Computation techniques play an important role in most engineering problems in which optimization problems have to be faced. Energy management operations represent one of these cases where real-time energy production, transfer, storage and consumption need to be optimized. In this context renewable energy sources can be managed using evolutionary computation and other tools. In this light artificial neural network solution based on weather forecast can estimate energy flows combined with the event-driven variability encouraging 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 the power grid in a future scenario.
Artificial intelligence forecast of PV plant production for integration in smart energy systems
SIMONOV, MIKHAIL;MUSSETTA, MARCO;GRIMACCIA, FRANCESCO;LEVA, SONIA;ZICH, RICCARDO
2012-01-01
Abstract
Computation techniques play an important role in most engineering problems in which optimization problems have to be faced. Energy management operations represent one of these cases where real-time energy production, transfer, storage and consumption need to be optimized. In this context renewable energy sources can be managed using evolutionary computation and other tools. In this light artificial neural network solution based on weather forecast can estimate energy flows combined with the event-driven variability encouraging 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 the power grid in a future scenario.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
