eprintid: 616
rev_number: 31
eprint_status: archive
userid: 7
dir: disk0/00/00/06/16
datestamp: 2011-07-27 08:36:19
lastmod: 2012-04-26 10:50:02
status_changed: 2011-07-27 08:36:19
type: conference_item
metadata_visibility: show
contact_email: alberto.bemporad@imtlucca.it
item_issues_count: 0
creators_name: Damoiseaux, Armand
creators_name: Jokic, Andrej
creators_name: Lazar, Mircea
creators_name: Alessio, Alessandro
creators_name: Van den bosch, Paul
creators_name: Hiskens, Ian
creators_name: Bemporad, Alberto
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: alberto.bemporad@imtlucca.it
title: Assessment of decentralized model predictive control techniques for power networks
ispublished: pub
subjects: QA75
subjects: TK
divisions: CSA
full_text_status: none
pres_type: paper
keywords: Model predictive control; Decentralized control; Distributed control; Power systems
abstract: Model predictive control (MPC) is one of the few advanced control methodologies that have proven to be very successful in real-life control applications. MPC has the capability to guarantee optimality with respect to a de- sired performance cost function, while explicitly taking con- straints into account. Recently, there has been an increas- ing interest in the usage of MPC schemes to control power networks. The major obstacle for implementation lies in the large scale of power networks, which is prohibitive for a centralized approach. In this paper we critically assess and compare the suitability of three model predictive control schemes for controlling power networks. These techniques are analyzed with respect to the following relevant characteristics: the performance of the closed-loop system, which is evaluated and compared to the performance achieved with the classical automatic generation control (AGC) structure; the decentralized implementation, which is investigated in terms of size of the models used for prediction, required measurements and data communication, type of cost function and the computational time required by each algorithm to obtain the control action. Based on the investigated properties mentioned above, the study presented in this paper provides valuable insights that can contribute to the successful decentralized implementation of MPC in real-life electrical power networks.
date: 2008
date_type: published
publication: 16th Power Systems Computation Conference
place_of_pub: 14th - 16th July 2008
event_title: 16th Power Systems Computation Conference
event_location: 14th - 16th July 2008
event_dates: Glasgow, Scotland
event_type: conference
refereed: TRUE
book_title: 16th Power Systems Computation Conference
official_url: http://www.pscc-central.org/uploads/tx_ethpublications/pscc2008_473.pdf
citation:   Damoiseaux, Armand and Jokic, Andrej and Lazar, Mircea and Alessio, Alessandro and Van den bosch, Paul and Hiskens, Ian and Bemporad, Alberto  Assessment of decentralized model predictive control techniques for power networks.  In: 16th Power Systems Computation Conference, Glasgow, Scotland, 14th - 16th July 2008         (2008)