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Bayesian Nash Equilibrium in Electricity Spot Markets: An Affine-Plane Approximation Approach
IIT Madras, Dept Elect Engn, Gurgaon 122006, Haryana, India..
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-9998-9773
Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78751 USA..
Australian Competit & Consumer Commiss, Canberra, ACT 2601, Australia..
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2022 (English)In: IEEE Transactions on Control of Network Systems, E-ISSN 2325-5870, Vol. 9, no 3, p. 1421-1434Article in journal (Refereed) Published
Abstract [en]

This article proposes a system of stochastic mixed complementarity problems (MCP) for calculating the Bayesian Nash equilibrium (BNE) in an electricity spot market. The generators submit strategic multilevel price-offer functions under incomplete information about their rivals' marginal cost. This strategic multilevel price offering of each generator is modeled as a standard stochastic bilevel program which is then reformulated as its equivalent MCP. The merger concept is employed and formulated to model multilevel price-offer functions. We then propose the BNE-MCP model which solves the stochastic MCP models of all generators together. A scenario reduction technique is also developed to accurately model the wind and demand uncertainty in the proposed BNE-MCP model. The proposed BNE-MCP model is carefully studied on two illustrative examples. The modified IEEE 14-, 30-, and 57-node systems are also examined in our article.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 9, no 3, p. 1421-1434
Keywords [en]
Affine-Plane approximation, Bayesian Nash equilibrium (BNE), merger, mixed complementarity problem (MCP), scenario reduction algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-319760DOI: 10.1109/TCNS.2021.3128510ISI: 000856122100035Scopus ID: 2-s2.0-85139223212OAI: oai:DiVA.org:kth-319760DiVA, id: diva2:1701719
Note

QC 20221007

Available from: 2022-10-07 Created: 2022-10-07 Last updated: 2023-06-08Bibliographically approved

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Hesamzadeh, Mohammad Reza

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