使用智能电表数据和太阳能示例分解客户级电表后PV发电
Disaggregating Customer-level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars
摘要：
客户级屋顶光伏（PV）已被广泛集成到配电系统中。在大多数情况下，PV安装在电表后面（BTM），只记录净需求。因此，本地需求和光伏发电是未知的公用事业。从净需求中分离本地需求和太阳能发电对于提高电网边缘可观测性至关重要。在本文中，提出了一种新的方法分解客户级的BTM光伏发电使用低分辨率，但广泛可用的每小时智能电表数据。所提出的方法利用每月夜间和昼夜本地需求之间的强相关性和光伏发电配置文件之间的高度相似性。首先，一个联合概率密度函数（PDF）的每月夜间和昼夜本地需求的客户构建没有PV，使用高斯混合模型（GMM）。与构建的PDF的偏差用于概率性地评估具有PV的客户的每月太阳能发电量。然后，为了确定这些客户的每小时BTM太阳能发电量，将其估计的每月太阳能发电量分解为每小时的时间尺度;为此，我们提出了一种基于最大似然估计（MLE）的技术，该技术利用每小时的典型太阳能样本。利用强大的月度本地需求相关性和高光伏发电相似性，增强了我们的方法对客户每小时负荷波动的鲁棒性，并实现了高度准确的分解。所提出的方法已被验证使用真实的本地需求和光伏发电数据。
英文摘要：
Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal native demands is constructed for customers without PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers with PVs. Then, to identify hourly BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers' hourly load and enables highly accurate disaggregation. The proposed approach has been verified using real native demand and PV generation data.
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