Robust Model Predictive Control of Large-Scale Power Network with Moving Horizon Estimation

Toru Namerikawa

doi:10.1109/CCTA54093.2023.10253394

Robust Model Predictive Control of Large-Scale Power Network with Moving Horizon Estimation

Toru Namerikawa

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, we discuss the load frequency control problem for large-scale power network systems. The control is conducted to preserve the demand-supply power balance by controlling the output of generators in a power network. However, it becomes difficult to retain the balance because of the injection of a large amount of renewable energy whose output is uncertain. This paper proposes a new control application framework, especially focused on a large-scale connected power network system that contains a large-volume wind turbine generator, thermal turbine generator, and battery. More concretely, to satisfy the constraints of state and input control, we propose a method of load frequency control using moving horizon estimation and robust model predictive control.

Original language	English
Title of host publication	2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	823-828
Number of pages	6
ISBN (Electronic)	9798350335446
DOIs	https://doi.org/10.1109/CCTA54093.2023.10253394
Publication status	Published - 2023
Event	2023 IEEE Conference on Control Technology and Applications, CCTA 2023 - Bridgetown, Barbados Duration: 2023 Aug 16 → 2023 Aug 18

Publication series

Name	2023 IEEE Conference on Control Technology and Applications, CCTA 2023

Conference

Conference	2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Country/Territory	Barbados
City	Bridgetown
Period	23/8/16 → 23/8/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

ASJC Scopus subject areas

Computer Science Applications
Control and Systems Engineering
Control and Optimization

Access to Document

10.1109/CCTA54093.2023.10253394

Cite this

Namerikawa, T. (2023). Robust Model Predictive Control of Large-Scale Power Network with Moving Horizon Estimation. In 2023 IEEE Conference on Control Technology and Applications, CCTA 2023 (pp. 823-828). (2023 IEEE Conference on Control Technology and Applications, CCTA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCTA54093.2023.10253394

Robust Model Predictive Control of Large-Scale Power Network with Moving Horizon Estimation. / Namerikawa, Toru.
2023 IEEE Conference on Control Technology and Applications, CCTA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 823-828 (2023 IEEE Conference on Control Technology and Applications, CCTA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Namerikawa, T 2023, Robust Model Predictive Control of Large-Scale Power Network with Moving Horizon Estimation. in 2023 IEEE Conference on Control Technology and Applications, CCTA 2023. 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 823-828, 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Bridgetown, Barbados, 23/8/16. https://doi.org/10.1109/CCTA54093.2023.10253394

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abstract = "In this paper, we discuss the load frequency control problem for large-scale power network systems. The control is conducted to preserve the demand-supply power balance by controlling the output of generators in a power network. However, it becomes difficult to retain the balance because of the injection of a large amount of renewable energy whose output is uncertain. This paper proposes a new control application framework, especially focused on a large-scale connected power network system that contains a large-volume wind turbine generator, thermal turbine generator, and battery. More concretely, to satisfy the constraints of state and input control, we propose a method of load frequency control using moving horizon estimation and robust model predictive control.",

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AB - In this paper, we discuss the load frequency control problem for large-scale power network systems. The control is conducted to preserve the demand-supply power balance by controlling the output of generators in a power network. However, it becomes difficult to retain the balance because of the injection of a large amount of renewable energy whose output is uncertain. This paper proposes a new control application framework, especially focused on a large-scale connected power network system that contains a large-volume wind turbine generator, thermal turbine generator, and battery. More concretely, to satisfy the constraints of state and input control, we propose a method of load frequency control using moving horizon estimation and robust model predictive control.

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BT - 2023 IEEE Conference on Control Technology and Applications, CCTA 2023

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ER -

Robust Model Predictive Control of Large-Scale Power Network with Moving Horizon Estimation

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Access to Document

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