In this paper an optimal controller for battery management in smart home environments is presented in order to save costs and minimize energy waste. The considered scenario includes a load profile that must always be satisfied, a battery-system that is able to storage electrical energy, a photovoltaic (PV) panel, and the main grid that is used when it is necessary to satisfy the load requirements or charge the battery. The optimal controller design is based on a class of adaptive critic designs (ACDs) called action dependent heuristic dynamic programming (ADHDP). Results obtained with this scheme outperform the ones obtained by using the particle swarm optimization (PSO) method.
Optimal Battery Management with ADHDP in Smart Home Environments / D., Fuselli; F., de Angelis; M., Boaro; D., Liu; Q., Wei; Squartini, Stefano; Piazza, Francesco. - Volume 7368 LNCS, Issue PART 2:(2012), pp. 355-364. [10.1007/978-3-642-31362-2_40]
Optimal Battery Management with ADHDP in Smart Home Environments
SQUARTINI, Stefano;PIAZZA, Francesco
2012-01-01
Abstract
In this paper an optimal controller for battery management in smart home environments is presented in order to save costs and minimize energy waste. The considered scenario includes a load profile that must always be satisfied, a battery-system that is able to storage electrical energy, a photovoltaic (PV) panel, and the main grid that is used when it is necessary to satisfy the load requirements or charge the battery. The optimal controller design is based on a class of adaptive critic designs (ACDs) called action dependent heuristic dynamic programming (ADHDP). Results obtained with this scheme outperform the ones obtained by using the particle swarm optimization (PSO) method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
