Modelling the effects of low-cost large-scale energy storage in the UK electricity network

Edward Barbour; Andrew Pimm; David Parra

doi:10.1109/OSES.2019.8867348

Modelling the effects of low-cost large-scale energy storage in the UK electricity network

Edward Barbour
, Andrew Pimm
, David Parra

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

Abstract

In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.

Original language	English
Title of host publication	2019 Offshore Energy and Storage Summit, OSES 2019
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781728123172
DOIs	https://doi.org/10.1109/OSES.2019.8867348
Publication status	Published - Jul 2019
Event	2019 Offshore Energy and Storage Summit, OSES 2019 - Brest, France Duration: 10 Jul 2019 → 12 Jul 2019

Publication series

Name	2019 Offshore Energy and Storage Summit, OSES 2019

Conference

Conference	2019 Offshore Energy and Storage Summit, OSES 2019
Country/Territory	France
City	Brest
Period	10/07/19 → 12/07/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Carbon emissions
Energy storage
MILP
Nonlinear optimization
Optimization
UK electricity system

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Oceanography
Energy Engineering and Power Technology

Access to Document

10.1109/OSES.2019.8867348

Cite this

@inproceedings{b8e5b052fbfa4366a1617a4ad13e31d2,

title = "Modelling the effects of low-cost large-scale energy storage in the UK electricity network",

abstract = "In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.",

keywords = "Carbon emissions, Energy storage, MILP, Nonlinear optimization, Optimization, UK electricity system",

author = "Edward Barbour and Andrew Pimm and David Parra",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Offshore Energy and Storage Summit, OSES 2019 ; Conference date: 10-07-2019 Through 12-07-2019",

year = "2019",

month = jul,

doi = "10.1109/OSES.2019.8867348",

language = "English",

series = "2019 Offshore Energy and Storage Summit, OSES 2019",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

booktitle = "2019 Offshore Energy and Storage Summit, OSES 2019",

}

Barbour, E, Pimm, A & Parra, D 2019, Modelling the effects of low-cost large-scale energy storage in the UK electricity network. in 2019 Offshore Energy and Storage Summit, OSES 2019., 8867348, 2019 Offshore Energy and Storage Summit, OSES 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019 Offshore Energy and Storage Summit, OSES 2019, Brest, France, 10/07/19. https://doi.org/10.1109/OSES.2019.8867348

Modelling the effects of low-cost large-scale energy storage in the UK electricity network. / Barbour, Edward; Pimm, Andrew; Parra, David.
2019 Offshore Energy and Storage Summit, OSES 2019. Institute of Electrical and Electronics Engineers (IEEE), 2019. 8867348 (2019 Offshore Energy and Storage Summit, OSES 2019).

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

TY - GEN

T1 - Modelling the effects of low-cost large-scale energy storage in the UK electricity network

AU - Barbour, Edward

AU - Pimm, Andrew

AU - Parra, David

PY - 2019/7

Y1 - 2019/7

N2 - In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.

AB - In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.

KW - Carbon emissions

KW - Energy storage

KW - MILP

KW - Nonlinear optimization

KW - Optimization

KW - UK electricity system

UR - https://www.scopus.com/pages/publications/85074270574

U2 - 10.1109/OSES.2019.8867348

DO - 10.1109/OSES.2019.8867348

M3 - Conference contribution

AN - SCOPUS:85074270574

T3 - 2019 Offshore Energy and Storage Summit, OSES 2019

BT - 2019 Offshore Energy and Storage Summit, OSES 2019

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2019 Offshore Energy and Storage Summit, OSES 2019

Y2 - 10 July 2019 through 12 July 2019

ER -

Modelling the effects of low-cost large-scale energy storage in the UK electricity network

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this