasts. A fuzzy logic methodology is proposed in order to extract rules from the input variables and provide Brazil's long-term annual electricity demand forecasts. In recent literature, the formulation of these types of models has been limited to treating the explanatory variables in the univariate form, or involving only the GDP. This study proposes an extension of this model, starting with population and the GDP additional value. The proposed model is compared with the official projections. The obtained results are quite promising.

In Brazil, the transmission assets are remunerated for their availability regardless of the full utilization of their capacities. The annual allowed revenue for each transmission utility is determined by the Brazilian Electricity Regulatory Agency (ANEEL). ANEEL has been using Data Envelopment Analysis (DEA) to define the regulatory operational expenditure (OPEX) for each utility, a key element for the annual allowed revenue assessment. Based on the ANEEL's DEA model this work proposes improvements in representing the quality of service, transmission capacity and transmission network length in the DEA model. Furthermore, this paper presents a more robust methodology to evaluate the regulatory operational expenditure of transmission utilities by applying several distinct DEA models; then a global efficiency score is defined by the geometric mean of the efficiency scores resulting from the set of DEA models analyzed. The performance of the proposed approach is illustrated with real data of the Brazilian transmission utilities.

In this paper we estimate, analyze and predict short-term non-technical loss (NTL) of electric power of Brazilian energy service companies based on different assumptions for the covariance structure of the errors and controlling for socio-economic confounding variables. Although the correlation among repeated responses is not usually of intrinsic interest, it is an important aspect of the data that must properly be accounted for to produce valid inferences in longitudinal or panel data analysis. In the extended linear mixed effects model, the covariance matrix of the response vector is comprised by two subcomponents, a random effect component that can represent between group variation and a intraclass or within group component. So, in order to adequately treat the longitudinal character of NTL data, we use the decomposition of these variance components to evaluate different architectures to the within group errors. Using data of 59 Brazilian distributing utilities from 2004 to 2012, we fit a conditionally independent errors model and three other models with autoregressive-moving average parametrization to the intraclass disturbances. Finally, we compare models using the MAD and MAPE metrics in the prediction of NTL for the year of 2013. The findings suggest that the approach can be satisfactorily implemented in future statistical analysis of NTL.

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.

This paper shows efficiency indices for 60 Brazilian electricity distribution utilities. The efficiency scores are gauged by three DEA models. For both models, these quantities are evaluated under different contexts. One treats with respect to the regulator perspective. The others examine an alternative approach based on cluster analysis and restrictions on factor weights. It is worth pointing out that these developments can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental in incentive regulation schemes.