Advanced predictive models towards PV energy integration in smart grid

Computational techniques play an important role in most engineering problems in which optimization problems have to be faced. Renewable energy operations represent one of these cases where energy transfer and storage, real-time operations and consumption profiles need to be optimized. In this context renewable sources can be managed using evolutionary computation and other tools. In this light artificial neural network solution using fuzzy logic techniques can estimate energy flows basing their estimation on weather forecast, and the knowledge of this event-driven variability can encourage photovoltaic integration with the electric power system. This article discusses the role of these computational tools and some issues related to the variability and uncertainty in the operations where PV plants are potentially fully connected to a smart grid future scenario.