Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing Article Swipe
Shuyang Zhu
,
Ziqing Zhu
,
Linghua Zhu
,
Yujian Ye
,
Siqi Bu
,
Sasa Djokic
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.16404
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.16404
The analysis of decision-making process in electricity markets is crucial for understanding and resolving issues related to market manipulation and reduced social welfare. Traditional Multi-Agent Reinforcement Learning (MARL) method can model decision-making of generation companies (GENCOs), but faces challenges due to uncertainties in policy functions, reward functions, and inter-agent interactions. Quantum computing offers a promising solution to resolve these uncertainties, and this paper introduces the Quantum Multi-Agent Deep Q-Network (Q-MADQN) method, which integrates variational quantum circuits into the traditional MARL framework. The main contributions of the paper are: identifying the correspondence between market uncertainties and quantum properties, proposing the Q-MADQN algorithm for simulating electricity market bidding, and demonstrating that Q-MADQN allows for a more thorough exploration and simulates more potential bidding strategies of profit-oriented GENCOs, compared to conventional methods, without compromising computational efficiency. The proposed method is illustrated on IEEE 30-bus test network, confirming that it offers a more accurate model for simulating complex market dynamics.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.16404
- https://arxiv.org/pdf/2407.16404
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406073908
All OpenAlex metadata
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https://openalex.org/W4406073908Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.16404Digital Object Identifier
- Title
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Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum ComputingWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-07-23Full publication date if available
- Authors
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Shuyang Zhu, Ziqing Zhu, Linghua Zhu, Yujian Ye, Siqi Bu, Sasa DjokicList of authors in order
- Landing page
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https://arxiv.org/abs/2407.16404Publisher landing page
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https://arxiv.org/pdf/2407.16404Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2407.16404Direct OA link when available
- Concepts
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Electricity, Computer science, Quantum, Electricity system, Artificial intelligence, Machine learning, Electrical engineering, Engineering, Electricity generation, Physics, Quantum mechanics, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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