Application of Bayesian networks in modeling of underground gas storage energy security

2022
journal article
article
3
cris.lastimport.wos2024-04-09T20:51:17Z
dc.abstract.enEnergy security is a multidimensional and multifaceted concept, therefore defining it is a complex problem. It requires the consideration of a wide set of factors from the fields of economics, geology, ecology and geopolitics, all of which have an influence on energy security or the lack thereof. The article focuses on natural gas, which is a very specific fuel in the European context. It is the most “politicized” source of energy, as a consequence of its growing importance as a transition fuel in the energy transformation process. In order to identify dependencies between variables on the gas market and analyze their impact on it (in particular on underground storage), the authors chose a set of variables and built a Bayesian network. The network is an effective and flexible tool that allows analysis of the relationships between the variables that build them and model their values based on evidence. The article presents two stages of work with the Bayesian network. In the first one, a network was built based on historical data. It shows the relationships between the variables as well as the probability of the value ranges of individual variables. A huge advantage of the presented Bayesian network is that it can be used to model various scenarios on the gas market. Moreover, the ability to make statistical inferences for all its nodes represents a valuable additional feature. Several examples of such inferences are presented in the second stage of the analysis, examining the impact of consumption variability on the level of inventory in underground gas storage facilities, the impact of having an LNG terminal and the share of natural gas in electricity production on the storage capacity of a given country. The use of tools such as Bayesian networks allows us to better discover the interrelationships between variables influencing the energy market, analyze them, and estimate the impact on energy security of distinct scenarios described with specific metrics. A simple example of such a metric, i.e., the minimum level of gas storage at the end of the winter season, as well as its analysis and modeling using a relatively simple Bayesian network, is presented in this article.pl
dc.affiliationWydział Studiów Międzynarodowych i Politycznych : Instytut Rosji i Europy Wschodniejpl
dc.contributor.authorKosowski, Piotrpl
dc.contributor.authorKosowska, Katarzyna - 200792 pl
dc.contributor.authorNawalaniec, Wojciechpl
dc.date.accessioned2022-07-21T08:26:21Z
dc.date.available2022-07-21T08:26:21Z
dc.date.issued2022pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.number14pl
dc.description.versionostateczna wersja wydawcy
dc.description.volume15pl
dc.identifier.articleid5185pl
dc.identifier.doi10.3390/en15145185pl
dc.identifier.eissn1996-1073pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/297694
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa*
dc.rights.licenceCC-BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcode.pl*
dc.share.typeotwarte czasopismo
dc.subject.enenergy securitypl
dc.subject.enBayesian networkpl
dc.subject.ennatural gaspl
dc.subject.engas storagepl
dc.subject.enEuropepl
dc.subject.enenergypl
dc.subtypeArticlepl
dc.titleApplication of Bayesian networks in modeling of underground gas storage energy securitypl
dc.title.journalEnergiespl
dc.typeJournalArticlepl
dspace.entity.typePublication
cris.lastimport.wos
2024-04-09T20:51:17Z
dc.abstract.enpl
Energy security is a multidimensional and multifaceted concept, therefore defining it is a complex problem. It requires the consideration of a wide set of factors from the fields of economics, geology, ecology and geopolitics, all of which have an influence on energy security or the lack thereof. The article focuses on natural gas, which is a very specific fuel in the European context. It is the most “politicized” source of energy, as a consequence of its growing importance as a transition fuel in the energy transformation process. In order to identify dependencies between variables on the gas market and analyze their impact on it (in particular on underground storage), the authors chose a set of variables and built a Bayesian network. The network is an effective and flexible tool that allows analysis of the relationships between the variables that build them and model their values based on evidence. The article presents two stages of work with the Bayesian network. In the first one, a network was built based on historical data. It shows the relationships between the variables as well as the probability of the value ranges of individual variables. A huge advantage of the presented Bayesian network is that it can be used to model various scenarios on the gas market. Moreover, the ability to make statistical inferences for all its nodes represents a valuable additional feature. Several examples of such inferences are presented in the second stage of the analysis, examining the impact of consumption variability on the level of inventory in underground gas storage facilities, the impact of having an LNG terminal and the share of natural gas in electricity production on the storage capacity of a given country. The use of tools such as Bayesian networks allows us to better discover the interrelationships between variables influencing the energy market, analyze them, and estimate the impact on energy security of distinct scenarios described with specific metrics. A simple example of such a metric, i.e., the minimum level of gas storage at the end of the winter season, as well as its analysis and modeling using a relatively simple Bayesian network, is presented in this article.
dc.affiliationpl
Wydział Studiów Międzynarodowych i Politycznych : Instytut Rosji i Europy Wschodniej
dc.contributor.authorpl
Kosowski, Piotr
dc.contributor.authorpl
Kosowska, Katarzyna - 200792
dc.contributor.authorpl
Nawalaniec, Wojciech
dc.date.accessioned
2022-07-21T08:26:21Z
dc.date.available
2022-07-21T08:26:21Z
dc.date.issuedpl
2022
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.numberpl
14
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
15
dc.identifier.articleidpl
5185
dc.identifier.doipl
10.3390/en15145185
dc.identifier.eissnpl
1996-1073
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/297694
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa
dc.rights.licence
CC-BY
dc.rights.uri*
http://creativecommons.org/licenses/by/4.0/legalcode.pl
dc.share.type
otwarte czasopismo
dc.subject.enpl
energy security
dc.subject.enpl
Bayesian network
dc.subject.enpl
natural gas
dc.subject.enpl
gas storage
dc.subject.enpl
Europe
dc.subject.enpl
energy
dc.subtypepl
Article
dc.titlepl
Application of Bayesian networks in modeling of underground gas storage energy security
dc.title.journalpl
Energies
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

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