Poster: Uncertain FlexOffers, a scalable, uncertainty-Aware model for energy flexibility

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

Abstract

The usage of Renewable Energy Sources in electricity grids is spreading more and more, and energy flexibility has a crucial role in it. Many models for flexibility have been proposed; in particular, Flex-Offer (FO) is an approximate model with the following properties: i) it models flexibility from different device types in an unified format; ii) it is scalable with respect to optimization for long time horizons, and aggregation of many loads; iii) it captures most/all of the available flexibility. This work proposes an extension of that model, uncertain FlexOffers (UFOs), which has the same properties, plus iv) considers the uncertainty affecting flexibility over long time horizons. We show that UFOs are 5 orders of magnitude faster than exact models for optimization, can retain 88.3% of the total flexibility when imbalance penalties are high, and allow to aggregate up to 3000 loads and up to 96 time units in less than 30 minutes.

Original languageEnglish
Title of host publicatione-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems
Number of pages2
PublisherAssociation for Computing Machinery
Publication date28 Jun 2022
Pages448-449
ISBN (Electronic)9781450393973
DOIs
Publication statusPublished - 28 Jun 2022
Event13th ACM International Conference on Future Energy Systems, e-Energy 2022 - Virtual, Online, United States
Duration: 28 Jun 20221 Jul 2022

Conference

Conference13th ACM International Conference on Future Energy Systems, e-Energy 2022
Country/TerritoryUnited States
CityVirtual, Online
Period28/06/202201/07/2022
SponsorACM, ACM SIGEnergy, Akamai, Huawei Technologies Co., Ltd.
Seriese-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems

Bibliographical note

Funding Information:
This work was supported by the H2020 projects FEVER and DomOS, GAs 894240 and 864537 respectively.

Publisher Copyright:
© 2022 Owner/Author.

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