Data Analytics in Competitive Electricity Markets to Uncover the Impact of Emerging Technologies

Simple item page
atmire.migration.oldid5545
dc.contributor.advisorZareipour, Hamid
dc.contributor.advisorRakai, Logan
dc.contributor.authorZamanidehkordi, Payam
dc.contributor.committeememberKarki, Rajesh
dc.contributor.committeememberFar, Behrouz
dc.contributor.committeememberKnight, Andrew
dc.contributor.committeememberNowicki, Edwin
dc.contributor.committeememberHollis, Aidan
dc.date.accessioned2017-04-28T21:42:02Z
dc.date.available2017-04-28T21:42:02Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractThe electrical power industry has entered a transition towards sustainable, reliable and clean solutions. It is a continuous revolution trending to a large-scale expansion of renewables in power systems. There have been, however, serious concerns over reliable and secure operation of power systems. Energy storage facilities are increasingly being used to help integrate renewable energy resources into the grid. While understanding the environmental benefits of these emerging technologies is straightforward, the economic impacts of their integration in a competitive market is more complicated. These emerging technologies are likely to have an economically-important effect on the dynamics of electricity prices. This is a concern to different sections of electricity markets including power suppliers, policy makers, and end users. This thesis focuses on applying data mining tools to competitive electricity markets in order to uncover the impact of emerging technologies such as wind power and storage systems on the dynamics of electricity prices. Data-driven approaches are developed to explore the impact on wholesale prices of individual wind farms and independently-operated large-scale energy storage systems. Additionally, this thesis proposes a data-driven methodology to determine a justified support scheme for upcoming wind farms by incorporating their estimated revenue and levelized cost of energy. Moreover, an operation-inspired electricity price prediction scheme is developed to improve the economic profit obtained from operation of storage facilities in competitive markets. Numerical simulations are provided for the electricity markets of Alberta and Ontario. The results prove the efficiency and accuracy of proposed methodologies in estimating the impact on wholesale prices of emerging technologies. In addition, the obtained results from both competitive markets indicate that the presented methodology in this thesis is able to estimate the revenue of an upcoming wind farm with reasonable accuracy, which successively determines the support scheme awarded to the project. Moreover, the performed analyses manifest the effectiveness of the proposed price prediction scheme in improving the economic performance of storage systems.en_US
dc.identifier.citationZamanidehkordi, P. (2017). Data Analytics in Competitive Electricity Markets to Uncover the Impact of Emerging Technologies (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25516en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25516
dc.identifier.urihttp://hdl.handle.net/11023/3755
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEngineering--Electronics and Electrical
dc.subject.otherElectricity Market
dc.subject.otherData mining
dc.subject.otherRenewable Energy
dc.subject.otherEnergy Storage
dc.titleData Analytics in Competitive Electricity Markets to Uncover the Impact of Emerging Technologies
dc.typedoctoral thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
Files
Collections
Open Theses and Dissertations