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基于K均值聚类的工业负荷参与长周期备用市场能力评估

Potential Assessment of Industrial Load Participating in Capacity Market under New Power System

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【作者】 田雪沁陈宇衡刘壮壮王瑛玮王新雷郭钰锋

【Author】 TIAN Xue-qin;CHEN Yu-heng;LIU Zhuang-zhuang;WANG Ying-wei;WANG Xin-lei;GUO Yu-feng;Huajian Power Design and Research Institute;School of Electrical Engineering and Automation, Harbin Institute of Technology;

【机构】 国网经济技术研究院有限公司哈尔滨工业大学电气工程及自动化学院

【摘要】 随着以新能源为供给主体的新型电力系统建设思想提出,确保电网供电可靠性、提升新能源消纳率成为两大难题,从需求侧出发,开发长时间尺度电力市场不失为一种新的破局方法。山东电网提出参考需求侧资源参与容量市场方式,结合现有备用辅助市场,建立以负荷资源为主体的长周期备用市场,实现电网消纳新能源与参与主体创收的双赢。该文参考容量市场出清结果,基于K均值聚类算法,采用七大物理特征指标降维,针对调节潜力最大的工业负荷建立多时间长度响应潜力量化评估模型,并采用交付年与日负荷曲线采样年的行业用电量之比作为长周期修正系数,将求得的需求响应潜力映射至长周期市场,为负荷资源参与长周期备用市场能力的评估提供一定的参考。

【Abstract】 With the new power system construction idea with new energy as the main supply, ensuring the power supply reliability of the power grid and improving the consumption rate of new energy have become two major problems. Starting from the demand side, developing a long-time scale power market is a new way to break the situation. Shandong power grid proposes to refer to the demand side resources to participate in the capacity market, combined with the existing standby auxiliary market, establish a long-term standby market with load resources as the main body, so as to realize the win-win situation of power grid consumption of new energy and income generation of participants. Referring to the clearing results of the capacity market, based on the K-means clustering algorithm, using seven physical characteristic indexes to reduce the dimension, this paper establishes a multi time length response potential quantitative evaluation model for the industrial load with the greatest regulation potential, and uses the ratio of the industrial power consumption in the delivery year and the sampling year of the daily load curve as the correction coefficient to map the obtained demand response potential to the long-term market, It provides a certain reference for the evaluation of load resources participating in the long-term reserve market.

  • 【文献出处】 节能技术 ,Energy Conservation Technology , 编辑部邮箱 ,2022年04期
  • 【分类号】TM73
  • 【下载频次】107
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