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 pro?les need to be optimized. In this context renewable sources can be managed using evolutionary computation and other tools. In this light arti?cial neural network solution using fuzzy logic techniques can estimate energy ?ows basing their estimation on weather forecast, and the knowledge of this eventdriven 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.
Advanced Predictive Models towards PV Energy Integration in Smart Grid
GRIMACCIA, FRANCESCO;MUSSETTA, MARCO;ZICH, RICCARDO
2012-01-01
Abstract
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 pro?les need to be optimized. In this context renewable sources can be managed using evolutionary computation and other tools. In this light arti?cial neural network solution using fuzzy logic techniques can estimate energy ?ows basing their estimation on weather forecast, and the knowledge of this eventdriven 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
