Kalman filter-based power compensation strategy for Microgrids under uncertain disturbance

Jingxuan Wu; Josep M. Guerrero; Amir Basati; Juan C. Vasquez; Shuting Li

doi:10.1109/SEST53650.2022.9898466

Kalman filter-based power compensation strategy for Microgrids under uncertain disturbance

Jingxuan Wu^*, Josep M. Guerrero, Amir Basati, Juan C. Vasquez, Shuting Li

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

3 Citations (Scopus)

Abstract

The measurement and model errors always exist in the sample or data analysis part of Microgrids (MGs) system, bringing oscillation and inaccuracy to the controller and energy management system (EMS). This paper proposed a Kalman
filter (KF)-based control strategy for the battery energy storage system (BESS) in MGs with integrated renewable energy sources. The control scheme aims at minimizing the active and reactive power purchased from the main grid by regulating the output current of BESS. A detailed model of a wind-PV-battery
MG is built, and the sampling and measurement inaccuracy is considered. The control strategy is tested under rapid power supply oscillation and load changes, and the results show good performance.

Original language	English
Title of host publication	SEST 2022 - 5th International Conference on Smart Energy Systems and Technologies : SEST 2022
Publisher	IEEE
Publication date	28 Sept 2022
ISBN (Electronic)	9781665405577
DOIs	https://doi.org/10.1109/SEST53650.2022.9898466
Publication status	Published - 28 Sept 2022

Keywords

Battery Energy Storage
Kalman filter (KF)
Microgird
Renewable

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Cite this

@inproceedings{b4487e02ae8a4a4fb73ead6cdf49ec01,

title = "Kalman filter-based power compensation strategy for Microgrids under uncertain disturbance",

abstract = "The measurement and model errors always exist in the sample or data analysis part of Microgrids (MGs) system, bringing oscillation and inaccuracy to the controller and energy management system (EMS). This paper proposed a Kalmanfilter (KF)-based control strategy for the battery energy storage system (BESS) in MGs with integrated renewable energy sources. The control scheme aims at minimizing the active and reactive power purchased from the main grid by regulating the output current of BESS. A detailed model of a wind-PV-batteryMG is built, and the sampling and measurement inaccuracy is considered. The control strategy is tested under rapid power supply oscillation and load changes, and the results show good performance.",

keywords = "Battery Energy Storage, Kalman filter (KF), Microgird, Renewable",

author = "Jingxuan Wu and Guerrero, {Josep M.} and Amir Basati and Vasquez, {Juan C.} and Shuting Li",

year = "2022",

month = sep,

day = "28",

doi = "10.1109/SEST53650.2022.9898466",

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Kalman filter-based power compensation strategy for Microgrids under uncertain disturbance. / Wu, Jingxuan ; Guerrero, Josep M.; Basati, Amir et al.
SEST 2022 - 5th International Conference on Smart Energy Systems and Technologies: SEST 2022. IEEE, 2022.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Kalman filter-based power compensation strategy for Microgrids under uncertain disturbance

AU - Wu, Jingxuan

AU - Guerrero, Josep M.

AU - Basati, Amir

AU - Vasquez, Juan C.

AU - Li, Shuting

PY - 2022/9/28

Y1 - 2022/9/28

N2 - The measurement and model errors always exist in the sample or data analysis part of Microgrids (MGs) system, bringing oscillation and inaccuracy to the controller and energy management system (EMS). This paper proposed a Kalmanfilter (KF)-based control strategy for the battery energy storage system (BESS) in MGs with integrated renewable energy sources. The control scheme aims at minimizing the active and reactive power purchased from the main grid by regulating the output current of BESS. A detailed model of a wind-PV-batteryMG is built, and the sampling and measurement inaccuracy is considered. The control strategy is tested under rapid power supply oscillation and load changes, and the results show good performance.

AB - The measurement and model errors always exist in the sample or data analysis part of Microgrids (MGs) system, bringing oscillation and inaccuracy to the controller and energy management system (EMS). This paper proposed a Kalmanfilter (KF)-based control strategy for the battery energy storage system (BESS) in MGs with integrated renewable energy sources. The control scheme aims at minimizing the active and reactive power purchased from the main grid by regulating the output current of BESS. A detailed model of a wind-PV-batteryMG is built, and the sampling and measurement inaccuracy is considered. The control strategy is tested under rapid power supply oscillation and load changes, and the results show good performance.

KW - Battery Energy Storage

KW - Kalman filter (KF)

KW - Microgird

KW - Renewable

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U2 - 10.1109/SEST53650.2022.9898466

DO - 10.1109/SEST53650.2022.9898466

M3 - Article in proceeding

BT - SEST 2022 - 5th International Conference on Smart Energy Systems and Technologies

PB - IEEE

ER -

Kalman filter-based power compensation strategy for Microgrids under uncertain disturbance

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