A Stochastic Game Approach for Modeling Wholesale Energy Bidding in Deregulated Power Markets
Abstract
It has been noted in a recent report (Dec. 2002) from the General Accounting Office of the U.S. that "various design flaws in wholesale markets and transmission services have created operational problems within and between wholesale markets." This paper presents a novel methodology to analyze and design the wholesale energy bidding aspect of a deregulated power market. We adopt a system-wide approach that considers many of the relevant features including transmission congestion. We develop a two-stage model: 1) a nonzero sum stochastic game with average reward for the wholesale energy market operation, and 2) a nonlinear programming (NLP) model for the unit-commitment (UC) and the optimal power-flow aspects. The solution approach for the two-stage model is based on a reinforcement-learning (RL) algorithm, which is designed to obtain Nash equilibrium policies. We use a three-retailer/three-supplier power network with and without congestion to test and benchmark our methodology.
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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 [654]
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References 34 · Cited by 31
- Authors
- Ragupathi, R., & Das, T. K.
- Affiliations
- University of South Florida (US)
- ORCID
- Das, T.K.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2004-05
- Volume
- 19
- Issue
- 2
- Pages
- 849-856
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2153168378
- Cited by
- 31
- References
- 34
- Citations by year
- 2022: 4 · 2021: 2 · 2020: 5 · 2019: 2 · 2018: 1 · 2014: 3 · 2012: 4
- FWCI
- 1.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- Funders
- National Science Foundation
- Texas A and M University
- Power Systems Engineering Research Center
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Keywords
- deregulated electricity markets, stochastic games, non-zero-sum games, average reward, transmission congestion, unit commitment, optimal power flow, reinforcement learning, two-stage model, electricity market design
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