0557719 20240402213500.720220530235959.9 85130531084 000796440200001 10.1080/02331934.2022.2076232 23 s. P cav_un_epca*02582180233-19347211 (2023)2745-2767Taylor & Francis electricity market multileader-common-follower game stochastic demand day-ahead bidding chance constraints cav_un_auth*0280972 Branda Martin UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0015558 Henrion R. DE cav_un_auth*0234872 Pištěk Miroslav UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. https://www.tandfonline.com/doi/full/10.1080/02331934.2022.2076232 http://library.utia.cas.cz/separaty/2022/MTR/pistek-0557719.pdf GA18-04145S GA ČR CZ cav_un_auth*0373104 GA21-07494S GA ČR CZ cav_un_auth*0430801 We deal with several sources of uncertainty in electricity markets. The independent system operator (ISO) maximizes the social welfare using chance constraints to hedge against discrepancies between the estimated and real electricity demand. We find an explicit solution to the ISO problem and use it to tackle the problem of a producer. In our model, production, as well as the income of a producer, are determined based on the estimated electricity demand predicted by the ISO, which is unknown to producers. Thus, each producer is hedging against the uncertainty of the prediction of the demand using the value-at-risk approach. To illustrate our results, a numerical study of a producer's best response given a historical distribution of both estimated and real electricity demand is provided. WOS AH 10000 10100 10102 2024 RVO:67985556 https://hdl.handle.net/11104/0346178 S Article Operations Research Management Science|Mathematics Applied A MATHEMATICS.APPLIED|OPERATIONSRESEARCH&MANAGEMENTSCIENCE 1.9 0.3 5.9 0.00431 0.652 3146 63 0.699 35.08 1.85 828 Q2 1.500 129 Q2 56.3 Q3 Q2 0.7 Q2 73.3 2023 85130531084 SCOPUS PUBMED 000796440200001 WOS cav_un_epca*0258218 Optimization 72 11 2023 2745 2767 0233-1934 1029-4945 Taylor & Francis