Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynam...

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Vydáno v:IEEE transactions on smart grid Ročník 9; číslo 3; s. 2276 - 2283
Hlavní autoři: Kampouropoulos, Konstantinos, Andrade, Fabio, Sala, Enric, Espinosa, Antonio Garcia, Romeral, Luis
Médium: Journal Article Publikace
Jazyk:angličtina
Vydáno: IEEE 01.05.2018
Institute of Electrical and Electronics Engineers (IEEE)
Témata:
Dynamic optimization
Electrical engineering
Energy hub
Energy optimization
Energy prediction
Enginyeria elèctrica
Genetic algorithms
Indexes
Linear programming
Manufacturing plants
Mathematical model
Mixed-integer programming
Multicarrier systems
Multiobjective problem
Optimal control
Optimization
Power demand
Predictive models
Àrees temàtiques de la UPC
ISSN:1949-3053, 1949-3061
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Abstract This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment's thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost, and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.
AbstractList This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment’s thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.
Author Sala, Enric
Espinosa, Antonio Garcia
Kampouropoulos, Konstantinos
Andrade, Fabio
Romeral, Luis
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Cites_doi 10.1016/j.ijepes.2014.08.020
10.1109/ISIE.2013.6563627
10.1109/TSTE.2013.2267235
10.1109/TSTE.2013.2271517
10.1109/TPWRS.2013.2283259
10.1109/NAPS.2009.5484065
10.1109/21.256541
10.1109/TPWRD.2014.2365491
10.7148/2015-0259
10.1080/17445760.2014.974600
10.1109/PESC.2004.1354758
10.1109/UPEC.2014.6934602
10.1002/etep.112
10.1109/MPAE.2007.264850
10.1109/TSG.2014.2377020
10.1016/0360-5442(95)00067-Q
10.1016/j.ijhydene.2006.10.013
10.1109/PCT.2007.4538520
10.1109/TPWRS.2006.888988
10.1016/j.energy.2015.05.109
10.1007/s00158-003-0368-6
10.1109/FPS.2005.204199
10.1109/TSTE.2011.2106228
10.1109/IREP.2007.4410517
10.1109/EEEIC.2010.5489999
10.1109/PES.2010.5589763
ContentType Journal Article
Publication
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Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
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References ref13
ref34
ref12
ref15
ref31
ref30
ref32
ref10
(ref1) 2008
ref2
ref17
ref16
frik (ref8) 2004
ref19
ref18
ref24
ref23
geidl (ref14) 2005
ref26
ref25
bouwmans (ref11) 2002
ref20
bahrami (ref28) 2015; 7
ref22
(ref33) 2016
ref21
ref27
ref29
geidl (ref6) 2007
ref7
geidl (ref9) 2006; 41
ref4
ref3
ref5
References_xml – ident: ref26
  doi: 10.1016/j.ijepes.2014.08.020
– ident: ref32
  doi: 10.1109/ISIE.2013.6563627
– ident: ref22
  doi: 10.1109/TSTE.2013.2267235
– ident: ref13
  doi: 10.1109/TSTE.2013.2271517
– ident: ref16
  doi: 10.1109/TPWRS.2013.2283259
– ident: ref18
  doi: 10.1109/NAPS.2009.5484065
– start-page: 1
  year: 2005
  ident: ref14
  article-title: Optimal power dispatch and conversion in systems with multiple energy carriers
  publication-title: Proc 15th Power Syst Comput Conf
– ident: ref31
  doi: 10.1109/21.256541
– ident: ref3
  doi: 10.1109/TPWRD.2014.2365491
– ident: ref34
  doi: 10.7148/2015-0259
– year: 2016
  ident: ref33
  publication-title: Apros-Dynamic Process Simulation Software for Nuclear and Thermal Power Plant Applications
– ident: ref25
  doi: 10.1080/17445760.2014.974600
– start-page: 13
  year: 2007
  ident: ref6
  article-title: The energy hub-A powerful concept for future energy systems
  publication-title: Proc 3rd Annu Carnegie Mellon Conf Elect Ind
– volume: 41
  year: 2006
  ident: ref9
  article-title: A greenfield approach for future power systems
  publication-title: CIGRE Gen Session
– year: 2004
  ident: ref8
  article-title: Proposal for a multifunctional energy bus and its interlink with generation and consumption
– ident: ref12
  doi: 10.1109/PESC.2004.1354758
– ident: ref2
  doi: 10.1109/UPEC.2014.6934602
– ident: ref20
  doi: 10.1002/etep.112
– ident: ref5
  doi: 10.1109/MPAE.2007.264850
– year: 2008
  ident: ref1
  publication-title: 20 20 by 2020 Europe's Climate Change Opportunity
– ident: ref27
  doi: 10.1109/TSG.2014.2377020
– ident: ref10
  doi: 10.1016/0360-5442(95)00067-Q
– ident: ref7
  doi: 10.1016/j.ijhydene.2006.10.013
– ident: ref23
  doi: 10.1109/PCT.2007.4538520
– ident: ref4
  doi: 10.1109/TPWRS.2006.888988
– ident: ref29
  doi: 10.1016/j.energy.2015.05.109
– ident: ref30
  doi: 10.1007/s00158-003-0368-6
– ident: ref19
  doi: 10.1109/FPS.2005.204199
– ident: ref21
  doi: 10.1109/TSTE.2011.2106228
– volume: 7
  start-page: 650
  year: 2015
  ident: ref28
  article-title: From demand response in smart grid toward integrated demand response in smart energy hub
  publication-title: IEEE Trans Smart Grid
– ident: ref15
  doi: 10.1109/IREP.2007.4410517
– start-page: 1
  year: 2002
  ident: ref11
  article-title: Optimising energy systems-Hydrogen and distributed generation
  publication-title: Proc 2nd Int Symp Distrib Gener Power Syst Mark Aspects
– ident: ref17
  doi: 10.1109/EEEIC.2010.5489999
– ident: ref24
  doi: 10.1109/PES.2010.5589763
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Snippet This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference...
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SubjectTerms Dynamic optimization
Electrical engineering
Energy hub
Energy optimization
Energy prediction
Enginyeria elèctrica
Genetic algorithms
Indexes
Linear programming
Manufacturing plants
Mathematical model
Mixed-integer programming
Multicarrier systems
Multiobjective problem
Optimal control
Optimization
Power demand
Predictive models
Àrees temàtiques de la UPC
Title Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms
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