Provincial optimal decision-making technology based on multi-time-scale combined agent power purchase review

doi:10.11930/j.issn.1004-9649.202506029

Abstract

Abstract:

With the advancement of electricity market reform, industrial and commercial users will gradually enter the market, while the users not yet entering the market will purchase electricity through agents from grid companies. As key entities in agent-based electricity purchase, grid companies play a significant role in coordinating electricity purchases across multi-time-scale markets to reduce purchase costs, which is crucial for their operational efficiency. This study outlines the workflow of agent power purchase for power grid companies and establishes an annual power purchase decision review and optimization model that considers annual, monthly, and spot markets. The model is solved using the genetic algorithm and convolutional neural network (CNN) - long short-term memory network (LSTM) - attention mechanism (Attention), and the model's effectiveness is verified through case studies. The review results show that adjusting the power purchase ratio between the medium and long-term market and the spot market can effectively achieve cost reduction and efficiency improvement of power purchase strategies.

Key words: electricity market, curve decomposition, agent power purchase, purchase strategy, transaction review

LI Bin, LI Ruosong, MENG Zixuan, ZHANG Yumeng. Provincial optimal decision-making technology based on multi-time-scale combined agent power purchase review[J]. Electric Power, 2026, 59(6): 89-100.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202506029

https://www.electricpower.com.cn/EN/Y2026/V59/I6/89

Figures/Tables 12

Fig.1 Analysis of the source-load electricity matching relationship and its price formation mechanism

Fig.2 Integration framework for electricity purchasing agents participating in portfolio trading across medium and long-term, spot and other multi-type markets

Fig.3 Analysis of deviation indicators and their correlations in retrospective review

Table 1 Business parameter selection

业务参数	年度比例约束$ \alpha $	年度+月度比例约束$ \beta $	中长期+现货比例约束$ \gamma $
初始值	0.80	0.90	0.95

Fig.4 Review and optimization process

Fig.5 Comparative review of electricity consumption on typical days in January

Fig.6 Comparative review of electricity consumption on typical days in May

Fig.7 Comparative review of electricity consumption on typical days in August

Fig.8 Comparative review of electricity consumption on typical days in December

Fig.9 Comparison of review methods

Table 2 Performance evaluation of different algorithms for review

复盘方法	收敛时间/s	准确率/%
传统LSTM	92	91.68
CNN-BiLSTM	67	92.57
本文复盘方法	39	93.38

Fig.10 Review analysis of annual decomposed electricity quantity

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