引用本文: | 黎子律,王星华,伏辰阳,等.考虑可分解多尺度时序特征融合的规划态光荷联合场景生成[J].电力系统保护与控制,2025,53(12):152-164.[点击复制] |
LI Zilü,WANG Xinghua,FU Chenyang,et al.Planning-state PV-load joint scenario generation considering decomposable multi-scale temporal feature fusion[J].Power System Protection and Control,2025,53(12):152-164[点击复制] |
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摘要: |
针对高渗透率可再生能源并网与源荷出力不确定性导致配电网中长期规划偏离实际的问题,提出一种考虑时序特征分解的规划态源荷联合场景生成方法。首先,基于局部加权散点平滑法的季节和趋势分解(seasonal and trend decomposition using LOESS, STL)将光荷时间序列数据分解为平滑且具有不同特征的周期分量、趋势分量和残差分量。其次,利用TimeMixer深度学习模型预测规划年份的周期分量和趋势分量,该模型可以捕捉不同尺度下的时域信息,进一步聚合微观周期和宏观趋势信息。同时,针对具有随机性和不确定性的残差分量,采用时序变化的Copula相关性建模方法表征历史光荷相关特性。然后,结合分量预测结果生成规划态光荷联合场景,并利用多种聚类方法生成典型场景进行分析。最后,以比利时地区Elia电力运营商提供的光荷一体数据进行算例仿真,验证所提方法能生成考虑未来增长变化的规划场景,有效提升场景精度。 |
关键词: 规划场景 时间序列分解 TimeMixer Copula 联合场景 |
DOI:10.19783/j.cnki.pspc.241042 |
投稿时间:2024-08-05修订日期:2024-11-18 |
基金项目:国家自然科学基金项目资助(62273104) |
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Planning-state PV-load joint scenario generation considering decomposable multi-scale temporal feature fusion |
LI Zilü,WANG Xinghua,FU Chenyang,LIU Xixian,HUANG Xiangyuan,ZHAO Zhuoli |
(School of Automation, Guangdong University of Technology, Guangzhou 510006, China) |
Abstract: |
To address the deviation between medium- and long-term distribution network planning and actual conditions caused by high penetration of renewable energy and the uncertainty of source-load output, a planning-state source-load joint scenario generation method considering temporal feature decomposition is proposed. First, the seasonal and trend decomposition using LOESS (STL) method is used to decompose photovoltaic (PV)-load time series data into periodic, trend, and residual components, each with distinct characteristics. Next, the TimeMixer deep learning model is employed to forecast the seasonal and trend components for the planning year. This model captures temporal information across multiple scales and effectively integrates micro-periodic patterns with macro-trend information. Meanwhile, for the residual components, which contain random and unpredictable features, a time-varying Copula dependence modeling method is adopted to characterize the historical PV-load correlation. By combining the forecasted components, PV-load joint scenarios are generated, and various clustering methods are applied to representative scenarios for analysis. Finally, a case study using integrated PV-load data from Belgium’s Elia operator verifies that the proposed method can generate planning scenarios that reflect future growth and significantly improve scenario accuracy. |
Key words: planning scenarios time series decomposition TimeMixer Copula joint scenarios |