Previsão da produção de eletricidade em instalações de energia solar fotovoltaica usando métodos diretos / Forecast of electricity production in photovoltaic solar energy facilities using direct methods

O trabalho apresenta uma comparação entre os resultados obtidos para a previsão da produção de eletricidade em instalações de energia solar fotovoltaica, usando diferentes métodos diretos. Devido à natureza intermitente e incerta da energia solar, associada à influência de múltiplos fatores meteorológicos, a geração de energia fotovoltaica necessita de ferramentas de previsão cada vez mais precisas para garantir o funcionamento eficiente e confiável do sistema. Nesse estudo, as previsões para cada hora analisada são calculadas por três dos métodos mais usuais e são comparadas usando o erro percentual absoluto médio como referência. Os resultados são testados com os dados de geração de energia obtidos do Parque Solar Fotovoltaico Imías, que tem uma capacidade instalada de 2,2 MWp. Independentemente dos métodos mostrarem que garantem uma previsão com alta precisão, existem diferenças na eficácia de cada previsão, nas mesmas condições

ANN sizing procedure for the day-ahead output power forecast of a PV plant

Since the beginning of this century, the share of renewables in Europe's total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance

Optimization Models for islanded micro-grids: A comparative analysis between linear programming and mixed integer programming

This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production

Hybrid model analysis and validation for PV energy production forecasting

In this paper a forecasting method for the Next Day's energy production forecast is proposed with respect to photovoltaic plants. A new hybrid method PHANN (Physical Hybrid Artificial Neural Network) based on Artificial Neural Network (ANN) and basic Physical constraints of the PV plant, is presented and compared with an ANN standard method. Furthermore, the accuracy of the two methods have been studied in order to better understand the intrinsic error committed by the PHANN, reporting some numerical results. This computing-based hybrid approach is proposed for PV energy forecasting in view of optimal usage and management of RES in future smart grid applications