Real-Time Model Predictive Control of Battery Energy Storage Active and Reactive Power to Support the Distribution Network Operation

Ahmed A.Raouf Mohamed; D John Morrow; Robert J. Best

doi:10.1049/icp.2021.1356

Real-Time Model Predictive Control of Battery Energy Storage Active and Reactive Power to Support the Distribution Network Operation

Ahmed A.Raouf Mohamed^*
, D John Morrow
, Robert J. Best

^*Corresponding author for this work

School of Electronics, Electrical Engineering and Computer Science

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

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Abstract

This paper proposes a model predictive control technique to optimally dispatch of battery energy storage systems (BESS) installed on the medium voltage distribution network to manage the violations in addition to enhancing the power quality and stability. A two-phase strategy is developed to manage the BESS inverter power on the four active/reactive power quadrants. A multiobjective function is formulated in order to optimize the system voltage, power factor and line losses. The uncertainties associated with demand and generation forecasting are considered in the proposed strategy by introducing a real-time operational phase. The network, BESS, and inverter technical constraints are considered, and the proposed strategy is validated by simulating different scenarios on an 11 kV, 53-node distribution network located in Northern Ireland.

Original language	English
DOIs	https://doi.org/10.1049/icp.2021.1356
Publication status	Accepted - 01 Nov 2020
Event	The 9th International Conference on Renewable Power Generation - Dublin, Ireland Duration: 01 Mar 2021 → … Conference number: 9 https://rpg.theiet.org/

Conference

Conference	The 9th International Conference on Renewable Power Generation
Abbreviated title	IET RPG 2021
Country/Territory	Ireland
City	Dublin
Period	01/03/2021 → …
Internet address	https://rpg.theiet.org/

Keywords

Battery Energy Storage System
Distribution Networks
Model Predictive Control
Inverter
Optimization

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10.1049/icp.2021.1356

Real-Time Model Predictive Control of Battery Energy Storage Active and Reactive Power to Support the Distribution Network Operation
Copyright 2021the Author. This manuscript is distributed under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Accepted author manuscript, 305 KBLicence: CC BY-NC-ND

Cite this

@conference{315d96927e4c4bb6b2b7da7ae504ff08,

title = "Real-Time Model Predictive Control of Battery Energy Storage Active and Reactive Power to Support the Distribution Network Operation",

abstract = "This paper proposes a model predictive control technique to optimally dispatch of battery energy storage systems (BESS) installed on the medium voltage distribution network to manage the violations in addition to enhancing the power quality and stability. A two-phase strategy is developed to manage the BESS inverter power on the four active/reactive power quadrants. A multiobjective function is formulated in order to optimize the system voltage, power factor and line losses. The uncertainties associated with demand and generation forecasting are considered in the proposed strategy by introducing a real-time operational phase. The network, BESS, and inverter technical constraints are considered, and the proposed strategy is validated by simulating different scenarios on an 11 kV, 53-node distribution network located in Northern Ireland.",

keywords = "Battery Energy Storage System, Distribution Networks, Model Predictive Control, Inverter, Optimization",

author = "Mohamed, \{Ahmed A.Raouf\} and Morrow, \{D John\} and Best, \{Robert J.\}",

year = "2020",

month = nov,

day = "1",

doi = "10.1049/icp.2021.1356",

language = "English",

note = "The 9th International Conference on Renewable Power Generation, IET RPG 2021 ; Conference date: 01-03-2021",

url = "https://rpg.theiet.org/",

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T1 - Real-Time Model Predictive Control of Battery Energy Storage Active and Reactive Power to Support the Distribution Network Operation

AU - Mohamed, Ahmed A.Raouf

AU - Morrow, D John

AU - Best, Robert J.

N1 - Conference code: 9

PY - 2020/11/1

Y1 - 2020/11/1

N2 - This paper proposes a model predictive control technique to optimally dispatch of battery energy storage systems (BESS) installed on the medium voltage distribution network to manage the violations in addition to enhancing the power quality and stability. A two-phase strategy is developed to manage the BESS inverter power on the four active/reactive power quadrants. A multiobjective function is formulated in order to optimize the system voltage, power factor and line losses. The uncertainties associated with demand and generation forecasting are considered in the proposed strategy by introducing a real-time operational phase. The network, BESS, and inverter technical constraints are considered, and the proposed strategy is validated by simulating different scenarios on an 11 kV, 53-node distribution network located in Northern Ireland.

AB - This paper proposes a model predictive control technique to optimally dispatch of battery energy storage systems (BESS) installed on the medium voltage distribution network to manage the violations in addition to enhancing the power quality and stability. A two-phase strategy is developed to manage the BESS inverter power on the four active/reactive power quadrants. A multiobjective function is formulated in order to optimize the system voltage, power factor and line losses. The uncertainties associated with demand and generation forecasting are considered in the proposed strategy by introducing a real-time operational phase. The network, BESS, and inverter technical constraints are considered, and the proposed strategy is validated by simulating different scenarios on an 11 kV, 53-node distribution network located in Northern Ireland.

KW - Battery Energy Storage System

KW - Distribution Networks

KW - Model Predictive Control

KW - Inverter

KW - Optimization

UR - https://rpg.theiet.org/

U2 - 10.1049/icp.2021.1356

DO - 10.1049/icp.2021.1356

M3 - Paper

T2 - The 9th International Conference on Renewable Power Generation

Y2 - 1 March 2021

ER -

Real-Time Model Predictive Control of Battery Energy Storage Active and Reactive Power to Support the Distribution Network Operation

Abstract

Conference

Keywords

Access to Document

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R6966ELE: SPIRE 2 - Storage Platform for the Integration of Renewable Energy

The integration of battery energy storage systems in modern distribution networks

Cite this

Real-Time Model Predictive Control of Battery Energy Storage Active and Reactive Power to Support the Distribution Network Operation

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

R6966ELE: SPIRE 2 - Storage Platform for the Integration of Renewable Energy

Student theses

The integration of battery energy storage systems in modern distribution networks

Cite this