Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing
<|reference_start|>Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing: 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.<|reference_end|>
@article{zhu2024evaluating,
    title={Evaluating Uncertainties in Electricity Markets via Machine Learning and
  Quantum Computing},
    author={Shuyang Zhu, Ziqing Zhu, Linghua Zhu, Yujian Ye, Siqi Bu and Sasa Z.
  Djokic},
    journal={arXiv preprint arXiv:2407.16404},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16404},
    primaryClass={eess.SY cs.SY}
}