Model Predictive Control Analysis for the Battery Energy Storage System

Nimat Shamim; Anitha Subburaj; Stephen Bayne

doi:10.1109/GreenTech.2017.11

Model Predictive Control Analysis for the Battery Energy Storage System

Nimat Shamim, Anitha Subburaj, Stephen Bayne

Electrical and Computer Engineering

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

3 Scopus citations

Abstract

This paper describes the Model Predictive Control technique for three phase bi-directional converter to integrate a battery system with the grid. The paper presents an overview of different predictive control technologies. The paper describes the basic concept, operating principle, governing equations and control algorithm of model predictive control for the power converter. The control technique is analyzed to integrate a 1MWh battery system model with the grid. The analysis is done in PSCAD simulation environment for both steady state and fault scenarios. The simulation results are presented to show the effectiveness of Model Predictive Control technology for battery integration.

Original language	English
Title of host publication	Proceedings - 2017 9th Annual IEEE Green Technologies Conference, GreenTech 2017
Publisher	IEEE Computer Society
Pages	34-38
Number of pages	5
ISBN (Electronic)	9781509045358
DOIs	https://doi.org/10.1109/GreenTech.2017.11
State	Published - May 9 2017
Event	9th Annual IEEE Green Technologies Conference, GreenTech 2017 - Denver, United States Duration: Mar 29 2017 → Mar 31 2017

Publication series

Name	IEEE Green Technologies Conference
ISSN (Electronic)	2166-5478

Conference

Conference	9th Annual IEEE Green Technologies Conference, GreenTech 2017
Country/Territory	United States
City	Denver
Period	03/29/17 → 03/31/17

Keywords

Battery System
Model Predictive Control
PSCAD
Three Phase Converter

Access to Document

10.1109/GreenTech.2017.11

Cite this

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title = "Model Predictive Control Analysis for the Battery Energy Storage System",

abstract = "This paper describes the Model Predictive Control technique for three phase bi-directional converter to integrate a battery system with the grid. The paper presents an overview of different predictive control technologies. The paper describes the basic concept, operating principle, governing equations and control algorithm of model predictive control for the power converter. The control technique is analyzed to integrate a 1MWh battery system model with the grid. The analysis is done in PSCAD simulation environment for both steady state and fault scenarios. The simulation results are presented to show the effectiveness of Model Predictive Control technology for battery integration.",

keywords = "Battery System, Model Predictive Control, PSCAD, Three Phase Converter",

author = "Nimat Shamim and Anitha Subburaj and Stephen Bayne",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th Annual IEEE Green Technologies Conference, GreenTech 2017 ; Conference date: 29-03-2017 Through 31-03-2017",

year = "2017",

month = may,

day = "9",

doi = "10.1109/GreenTech.2017.11",

language = "English",

series = "IEEE Green Technologies Conference",

publisher = "IEEE Computer Society",

pages = "34--38",

booktitle = "Proceedings - 2017 9th Annual IEEE Green Technologies Conference, GreenTech 2017",

}

Shamim, N, Subburaj, A & Bayne, S 2017, Model Predictive Control Analysis for the Battery Energy Storage System. in Proceedings - 2017 9th Annual IEEE Green Technologies Conference, GreenTech 2017., 7923935, IEEE Green Technologies Conference, IEEE Computer Society, pp. 34-38, 9th Annual IEEE Green Technologies Conference, GreenTech 2017, Denver, United States, 03/29/17. https://doi.org/10.1109/GreenTech.2017.11

Model Predictive Control Analysis for the Battery Energy Storage System. / Shamim, Nimat; Subburaj, Anitha; Bayne, Stephen.
Proceedings - 2017 9th Annual IEEE Green Technologies Conference, GreenTech 2017. IEEE Computer Society, 2017. p. 34-38 7923935 (IEEE Green Technologies Conference).

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

TY - GEN

T1 - Model Predictive Control Analysis for the Battery Energy Storage System

AU - Shamim, Nimat

AU - Subburaj, Anitha

AU - Bayne, Stephen

PY - 2017/5/9

Y1 - 2017/5/9

N2 - This paper describes the Model Predictive Control technique for three phase bi-directional converter to integrate a battery system with the grid. The paper presents an overview of different predictive control technologies. The paper describes the basic concept, operating principle, governing equations and control algorithm of model predictive control for the power converter. The control technique is analyzed to integrate a 1MWh battery system model with the grid. The analysis is done in PSCAD simulation environment for both steady state and fault scenarios. The simulation results are presented to show the effectiveness of Model Predictive Control technology for battery integration.

AB - This paper describes the Model Predictive Control technique for three phase bi-directional converter to integrate a battery system with the grid. The paper presents an overview of different predictive control technologies. The paper describes the basic concept, operating principle, governing equations and control algorithm of model predictive control for the power converter. The control technique is analyzed to integrate a 1MWh battery system model with the grid. The analysis is done in PSCAD simulation environment for both steady state and fault scenarios. The simulation results are presented to show the effectiveness of Model Predictive Control technology for battery integration.

KW - Battery System

KW - Model Predictive Control

KW - PSCAD

KW - Three Phase Converter

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M3 - Conference contribution

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BT - Proceedings - 2017 9th Annual IEEE Green Technologies Conference, GreenTech 2017

PB - IEEE Computer Society

T2 - 9th Annual IEEE Green Technologies Conference, GreenTech 2017

Y2 - 29 March 2017 through 31 March 2017

ER -

Model Predictive Control Analysis for the Battery Energy Storage System

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Keywords

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