This paper concerns an application of a three-phase cage induction machine (IM) as a self-excited generator connected to the AC side of a voltage-source PWM bidirectional inverter. The generator is supposed to be driven by a low-head unregulated shaft hydraulic turbine. The proposed system is intended to be applied in rural plants as a low-cost source of high-quality AC sinusoidal regulated voltage with constant frequency. Simulation results are obtained based on the /spl alpha//spl beta//spl gamma/ stationary reference frame model of the IM. The experimental results demonstrated that the system presents satisfactory behavior when feeding AC loads and during the startup of induction motors.

The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.

Renewable energy sources (RES) like solar and wind, naturally present high daily and seasonal variability of power generation. This paper presents a real case study of solar photovoltaic (PV) power forecasting of a building-integrated PV plant as a tool for power systems operation. A new methodology composed of data processing strategy, Artificial Neural Networks (ANN) modelling, and error metrics definitions are proposed and validated for the intra-day PV power forecasting. Data were modelled in time steps of 15-minute averages and the forecasts obtained from 15 minutes to 120 minutes ahead. A persistence model is developed for the building-integrated PV systems (BIPVS) case. The forecast skills achieved range from 9.79% to 23.75%, which measures the ANN improvement in relation to the proposed benchmark persistence model.

This study focused on developing a sustainability project carried out in 11 Federal Institute of Education, Science, and Technology of Goiás (IFG) campuses wherein energy efficiency and distributed generation actions were developed. Energy consumption was optimized by retrofitting the lighting system, installing a photovoltaic (PV) generation system, quantifying the building efficiency, energy monitoring, training, and qualification, and focusing on the efficient use of electric energy. We first present the Brazilian legislation that regulates the Research and Development Program in the electric energy sector. Then, we describe the case study that was applied to the educational institution. In the lighting system, 18,377 inefficient lamps were replaced by lamps with more efficient technology, with an energy saving of 867.9 MWh/year and a peak demand reduction of 309.6 kW. The proposed generation system aimed to install 3076 PV modules on the roofs of selected campus buildings, totaling 1 MWp of installed power with an average annual power generation of 1736.9 MWh/year. The total project investment was USD 1,348,768.50 and the overall cost–benefit ratio of the project was 0.68, which will result in annual savings of approximately USD 197,321.85. This corresponded to a 58% reduction in energy bills. The project proposed in this work was considered technically and economically viable within the scope of the Brazilian Energy Efficiency Program.

The operating cost reduction of photovoltaic (PV) systems is an important way to increase its economic viability for stakeholders. In this study, an inverter sizing ratio (ISR) analysis is carried out in order to quantify its potential benefit in the context of building‐integrated PV systems and PV distributed generation in the central region of Brazil. An algorithm was created to analyse the ISR considering 12 years of measured climate data and data provided by manufacturers of system components. Among three analytical methodologies discussed in this study, the power limitation analysis seems to be the best reference for design adjustments related to a specific location or energy market. By that, a potential reduction over 10% of levelised cost of energy can be achieved in case of varying ISR from the unity value (theoretical) to its new fitted value.