dc.contributor.author | Kiesel, Rüdiger | |
dc.contributor.author | Paraschiv, Florentina | |
dc.contributor.author | Sætherø, Audun | |
dc.date.accessioned | 2019-07-09T06:10:30Z | |
dc.date.available | 2019-07-09T06:10:30Z | |
dc.date.created | 2018-02-20T16:09:18Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Computational Management Science. 2018, 1-25. | nb_NO |
dc.identifier.issn | 1619-697X | |
dc.identifier.uri | http://hdl.handle.net/11250/2603787 | |
dc.description.abstract | There are several approaches in the literature for the derivation of price forward curves (PFCs) which distinguish among each other by the procedure employed for the derivation of seasonality shapes, smoothing technique and by the design of the optimization procedure. However, a comparative study to highlight the strengths and weaknesses of different methods is missing. For the construction of PFCs we typically incorporate the information about market expectation from the observed futures prices and the deterministic seasonal effects of electricity prices. In most existing approaches, the seasonality shape is fitted to historically observed spot prices, and it is an exogenous input to the optimization procedure. As seasonal effects on electricity prices differ between markets, our model allows a more general and flexible definition of the seasonality shape. In this study, we propose an alternative calibration procedure for the seasonality shape, where the level of futures as well as historical spot prices are simultaneously taken into account in a joint optimization approach. We discuss comparatively the features of existing methods for PFCs, and highlight the advantages of our optimization procedure. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer Verlag | nb_NO |
dc.title | On the Construction of Hourly Price Forward Curves for Electricity Prices | nb_NO |
dc.title.alternative | On the Construction of Hourly Price Forward Curves for Electricity Prices | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.pagenumber | 1-25 | nb_NO |
dc.source.journal | Computational Management Science | nb_NO |
dc.identifier.doi | 10.1007/s10287-018-0300-6 | |
dc.identifier.cristin | 1567138 | |
dc.description.localcode | This article will not be available due to copyright restrictions (c) 2018 by Springer | nb_NO |
cristin.unitcode | 194,60,10,0 | |
cristin.unitname | NTNU Handelshøyskolen | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |