An ambiguity-averse model for planning the transmission grid under uncertainty on renewable distributed generation

Davidi Pozo, Alexandre Street, Velloso Alexandre

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

5 Citations (Scopus)

Abstract

We present a Distributionally Robust Optimization (DRO) mathematical framework for addressing the Transmission Expansion Planning (TEP) problem that accounts for long-and short-term uncertainty for significant penetration of new renewable distributed energy resources (DER). We consider the case where the exact probability distribution of future DER is ambiguous, i.e., difficult to accurately estimate. The resulting DRO model is robust against all possible probability distribution functions that lie on an ambiguity set with partial information of the exact probability distribution. The proposed framework allows finding an optimal transmission expansion plan without relying on strong assumptions about exact probability distribution. Additionally, our proposed approach enables transmission planners to consider, within a DRO framework, the effect on the structure of short-term DER uncertainties due to the conditional information of different scenarios for the long-term trends. We illustrate the effectiveness of the proposed approach on the IEEE IIS-bus test system.

Original languageEnglish
Title of host publication20th Power Systems Computation Conference, PSCC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781910963104
DOIs
Publication statusPublished - 20 Aug 2018
Event20th Power Systems Computation Conference, PSCC 2018 - Dublin, Ireland
Duration: 11 Jun 201815 Jun 2018

Publication series

Name20th Power Systems Computation Conference, PSCC 2018

Conference

Conference20th Power Systems Computation Conference, PSCC 2018
Country/TerritoryIreland
CityDublin
Period11/06/1815/06/18

Keywords

  • Ambiguity aversion
  • Distributionally Robust Optimization
  • Transmission Expansion Planning

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