Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An AI-agent based Approach
<|reference_start|>Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An AI-agent based Approach: To incentivize flexible resources such as Battery Energy Storage Systems (BESSs) to offer Frequency Control Ancillary Services (FCAS), Australia's National Electricity Market (NEM) has implemented changes in recent years towards shorter-term bidding rules and faster service requirements. However, firstly, existing bidding optimization methods often overlook or oversimplify the key aspects of FCAS market procedures, resulting in an inaccurate depiction of the market bidding process. Thus, the BESS bidding problem is modeled based on the actual bidding records and the latest market specifications and then formulated as a deep reinforcement learning (DRL) problem. Secondly, the erratic decisions of the DRL agent caused by imperfectly predicted market information increases the risk of profit loss. Hence, a Conditional Value at Risk (CVaR)-based DRL algorithm is developed to enhance the risk resilience of bidding strategies. Thirdly, well-trained DRL models still face performance decline in uncommon scenarios during online operations. Therefore, a Large Language Models (LLMs)-assisted artificial intelligence (AI)-agent interactive decision-making framework is proposed to improve the strategy timeliness, reliability and interpretability in uncertain new scenarios, where conditional hybrid decision and self-reflection mechanisms are designed to address LLMs' hallucination challenge. The experiment results demonstrate that our proposed framework has higher bidding profitability compared to the baseline methods by effectively mitigating the profit loss caused by various uncertainties.<|reference_end|>
@article{zhang2024large,
    title={Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An
  AI-agent based Approach},
    author={Borui Zhang, Chaojie Li, Guo Chen, Zhaoyang Dong},
    journal={arXiv preprint arXiv:2406.00974},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00974},
    primaryClass={eess.SY cs.SY}
}