Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

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

Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

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

Research output: Contribution to journal › Conference article in Journal › Research › peer-review

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 language	English
Book series	Elsevier IFAC Publications / IFAC Proceedings series
Issue number	1
ISSN	1474-6670
Publication status	Published - 2008
Event	17th IFAC World Congress - Seoul, Korea, Republic of Duration: 6 Jul 2008 → 11 Jul 2008 Conference number: 17

Conference

Conference	17th IFAC World Congress
Number	17
Country/Territory	Korea, Republic of
City	Seoul
Period	06/07/2008 → 11/07/2008

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Dynamical production optimization of a power plant portfolio - With regards to economy and availability.
Edlund, K. S., Bendtsen, J. D. & Andersen, P.
<ingen navn>
01/04/2007 → 01/04/2010
Project: Research

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.",

author = "Edlund, {Kristian Skjoldborg} and Bendtsen, {Jan Dimon} and Simon B{\o}rresen and Tommy M{\o}lbak",

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AU - Edlund, Kristian Skjoldborg

AU - Bendtsen, Jan Dimon

AU - Børresen, Simon

AU - Mølbak, Tommy

N1 - Conference code: 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

SN - 1474-6670

JO - Elsevier IFAC Publications / IFAC Proceedings series

JF - Elsevier IFAC Publications / IFAC Proceedings series

IS - 1

T2 - 17th IFAC World Congress

Y2 - 6 July 2008 through 11 July 2008

ER -

Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

Abstract

Conference

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Projects

Dynamical production optimization of a power plant portfolio - With regards to economy and availability.

Cite this