A genetic algorithm approach to optimization of power peaks in an automated warehouse

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/13871

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
dc.contributor	Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.contributor.author	Cárdenas Araújo, Juan José
dc.contributor.author	García Espinosa, Antonio
dc.contributor.author	Romeral Martínez, José Luis
dc.contributor.author	Andrade Rengifo, Fabio
dc.date	2009
dc.identifier.citation	Cardenas, J. [et al.]. A genetic algorithm approach to optimization of power peaks in an automated warehouse. A: IEEE Industrial Electronics Society (IECON). "35th IECON congres". IEEE Press. Institute of Electrical and Electronics Engineers, 2009, p. 3297-3302.
dc.identifier.citation	10.1109/IECON.2009.5415200
dc.identifier.uri	http://hdl.handle.net/2117/13871
dc.language.iso	eng
dc.publisher	IEEE Press. Institute of Electrical and Electronics Engineers
dc.relation	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5415200
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria electrònica::Components electrònics
dc.subject	Warehouses -- Management -- Automatic control
dc.subject	Genetic algorithms
dc.subject	Demand-side management (Electric utilities)
dc.subject	Algorismes genètics
dc.subject	Energia elèctrica -- Demanda
dc.subject	Magatzems -- Control automàtic
dc.title	A genetic algorithm approach to optimization of power peaks in an automated warehouse
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	The simultaneous operation of the automated storage and retrieval machines (ASRs) in an automated warehouse can increase the likelihood that high power demand peaks turn unstable the electric system. Furthermore, high power peaks mean the need for more electrical power contracted, which in turns leads to more fixed operation cost and inefficient use of the electrical installations. In this context, we present a genetic algorithm approach to implement demandside management (DSM) in an automated warehouse. It has been based on real data from ASRs and models of prognosis of load profile of ASRs. We took into account two main goals: minimize instantaneous power demand and keeping the performance of the system store and retrieval times.
dc.description.abstract	Peer Reviewed

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