A sequential Monte Carlo model of the combined GB gas and electricity network

Chaudry, Modassar ORCID: https://orcid.org/0000-0002-5577-9790, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Jenkins, Nicholas ORCID: https://orcid.org/0000-0003-3082-6260 2013. A sequential Monte Carlo model of the combined GB gas and electricity network. Energy Policy 62 , pp. 473-483. 10.1016/j.enpol.2013.08.011

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Abstract

A Monte Carlo model of the combined GB gas and electricity network was developed to determine the reliability of the energy infrastructure. The model integrates the gas and electricity network into a single sequential Monte Carlo simulation. The model minimises the combined costs of the gas and electricity network, these include gas supplies, gas storage operation and electricity generation. The Monte Carlo model calculates reliability indices such as loss of load probability and expected energy unserved for the combined gas and electricity network. The intention of this tool is to facilitate reliability analysis of integrated energy systems. Applications of this tool are demonstrated through a case study that quantifies the impact on the reliability of the GB gas and electricity network given uncertainties such as wind variability, gas supply availability and outages to energy infrastructure assets. Analysis is performed over a typical midwinter week on a hypothesised GB gas and electricity network in 2020 that meets European renewable energy targets. The efficacy of doubling GB gas storage capacity on the reliability of the energy system is assessed. The results highlight the value of greater gas storage facilities in enhancing the reliability of the GB energy system given various energy uncertainties.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TD Environmental technology. Sanitary engineering
Uncontrolled Keywords:	Monte Carlo; Gas and electricity network; Security of supply
Publisher:	Elsevier
ISSN:	0301-4215
Funders:	NERC, UKERC II
Last Modified:	25 Oct 2022 08:38
URI:	https://orca.cardiff.ac.uk/id/eprint/53801

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