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dc.contributor.author
Evangelou, Samouil
en
dc.date.accessioned
2018-04-26T08:24:45Z
dc.date.available
2018-04-27T00:00:18Z
dc.date.issued
2018-04-26
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29040
dc.rights
Default License
dc.subject
Energy pricing
en
dc.subject
Energy security
en
dc.subject
Pricing volatility
en
dc.title
On energy security and electricity price volatility in Europe
en
heal.type
masterThesis
en_US
heal.keywordURI.LCSH
Energy security
heal.keywordURI.LCSH
Power resources
heal.keywordURI.LCSH
Energy policy
heal.keywordURI.LCSH
Energy conservation
heal.keywordURI.LCSH
Energy consumption--Environmental aspects
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Energy Management
en_US
heal.publicationDate
2018-04-25
heal.abstract
This dissertation was written as a part of the MSc in Energy Management at the International Hellenic University. In this dissertation thesis it was examined whether electricity growth price volatility can be explained by the generation mix in Europe. A sample of nineteen European countries was selected, for different time periods, according to the market data availability for baseload electricity prices, which formed an unbalanced panel. After considering the solely significant variables, capable for explaining fluctuations, the final model resulted to nine significant electricity generation categories. More specifically, the hydro power generated from plants with capacity from one to ten megawatts, hydro power from plants with capacity larger than ten megawatts, wind power, electricity generated from gas/diesel, various oil products, natural gas, industrial waste, solid biofuels and liquid biofuels. Panel analysis resulted to the selection of a random effects model, meaning that whatever differences are found among the countries in the sample, do not correlate with the generation variables. In other words, any feature that affects all countries in the sample, as this is expressed by the concept of the unobserved heterogeneity, is considered to be random and is classified as a component of the error term. Goodness-of-fit is calculated at 27.21%.
en
heal.advisorName
Psychogios, Dimitrios
el
heal.committeeMemberName
Psychogios, Dimitrios
el
heal.committeeMemberName
Dergiades, Theologos
el
heal.committeeMemberName
Panagiotidis, Theodoros
el
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US
heal.numberOfPages
106
en_US
heal.spatialCoverage
Europe
en


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