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Statistical analysis of photovoltaic and wind power generation

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dc.FundingDetailsHigher Education Research Council Ministry of Higher Education, Malaysia
dc.FundingDetailsThe author acknowledges financial support from FINEWSTOCH, a research project funded by the Norwegian Research Council and the Ministry of Higher Education, Malaysia. Fred Espen Benth and Ingrid Hob�k Haff are thanked for valuable guidance and discussions. The author is also grateful for comments and suggestions from an anonymous referee, which resulted in a significant and remarkable improvement of the original paper.
dc.citedby1
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversity of Oslo
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorIbrahim N.A.en_US
dc.date.accessioned2024-05-28T08:28:28Z
dc.date.available2024-05-28T08:28:28Z
dc.date.issued2018
dc.description.abstractIn this paper, we do a comparison between maximal and daily average production of photovoltaic (PV) and wind energy based on a transmission system operator in Germany using statistical analysis with different seasonality functions. We adopt sun intensity as a seasonal function for PV and a trigonometric function for wind, while the deseasonalized data is modeled by an autoregressive process. The stochastic component of both energies is relatively well captured by a skew normal distribution. Further, a weak anticorrelation between residuals of PV and wind is found, which leads to the complexity of balancing supply of renewable energy. No clear copula pattern is detected between transformed residuals, but it exhibits seasonality in the dependence. We can exploit this feature as a risk management strategy, such as quanto options, for nonrenewable energy producers to hedge against high renewable energy generation. � 2018 Infopro Digital Risk (IP) Limited.en_US
dc.description.natureFinalen_US
dc.identifier.citationVOLUME 11, NUMBER 3 (SEPTEMBER 2018) PAGES: 43-69 DOI: 10.21314/JEM.2018.178en_US
dc.identifier.doi10.21314/JEM.2018.178
dc.identifier.epage69
dc.identifier.isiWOS:000442420100004
dc.identifier.issn17563607
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85059818377
dc.identifier.spage43
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059818377&doi=10.21314%2fJEM.2018.178&partnerID=40&md5=5dcd1ade18858c244cd1b469eb24353b
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8856
dc.identifier.volume11
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherInfopro digitalen_US
dc.relation.ispartofJournal of Energy Marketsen_US
dc.sourceScopus
dc.subjectAutoregressive processen_US
dc.subjectCopulaen_US
dc.subjectPhotovoltaic power productionen_US
dc.subjectQuanto optionsen_US
dc.subjectSkew normal distributionen_US
dc.subjectWind power productionen_US
dc.titleStatistical analysis of photovoltaic and wind power generationen_US
dc.typeArticleen_US
dspace.entity.typePublication

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