Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%

Phenomena of overirradiance have been pointed all over the World. This note presents the most extreme enhancement event reported in Brazil, which contains an irradiance reading of 1590 W/m2 measured in São Paulo (latitude 23°32′S) at relatively low altitude (760 m a.s.l.)

Transmission flexibility is a key component of current power systems and demands a reconfiguration of alternatives to expand transmission infrastructure. This paper addresses the use of a Battery Energy Storage System (BESS) as an asset of the transmission system that provides increased transmission capacity. Furthermore, the BESS also supports operational procedures of the transmission system in the course of the re-establishment of normal operation during transients, which helps maintain the power quality requirements. A case study is presented to assess the additional capabilities that an operational 30 MW/60 MWh BESS primarily used to provide congestion relief in the state of São Paulo, Brazil, could provide to the power system. Based on a 5-year horizon transmission and generation expansion plans by local governing bodies, a set of four alternative applications for this BESS was proposed and studied, as follows: (1) increased operational flexibility under contingencies and maintenance, (2) islanded operation for increased reliability, (3) grid support during system restoration, and (4) increased hosting capacity for variable renewables. The results show that the BESS improves performance and power quality indexes while aiding the operation during contingencies.

Many remote, isolated communities rely on diesel generators to meet their electrical demands because connecting to conventional electrical grids is either too expensive or technically impractical. In such situations, microgrids made up of diesel gensets and renewable energy sources are most appropriate. However, when diesel generators operate with reduced operating reserve margins, these isolated systems do not have sufficient capacity to withstand large electrical transients, which can lead to instability. As a result, a more detailed evaluation of acceptable levels of renewable energy penetration is required. Hence, the focus of this work is to develop a high-fidelity model for small three-phase diesel generators (SDG), incorporating adaptations to the traditional IEEE Exciter and the Woodward Diesel Engine-Governor Model, with the goal of enhancing the accuracy of dynamic frequency and voltage responses. The effectiveness of the proposed SDG model is validated using field test data of a three-phase genset of 40 kVA–220 V that is part of an operational microgrid. The results show that the accuracy of the proposed model is satisfactory, presenting Normalized Mean Squared Error (NMSE) values around 16% and 10% for speed and voltage responses, respectively.

This paper applies Artificial Neural Network to predict the amount of energy generated by a Grid Connected Photovoltaic System installed at the Institute of Electrotechnic and Energy of University of São Paulo (IEE/USP). Irradiance, back cell temperature and power data were collected during the period of one year. This methodology allows performing an analysis of the production of Grid Connected Photovoltaic Systems and the commercialization of the energy generated. Finally, the methodology was validated comparing relative error between measured data and estimated data.