Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo

Ali Ehsan; Robin Preece; Seyed Hamid Reza Hosseini; Adib Allahham; Phil Taylor

doi:10.1109/PMAPS47429.2020.9183604

Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo

Ali Ehsan, Robin Preece, Seyed Hamid Reza Hosseini, Adib Allahham, Phil Taylor

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

3 Citations (Scopus)

Abstract

This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-To-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.

Original language	English
Title of host publication	2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781728128221
DOIs	https://doi.org/10.1109/PMAPS47429.2020.9183604
Publication status	Published - Aug 2020
Event	2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Liege, Belgium Duration: 18 Aug 2020 → 21 Aug 2020

Publication series

Name	2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings

Conference

Conference	2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020
Country/Territory	Belgium
City	Liege
Period	18/08/20 → 21/08/20

Bibliographical note

Funding Information:
This work was supported by EPSRC through the Supergen Energy Networks Hub project under Grant EP/S00078X/1 and EPSRC National Centre for Energy Systems Integration under Grant EP/P01173/1.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

electricity
gas
muli-vector
network
probabilistic
propagation
uncertainty

UN SDGs

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

Access to Document

10.1109/PMAPS47429.2020.9183604

Handle.net

Persistent link

Cite this

Ehsan, A., Preece, R., Reza Hosseini, S. H., Allahham, A., & Taylor, P. (2020). Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo. In 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings Article 9183604 (2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/PMAPS47429.2020.9183604

Ehsan, Ali ; Preece, Robin ; Reza Hosseini, Seyed Hamid et al. / Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo. 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2020. (2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings).

@inproceedings{5e7029ac85f647288bb8f781ec735c97,

title = "Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo",

abstract = "This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-To-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.",

keywords = "electricity, gas, muli-vector, network, probabilistic, propagation, uncertainty",

author = "Ali Ehsan and Robin Preece and {Reza Hosseini}, {Seyed Hamid} and Adib Allahham and Phil Taylor",

note = "Funding Information: This work was supported by EPSRC through the Supergen Energy Networks Hub project under Grant EP/S00078X/1 and EPSRC National Centre for Energy Systems Integration under Grant EP/P01173/1. Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 ; Conference date: 18-08-2020 Through 21-08-2020",

year = "2020",

month = aug,

doi = "10.1109/PMAPS47429.2020.9183604",

language = "English",

series = "2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings",

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

booktitle = "2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings",

address = "United States",

}

Ehsan, A, Preece, R, Reza Hosseini, SH, Allahham, A & Taylor, P 2020, Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo. in 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings., 9183604, 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020, Liege, Belgium, 18/08/20. https://doi.org/10.1109/PMAPS47429.2020.9183604

Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo. / Ehsan, Ali; Preece, Robin; Reza Hosseini, Seyed Hamid et al.
2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2020. 9183604 (2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo

AU - Ehsan, Ali

AU - Preece, Robin

AU - Reza Hosseini, Seyed Hamid

AU - Allahham, Adib

AU - Taylor, Phil

N1 - Funding Information: This work was supported by EPSRC through the Supergen Energy Networks Hub project under Grant EP/S00078X/1 and EPSRC National Centre for Energy Systems Integration under Grant EP/P01173/1. Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/8

Y1 - 2020/8

N2 - This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-To-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.

AB - This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-To-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.

KW - electricity

KW - gas

KW - muli-vector

KW - network

KW - probabilistic

KW - propagation

KW - uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85091339583&partnerID=8YFLogxK

U2 - 10.1109/PMAPS47429.2020.9183604

DO - 10.1109/PMAPS47429.2020.9183604

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:85091339583

T3 - 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings

BT - 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020

Y2 - 18 August 2020 through 21 August 2020

ER -

Ehsan A, Preece R, Reza Hosseini SH, Allahham A, Taylor P. Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo. In 2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE). 2020. 9183604. (2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings). doi: 10.1109/PMAPS47429.2020.9183604

Uncertainty Propagation through Integrated Gas and Electricity Networks using Sequential Monte-Carlo

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Handle.net

Other files and links

Fingerprint

Cite this