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

LI Bin; LI Ruosong; MENG Zixuan; ZHANG Yumeng

doi:10.11930/j.issn.1004-9649.202506029

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

更新时间：2026-07-01

- Provincial optimal decision-making technology based on multi-time-scale combined agent power purchase review
- Vol. 59, Issue 6, Pages: 89-100(2026)
- 作者机构：
  
  华北电力大学电气与电子工程学院,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202506029
  CLC：
- Online First：22 June 2026，
  
  Published：2026
- 稿件说明：
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李彬, 李若松, 孟子轩, 张雨蒙. 基于多时间尺度组合代理购电复盘的省内优化决策技术[J]. 中国电力, 2026, 59(6): 89-100.

LI Bin, LI Ruosong, MENG Zixuan, et al. Provincial optimal decision-making technology based on multi-time-scale combined agent power purchase review[J]. 2026, 59(6): 89-100.
李彬, 李若松, 孟子轩, 张雨蒙. 基于多时间尺度组合代理购电复盘的省内优化决策技术[J]. 中国电力, 2026, 59(6): 89-100. DOI： 10.11930/j.issn.1004-9649.202506029.

LI Bin, LI Ruosong, MENG Zixuan, et al. Provincial optimal decision-making technology based on multi-time-scale combined agent power purchase review[J]. 2026, 59(6): 89-100. DOI： 10.11930/j.issn.1004-9649.202506029.

摘要

随着电力市场化改革的推进，工商业用户将逐步全面进入市场，未进入市场的用户由电网企业代理购电。作为代理购电业务的关键主体，统筹安排多时间尺度市场购电，降低购电成本，对电网企业的运营有重要意义。梳理电网企业代理购电工作流程，构建计及年度、月度、现货市场的电网企业年度购电决策复盘优化模型，采用遗传算法和基于卷积神经网络（convolutional neural network，CNN）-长短期记忆网络（long short-term memory network，LSTM）-注意力机制（attention mechanism，Attention）算法进行模型求解，通过算例分析验证所建模型的有效性。复盘结果显示，通过调整中长期与现货市场购电比例，可有效实现购电策略的降本增效。

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.

关键词

Keywords

references

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