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