A Robust Optimisation Approach using CVaR for Unit Commitment in a Market with Probabilistic Offers

W. A. Bukhsh, Athanasios Papakonstantinou, Pierre Pinson

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Abstract

The large scale integration of renewable energy sources (RES) challenges power system planners and operators alike as it can potentially introduce the need for costly investments in infrastructure. Furthermore, traditional market clearing mechanisms are no longer optimal due to the stochastic nature of RES. This paper presents a risk-aware market clearing strategy for a network with significant shares of RES. We propose an electricity market that embeds the uncertainty brought by wind power and other stochastic renewable sources by accepting probabilistic offers and use a risk measure defined by conditional value-at-risk (CVaR) to evaluate the risk of high re-dispatching cost due to the mis-estimation of renewable energy. The proposed model is simulated on a 39-bus network, whereby it is shown that significant reductions can be achieved by properly managing the risks of mis-estimation of stochastic generation.
Original languageEnglish
Title of host publication2016 IEEE International Energy Conference (ENERGYCON)
Number of pages6
PublisherIEEE
Publication date2016
ISBN (Print)978-1-4673-8463-6/
DOIs
Publication statusPublished - 2016
EventIEEE International Energy Conference 2016 - KU Leuven , Leuven , Belgium
Duration: 4 Apr 20168 Apr 2016

Conference

ConferenceIEEE International Energy Conference 2016
LocationKU Leuven
CountryBelgium
CityLeuven
Period04/04/201608/04/2016

Bibliographical note

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Keywords

  • Conditional value-at-risk
  • Market clearing
  • Optimal power flow
  • Risk analysis

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