What time-period aggregation method works best for power system operation models with renewables and storage?

S. Wogrin, D. A. Tejada-Arango, S. Pineda, J. M. Morales

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

In this paper we compare two cutting-edge time-period aggregation methodologies for power system models that consider both renewables and storage technologies: The chronological time-period clustering; and, the enhanced representative period approach. Such methodologies are used in order to reduce the computational burden of highly complex optimization models while not compromising the quality of the results. With this paper, we identify which method works best, and under which conditions, in order to reproduce the outcomes of the hourly benchmark model.

Original languageEnglish
Title of host publicationSEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781728111568
DOIs
Publication statusPublished - Sep 2019
Externally publishedYes
Event2nd International Conference on Smart Energy Systems and Technologies, SEST 2019 - Porto, Portugal
Duration: 9 Sep 201911 Sep 2019

Publication series

NameSEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies

Conference

Conference2nd International Conference on Smart Energy Systems and Technologies, SEST 2019
Country/TerritoryPortugal
CityPorto
Period9/09/1911/09/19

Keywords

  • clustering
  • power system models
  • time-period aggregation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

Fingerprint

Dive into the research topics of 'What time-period aggregation method works best for power system operation models with renewables and storage?'. Together they form a unique fingerprint.

Cite this