Distributed Generation supported by Smart Grids (SG) and Information and Communication Technology (ICT) can be one of the solutions to actual energy problems such as the continuous rise in price of electricity, the availability of fossil fuels or the integration of conventional energy systems with non-programmable renewable resources. One of the main issues in the managements of smart grids is the optimization of load distribution among the various energy systems which concur to the satisfaction of energy demand. To achieve this goal various optimization techniques have been applied such as genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FFA). In order to develop the load distribution optimization in a smart grid characterized by electrical, thermal, cooling and fuel energy fluxes, a new software has been developed by University of Bologna. The software, named EGO (Energy Grids Optimizer) is able to define the load distribution of a number of energy systems operating into a smart grid with the aim of minimize the total cost of the energy production. The software has been validated by comparing its results with the criterion of equal incremental cost. Further a smart grid case study is presented and analysed, to show the effect of the proposed genetic algorithm method on the minimization of the total cost of energy production.

Generation Side Management In Smart Grid. ASME-ATI-UIT 2015 Conference on Thermal Energy Systems: Production, Storage, Utilization and the Environment

ANCONA, MARIA ALESSANDRA;BIANCHI, MICHELE;BRANCHINI, LISA;DE PASCALE, ANDREA;MELINO, FRANCESCO;ORLANDINI, VALENTINA;PERETTO, ANTONIO
2015

Abstract

Distributed Generation supported by Smart Grids (SG) and Information and Communication Technology (ICT) can be one of the solutions to actual energy problems such as the continuous rise in price of electricity, the availability of fossil fuels or the integration of conventional energy systems with non-programmable renewable resources. One of the main issues in the managements of smart grids is the optimization of load distribution among the various energy systems which concur to the satisfaction of energy demand. To achieve this goal various optimization techniques have been applied such as genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FFA). In order to develop the load distribution optimization in a smart grid characterized by electrical, thermal, cooling and fuel energy fluxes, a new software has been developed by University of Bologna. The software, named EGO (Energy Grids Optimizer) is able to define the load distribution of a number of energy systems operating into a smart grid with the aim of minimize the total cost of the energy production. The software has been validated by comparing its results with the criterion of equal incremental cost. Further a smart grid case study is presented and analysed, to show the effect of the proposed genetic algorithm method on the minimization of the total cost of energy production.
Proceedings of ASME ATI UIT 2015 Conference on Thermal Energy Systems: Production, Storage, Utilization and the Environment
1
8
Ancona, M. A.; Bianchi, M.; Branchini, L.; De Pascale, A.; Melino, F.; Orlandini, V.; Peretto, A.
File in questo prodotto:
Eventuali allegati, non sono esposti

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: http://hdl.handle.net/11585/543627
 Attenzione

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

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