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Producer's Best Response in Pay-as-clear Electricity Market with Uncertain Demand
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SYSNO ASEP 0507761 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Producer's Best Response in Pay-as-clear Electricity Market with Uncertain Demand Author(s) Aussel, D. (FR)
Branda, Martin (UTIA-B) RID, ORCID
Henrion, R. (DE)
Pištěk, Miroslav (UTIA-B) RID, ORCIDNumber of authors 4 Article number 105 Source Title Proceedings of the 5th World Congress on New Technologies (NewTech'19). - Ontario : International ASET Inc., 2019 - ISSN 2369-8128 - ISBN 978-1-927877-62-3 Number of pages 2 s. Publication form Print - P Action World Congress on New Technologies (NewTech'19) /5./ Event date 18.08.2019 - 20.08.2019 VEvent location Lisabon Country PT - Portugal Event type WRD Language eng - English Country CA - Canada Keywords Nash equilibria ; uncertainty ; Independent System Operator (ISO) Subject RIV BA - General Mathematics OECD category Pure mathematics R&D Projects GA17-08182S GA ČR - Czech Science Foundation (CSF) GA18-04145S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 EID SCOPUS 85075142837 DOI 10.11159/icert19.105 Annotation In this work, we are particularly focused on several sources of uncertainty in (pay-as-clear) electricity markets. To deal with stochastic demand we employ the so-called chance constrained formulations of the problem [1] of the ISO as well as the problem of each producer. In detail, the ISO minimizes the production cost using a value-at-risk (VaR) approach, thus hedging against discrepancies between estimated and real electricity demand. Similarly, in the day-ahead market, each producer is hedging against the uncertainty of his own prediction of the demand using VaR approach again. In such a setting we aim at determining the “best response” of a given producer, i.e. the bid maximizing its profit. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2020
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