Tube-based model predictive control approach for real-time operation of energy storage system

Cheng Lyu; Youwei Jia; Zhao Xu

doi:10.1109/SGES51519.2020.00093

Tube-based model predictive control approach for real-time operation of energy storage system

Cheng Lyu, Youwei Jia, Zhao Xu

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

2 Citations (Scopus)

Abstract

Energy storage systems are widely used to complement high renewables and assist in supply-demand balance in smart grids. In practice, lithium-ion battery becomes the most popular due to its relatively long life cycles. However, there are two main challenges for batteries to participate in the real-time operation: 1) the change of battery energy level is across-time coupled; 2) uncertainties are unavoidably arisen in the forecasting process for renewable generation. In this paper, a segmental degradation cost model is proposed for real-time management of lithium-ion batteries. In particular, a tube-based model predictive control (MPC) approach is newly proposed in accommodating the real-time operation of energy storage system. Numerical simulation results demonstrate the effectiveness of the proposed approach.

Original language	English
Title of host publication	2020 International Conference on Smart Grids and Energy Systems (SGES 2020)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	493-497
Number of pages	5
ISBN (Electronic)	9781728185507
ISBN (Print)	9781665448505
DOIs	https://doi.org/10.1109/SGES51519.2020.00093
Publication status	E-pub ahead of print - 5 Mar 2021
Externally published	Yes
Event	2020 International Conference on Smart Grids and Energy Systems, SGES 2020 - Virtual, Perth, Australia Duration: 23 Nov 2020 → 26 Nov 2020

Conference

Conference	2020 International Conference on Smart Grids and Energy Systems, SGES 2020
Country/Territory	Australia
City	Virtual, Perth
Period	23/11/20 → 26/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE

Funding

This work was supported in part by Natural Science Foundation of Guangdong (2019A1515111173), Young Talent Program (Department of Education of Guangdong) (2018KQNCX223), High-level University Fund (G02236002), and National Natural Science Foundation of China (71971183).

Keywords

Battery energy storage system
Real time operation
Tube-based model predictive control

UN SDGs

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

Access to Document

10.1109/SGES51519.2020.00093

Cite this

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title = "Tube-based model predictive control approach for real-time operation of energy storage system",

abstract = "Energy storage systems are widely used to complement high renewables and assist in supply-demand balance in smart grids. In practice, lithium-ion battery becomes the most popular due to its relatively long life cycles. However, there are two main challenges for batteries to participate in the real-time operation: 1) the change of battery energy level is across-time coupled; 2) uncertainties are unavoidably arisen in the forecasting process for renewable generation. In this paper, a segmental degradation cost model is proposed for real-time management of lithium-ion batteries. In particular, a tube-based model predictive control (MPC) approach is newly proposed in accommodating the real-time operation of energy storage system. Numerical simulation results demonstrate the effectiveness of the proposed approach.",

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doi = "10.1109/SGES51519.2020.00093",

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Lyu, C, Jia, Y & Xu, Z 2021, Tube-based model predictive control approach for real-time operation of energy storage system. in 2020 International Conference on Smart Grids and Energy Systems (SGES 2020)., 9364485, Institute of Electrical and Electronics Engineers Inc., pp. 493-497, 2020 International Conference on Smart Grids and Energy Systems, SGES 2020, Virtual, Perth, Australia, 23/11/20. https://doi.org/10.1109/SGES51519.2020.00093

Tube-based model predictive control approach for real-time operation of energy storage system. / Lyu, Cheng; Jia, Youwei; Xu, Zhao.
2020 International Conference on Smart Grids and Energy Systems (SGES 2020). Institute of Electrical and Electronics Engineers Inc., 2021. p. 493-497 9364485.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

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AU - Lyu, Cheng

AU - Jia, Youwei

AU - Xu, Zhao

PY - 2021/3/5

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N2 - Energy storage systems are widely used to complement high renewables and assist in supply-demand balance in smart grids. In practice, lithium-ion battery becomes the most popular due to its relatively long life cycles. However, there are two main challenges for batteries to participate in the real-time operation: 1) the change of battery energy level is across-time coupled; 2) uncertainties are unavoidably arisen in the forecasting process for renewable generation. In this paper, a segmental degradation cost model is proposed for real-time management of lithium-ion batteries. In particular, a tube-based model predictive control (MPC) approach is newly proposed in accommodating the real-time operation of energy storage system. Numerical simulation results demonstrate the effectiveness of the proposed approach.

AB - Energy storage systems are widely used to complement high renewables and assist in supply-demand balance in smart grids. In practice, lithium-ion battery becomes the most popular due to its relatively long life cycles. However, there are two main challenges for batteries to participate in the real-time operation: 1) the change of battery energy level is across-time coupled; 2) uncertainties are unavoidably arisen in the forecasting process for renewable generation. In this paper, a segmental degradation cost model is proposed for real-time management of lithium-ion batteries. In particular, a tube-based model predictive control (MPC) approach is newly proposed in accommodating the real-time operation of energy storage system. Numerical simulation results demonstrate the effectiveness of the proposed approach.

KW - Battery energy storage system

KW - Real time operation

KW - Tube-based model predictive control

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M3 - Conference paper (refereed)

AN - SCOPUS:85102730915

SN - 9781665448505

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EP - 497

BT - 2020 International Conference on Smart Grids and Energy Systems (SGES 2020)

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ER -

Tube-based model predictive control approach for real-time operation of energy storage system

Abstract

Conference

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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