dc.contributor.author | Askeland, Magnus | |
dc.contributor.author | Burandt, Thorsten | |
dc.contributor.author | Gabriel, Steven A. | |
dc.date.accessioned | 2020-10-23T07:37:10Z | |
dc.date.available | 2020-10-23T07:37:10Z | |
dc.date.created | 2020-09-16T10:07:06Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1868-3967 | |
dc.identifier.uri | https://hdl.handle.net/11250/2684660 | |
dc.description.abstract | As the end-users increasingly can provide fexibility to the power system, it is important to consider how this fexibility can be activated as a resource for the grid. Electricity network tarifs is one option that can be used to activate this fexibility. Therefore, by designing efcient grid tarifs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tarif design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decisionmaking model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results based on both deterministic and stochastic settings are presented and discussed. The fndings suggest how electricity grid tarifs should be designed to provide an efcient price signal for reducing aggregate network peaks. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer Verlag | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | A Stochastic MPEC Approach for Grid Tariff Design with Demand-Side Flexibility | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Energy Systems, Springer Verlag | en_US |
dc.identifier.doi | 10.1007/s12667-020-00407-7 | |
dc.identifier.cristin | 1830299 | |
dc.description.localcode | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |