Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones.

中文翻译：

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家用电动汽车充电负载模式提取：一种免训练的非侵入式方法

提取家用电动汽车 (REV) 的充电负载模式将有助于电网运营商在调度和需求侧响应管理方面做出明智的决策。由于从住宅的角度来看，综合住宅电器的多态、高频特性，难以准确提取充电负荷模式。为了解决这个问题，本文提出了一种基于住宅智能电表数据的新型充电负荷提取方法，以无创地提取 REV 充电负荷模式。所提出的算法利用充电负载模式的低频特性，并应用两阶段分解技术来提取充电负载的特性。两阶段分解技术主要包括：充电负荷的趋势分量采用季节分解，采用黄土法进行趋势分解，低频近似分量采用离散小波技术分解。此外，基于提取的特征，应用事件监控和动态时间扭曲来估计最近的充电间隔和幅度。所提出算法的主要特点包括：1）提取精度显着提高；2）抗噪声能力强；3）在线执行提取。基于地面实况数据的实验验证了所提出方法与现有方法相比的优越性。基于提取的特征，应用事件监控和动态时间扭曲来估计最近的充电间隔和幅度。所提出算法的主要特点包括：1）提取精度显着提高；2）抗噪声能力强；3）在线执行提取。基于地面实况数据的实验验证了所提出方法与现有方法相比的优越性。基于提取的特征，应用事件监控和动态时间扭曲来估计最近的充电间隔和幅度。所提出算法的主要特点包括：1）提取精度显着提高；2）抗噪声能力强；3）在线执行提取。基于地面实况数据的实验验证了所提出方法与现有方法相比的优越性。