CoChat
Sign Up
Cited by 168Electric Power System OptimizationAuction Theory and ApplicationsSmart Grid Energy Management

A Reinforcement Learning Model to Assess Market Power Under Auction-Based Energy Pricing

Nanduri, Vishnuteja, Das, Tapas K.·2007·IEEE Transactions on Power Systems
DOI 10.1109/tpwrs.2006.888977
Open this paper in CoChat — free account

Abstract

Auctions serve as a primary pricing mechanism in various market segments of a deregulated power industry. In day-ahead (DA) energy markets, strategies such as uniform price, discriminatory, and second-price uniform auctions result in different price settlements and thus offer different levels of market power. In this paper, we present a nonzero sum stochastic game theoretic model and a reinforcement learning (RL)-based solution framework that allow assessment of market power in DA markets. Since there are no available methods to obtain exact analytical solutions of stochastic games, an RL-based approach is utilized, which offers a computationally viable tool to obtain approximate solutions. These solutions provide effective bidding strategies for the DA market participants. The market powers associated with the bidding strategies are calculated using well-known indexes like Herfindahl-Hirschmann index and Lerner index and two new indices, quantity modulated price index (QMPI) and revenue-based market power index (RMPI), which are developed in this paper. The proposed RL-based methodology is tested on a sample network

Integrity signals

Registry

Crossref · 10.1109/tpwrs.2006.888977

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • RL has been applied to predict bids and market power in electricity markets, and to set dynamic prices in more general settings.

    “For example, RL has been applied to predict bids [654] and market power [572] in electricity markets, and to set dynamic prices in more general settings [510].”

    From Tackling climate change with machine learning (2023) · cites this paper as [572]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Nanduri, V., & Das, T. K.
Affiliations
University of South Florida (US)
ORCID
Das, Tapas K.
Venue
IEEE Transactions on Power Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2007-02
Volume
22
Issue
1
Pages
85-95
ISSN
0885-8950 (ISSN-L)
DOI
10.1109/tpwrs.2006.888977
OpenAlex
W2138620854
Cited by
168
References
46
Citations by year
2026: 3 · 2025: 4 · 2024: 8 · 2023: 6 · 2022: 6 · 2021: 8 · 2020: 12 · 2019: 43 · 2018: 7 · 2017: 4 · 2016: 9 · 2015: 5 · 2014: 2 · 2013: 8 · 2012: 6
FWCI
7.88
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Transactions on Power Systems (publishedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Auction Theory and Applications
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Keywords
market power, day-ahead market, reinforcement learning, bidding strategy, uniform-price auctions, discriminatory auctions, second-price auctions, Lerner index

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review