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1.智能电网教育部重点实验室(天津大学),天津市 300072
2.中国电力科学研究院有限公司,北京市 100192
3.新型电力系统运行与控制全国重点实验室(清华大学),北京市100084
Received:01 May 2022,
Revised:2022-07-02,
Published Online:25 August 2022,
Published:25 September 2022
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孔祥玉,刘超,陈宋宋等.考虑动态过程的可调资源集群多时间节点响应潜力评估方法[J].电力系统自动化,2022,46(18):55-64.
KONG Xiangyu,LIU Chao,CHEN Songsong,et al.Assessment Method for Multi-time-node Response Potential of Adjustable Resource Cluster Considering Dynamic Process[J].Automation of Electric Power Systems,2022,46(18):55-64.
孔祥玉,刘超,陈宋宋等.考虑动态过程的可调资源集群多时间节点响应潜力评估方法[J].电力系统自动化,2022,46(18):55-64. DOI: 10.7500/AEPS20220501001.
KONG Xiangyu,LIU Chao,CHEN Songsong,et al.Assessment Method for Multi-time-node Response Potential of Adjustable Resource Cluster Considering Dynamic Process[J].Automation of Electric Power Systems,2022,46(18):55-64. DOI: 10.7500/AEPS20220501001.
随着高比例可再生能源的接入,负荷侧可调节资源成为各类电网平衡调节业务的重要手段。为获取可调资源集群在各种不确定性因素影响下不同时间节点响应潜力的概率分布,提出了考虑动态过程的可调资源集群多时间节点响应潜力评估方法。该方法首先提出考虑响应动态过程的响应潜力评估指标,然后使用集成经验模态分解和堆栈自编码器提取可调资源用电特征,接着将该特征用于求取消费者心理学模型中的关键参数,最终形成各时间节点下可调资源集群响应潜力的概率分布。最后,通过算例分析验证了所提方法的有效性,并分析了典型业务场景下集群的有效响应潜力。
With the access of the high proportion of renewable energy
the load-side adjustable resources have become an important tool in various balance adjustment businesses of the power grid. To obtain the probability distribution of response potential of adjustable resource clusters at different time nodes under the influence of various uncertain factors
this paper proposes an assessment method for multi-time-node response potential of adjustable resource clusters considering the dynamic process. Firstly
the response potential assessment indices considering the dynamic process of the response are obtained. Secondly
the ensemble empirical mode decomposition and the stacked autoencoder are adopted to extract the electricity consumption features of adjustable resources
and the features are used to obtain key parameters in the consumer psychology model. Thirdly
the probability distribution of the response potential of the adjustable resource cluster at each time node is formed. Finally
the effectiveness of the proposed method is verified by the example analysis
and the effective response potential of the cluster in typical business scenarios is analyzed.
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