Title :
Recurrent neural network-based control strategy for battery energy storage in generation systems with intermittent renewable energy sources
Author :
Capizzi, G. ; Bonanno, F. ; Napoli, C.
Author_Institution :
Dept. of Electr., Electron. & Inf. Eng., Univ. of Catania, Catania, Italy
Abstract :
The intermittent nature of renewable sources as wind and solar puts a challenge for their use in supply energy to small islands, isolated communities or in developing countries. The integration of battery energy storage system (BESS) or diesel groups is then mandatory. The aim of the paper is to propose a complete recurrent neural networks (RNN) based control strategy of the BESS accounting state of charge (SOC) and terminal voltage and that can be used for their size and to test the use of different type of BESS.
Keywords :
battery storage plants; energy storage; power generation control; recurrent neural nets; renewable energy sources; BESS accounting state of charge; battery energy storage; diesel groups; generation systems; intermittent renewable energy sources; isolated communities; recurrent neural network-based control; small islands; solar; terminal voltage; wind; Batteries; Load modeling; Mathematical model; Recurrent neural networks; Renewable energy resources; System-on-a-chip; Battery energy storage systems; RNN based control; integrated generation systems; recurrent neural network; renewable energies; state of charge;
Conference_Titel :
Clean Electrical Power (ICCEP), 2011 International Conference on
Conference_Location :
Ischia
Print_ISBN :
978-1-4244-8929-9
Electronic_ISBN :
978-1-4244-8928-2
DOI :
10.1109/ICCEP.2011.6036300