Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

Kristian Skjoldborg Edlund, Jan Dimon Bendtsen, Simon Børresen, Tommy Mølbak

Research output: Contribution to journalConference article in JournalResearchpeer-review

16 Citations (Scopus)

Abstract

This paper introduces a model predictive control (MPC) approach for construction
of a controller for balancing the power generation against consumption in a power system.
The objective of the controller is to coordinate a portfolio consisting of multiple power plant
units in the effort to perform reference tracking and disturbance rejection in an economically
optimal way. The performance function is chosen as a mixture of the `1-norm and a linear
weighting to model the economics of the system. Simulations show a significant improvement of
the performance of the MPC compared to the current implementation consisting of a distributed
PI controller structure, both in terms of minimising the overall cost but also in terms of the
ability to minimise deviation, which is the classical objective.
Original languageEnglish
Book seriesElsevier IFAC Publications / IFAC Proceedings series
Issue number1
ISSN1474-6670
Publication statusPublished - 2008
Event17th IFAC World Congress - Seoul, Korea, Republic of
Duration: 6 Jul 200811 Jul 2008
Conference number: 17

Conference

Conference17th IFAC World Congress
Number17
CountryKorea, Republic of
CitySeoul
Period06/07/200811/07/2008

Fingerprint

Model predictive control
Power plants
Controllers
Disturbance rejection
Power generation
Economics
Costs

Cite this

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title = "Introducing Model Predictive Control for Improving Power Plant Portfolio Performance",
abstract = "This paper introduces a model predictive control (MPC) approach for constructionof a controller for balancing the power generation against consumption in a power system.The objective of the controller is to coordinate a portfolio consisting of multiple power plantunits in the effort to perform reference tracking and disturbance rejection in an economicallyoptimal way. The performance function is chosen as a mixture of the `1-norm and a linearweighting to model the economics of the system. Simulations show a significant improvement ofthe performance of the MPC compared to the current implementation consisting of a distributedPI controller structure, both in terms of minimising the overall cost but also in terms of theability to minimise deviation, which is the classical objective.",
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Introducing Model Predictive Control for Improving Power Plant Portfolio Performance. / Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon; Mølbak, Tommy.

In: Elsevier IFAC Publications / IFAC Proceedings series, No. 1, 2008.

Research output: Contribution to journalConference article in JournalResearchpeer-review

TY - GEN

T1 - Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

AU - Edlund, Kristian Skjoldborg

AU - Bendtsen, Jan Dimon

AU - Børresen, Simon

AU - Mølbak, Tommy

N1 - Volumne: 17

PY - 2008

Y1 - 2008

N2 - This paper introduces a model predictive control (MPC) approach for constructionof a controller for balancing the power generation against consumption in a power system.The objective of the controller is to coordinate a portfolio consisting of multiple power plantunits in the effort to perform reference tracking and disturbance rejection in an economicallyoptimal way. The performance function is chosen as a mixture of the `1-norm and a linearweighting to model the economics of the system. Simulations show a significant improvement ofthe performance of the MPC compared to the current implementation consisting of a distributedPI controller structure, both in terms of minimising the overall cost but also in terms of theability to minimise deviation, which is the classical objective.

AB - This paper introduces a model predictive control (MPC) approach for constructionof a controller for balancing the power generation against consumption in a power system.The objective of the controller is to coordinate a portfolio consisting of multiple power plantunits in the effort to perform reference tracking and disturbance rejection in an economicallyoptimal way. The performance function is chosen as a mixture of the `1-norm and a linearweighting to model the economics of the system. Simulations show a significant improvement ofthe performance of the MPC compared to the current implementation consisting of a distributedPI controller structure, both in terms of minimising the overall cost but also in terms of theability to minimise deviation, which is the classical objective.

M3 - Conference article in Journal

JO - I F A C Workshop Series

JF - I F A C Workshop Series

SN - 1474-6670

IS - 1

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