Risk based robust Model Predictive Control and its application to power plant optimal scheduling

Yutaka Iino

Risk based robust Model Predictive Control and its application to power plant optimal scheduling

Yutaka Iino^*

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

This paper proposes a Model Predictive Control method with stochastic set point and cost parameters. It is applicable to optimal scheduling and load dispatching problem with multiple unit systems such as group control of distributed electric generators. "Value at Risk" type constraint condition, which means a worst-case index, is introduced to realize robustness of optimization against random parameters. Additionally a computation reduction method using orthogonal matrix is newly applied. An example for power system schedule optimization problem is evaluated to show the effectiveness of the proposed method.

Original language	English
Pages	1215-1218
Number of pages	4
Publication status	Published - 2005 Dec 1
Externally published	Yes
Event	SICE Annual Conference 2005 - Okayama, Japan Duration: 2005 Aug 8 → 2005 Aug 10

Conference

Conference	SICE Annual Conference 2005
Country/Territory	Japan
City	Okayama
Period	05/8/8 → 05/8/10

Keywords

Model Predictive Control
Risk sensitivity
Stochastic cost function
Value at risk

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Cite this

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title = "Risk based robust Model Predictive Control and its application to power plant optimal scheduling",

abstract = "This paper proposes a Model Predictive Control method with stochastic set point and cost parameters. It is applicable to optimal scheduling and load dispatching problem with multiple unit systems such as group control of distributed electric generators. {"}Value at Risk{"} type constraint condition, which means a worst-case index, is introduced to realize robustness of optimization against random parameters. Additionally a computation reduction method using orthogonal matrix is newly applied. An example for power system schedule optimization problem is evaluated to show the effectiveness of the proposed method.",

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N2 - This paper proposes a Model Predictive Control method with stochastic set point and cost parameters. It is applicable to optimal scheduling and load dispatching problem with multiple unit systems such as group control of distributed electric generators. "Value at Risk" type constraint condition, which means a worst-case index, is introduced to realize robustness of optimization against random parameters. Additionally a computation reduction method using orthogonal matrix is newly applied. An example for power system schedule optimization problem is evaluated to show the effectiveness of the proposed method.

AB - This paper proposes a Model Predictive Control method with stochastic set point and cost parameters. It is applicable to optimal scheduling and load dispatching problem with multiple unit systems such as group control of distributed electric generators. "Value at Risk" type constraint condition, which means a worst-case index, is introduced to realize robustness of optimization against random parameters. Additionally a computation reduction method using orthogonal matrix is newly applied. An example for power system schedule optimization problem is evaluated to show the effectiveness of the proposed method.

KW - Model Predictive Control

KW - Risk sensitivity

KW - Stochastic cost function

KW - Value at risk

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ER -

Risk based robust Model Predictive Control and its application to power plant optimal scheduling

Abstract

Conference

Keywords

ASJC Scopus subject areas

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