In this paper we present an interesting application of Computational Intelligence techniques for the power demand side and flow management optimization in a microgrid. In particular, we used a Fuzzy Logic Controller (FLC) for Time-of use Cost Management program in the microgrid. FLC can either sell and buy energy from outside the microgrid making use of an aggregate of energy storage capacity realized with lithium ion batteries. According to the hybrid Fuzzy-GA paradigm, the Fuzzy Logic Controller that operates decision making on energy flows is optimized by a Genetic Algorithm. The experimental results show that the proposed control system can manage effectively the energy trade with the main grid on the basis of real time prices. © 2013 IEEE.

Genetic optimization of a fuzzy control system for energy flow management in micro-grids / DE SANTIS, ENRICO; RIZZI, Antonello; Alireza, Sadeghian; FRATTALE MASCIOLI, Fabio Massimo. - (2013), pp. 418-423. (Intervento presentato al convegno 9th Joint World Congress on Fuzzy Systems and NAFIPS Annual Meeting, IFSA/NAFIPS 2013 tenutosi a Edmonton; Canada nel 24 June 2013 through 28 June 2013) [10.1109/ifsa-nafips.2013.6608437].

Genetic optimization of a fuzzy control system for energy flow management in micro-grids

Enrico De Santis;RIZZI, Antonello;FRATTALE MASCIOLI, Fabio Massimo
2013

Abstract

In this paper we present an interesting application of Computational Intelligence techniques for the power demand side and flow management optimization in a microgrid. In particular, we used a Fuzzy Logic Controller (FLC) for Time-of use Cost Management program in the microgrid. FLC can either sell and buy energy from outside the microgrid making use of an aggregate of energy storage capacity realized with lithium ion batteries. According to the hybrid Fuzzy-GA paradigm, the Fuzzy Logic Controller that operates decision making on energy flows is optimized by a Genetic Algorithm. The experimental results show that the proposed control system can manage effectively the energy trade with the main grid on the basis of real time prices. © 2013 IEEE.
2013
9th Joint World Congress on Fuzzy Systems and NAFIPS Annual Meeting, IFSA/NAFIPS 2013
energy flow management; fuzzy control; genetic optimization; micro-grids
Pubblicazione in atti di convegno::04b Atto di convegno in volume
Genetic optimization of a fuzzy control system for energy flow management in micro-grids / DE SANTIS, ENRICO; RIZZI, Antonello; Alireza, Sadeghian; FRATTALE MASCIOLI, Fabio Massimo. - (2013), pp. 418-423. (Intervento presentato al convegno 9th Joint World Congress on Fuzzy Systems and NAFIPS Annual Meeting, IFSA/NAFIPS 2013 tenutosi a Edmonton; Canada nel 24 June 2013 through 28 June 2013) [10.1109/ifsa-nafips.2013.6608437].
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/525790
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 37
  • ???jsp.display-item.citation.isi??? 24
social impact