Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

Method For Medium- to Long-Term Time-of-day Trading Decision in Agent-Based Power Purchase of Grid Enterprises Considering CVaR

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  • @INPROCEEDINGS{10.4108/eai.12-1-2024.2347227,
        author={Yue  Shi and Jiangbo  Wang and Junhui  Liu and Yao  Lu and Shuo  Yin and Mingshun  Ji and Xinrui  Zhong and Yihan  Zhang},
        title={Method For Medium- to Long-Term Time-of-day Trading Decision in Agent-Based Power Purchase of Grid Enterprises Considering CVaR},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China},
        publisher={EAI},
        proceedings_a={BDEDM},
        year={2024},
        month={6},
        keywords={electricity market; transaction decision model; power grid enterprise; proxy electricity purchase; cvar},
        doi={10.4108/eai.12-1-2024.2347227}
    }
    
  • Yue Shi
    Jiangbo Wang
    Junhui Liu
    Yao Lu
    Shuo Yin
    Mingshun Ji
    Xinrui Zhong
    Yihan Zhang
    Year: 2024
    Method For Medium- to Long-Term Time-of-day Trading Decision in Agent-Based Power Purchase of Grid Enterprises Considering CVaR
    BDEDM
    EAI
    DOI: 10.4108/eai.12-1-2024.2347227
Yue Shi1, Jiangbo Wang2, Junhui Liu2, Yao Lu2, Shuo Yin2, Mingshun Ji3, Xinrui Zhong3, Yihan Zhang1,*
  • 1: State Grid Henan Electric Power Company
  • 2: State Grid Henan Electric Power Company Economic and Technological Research Institute
  • 3: Beijing Tslntergy Technology Co., Ltd
*Contact email: zhangyihan2@ha.sgcc.com.cn

Abstract

To further promote fair participation of grid enterprise agent power purchasers in electricity spot trading, it is necessary to strengthen the connection mechanism between agent power purchase activities and the medium- to long-term electricity market and spot market. Given the uncertainty in the electricity demand of agent users and market prices, a reasonable allocation of power purchase proportions in multi-time scales and multi-product electricity trading can help reduce cash flow risks for grid enterprises and promote the safe and stable operation of the electricity market. The optimal strategy is determined using the Monte Carlo simulation method, and the effectiveness of the proposed model and method is validated through numerical examples. The results demonstrate a reduction in conditional risk value and other relevant indicators, providing grid enterprises with valuable references for mitigating trading risks and formulating agent power purchase strategies.