A Deep Generative Model for Non-intrusive Identification of EV Charging Profiles
A Deep Generative Model for Non-intrusive Identification of EV Charging Profiles
复制标题
用于非侵入式识别电动汽车充电配置文件的深度生成模型
DOI：
10.1109/tsg.2020.2998080
复制
发表时间：
2020
影响因子：
9.6
通讯作者：
Zhao, Dongbo
中科院分区：
文献类型：
--
作者：
Wang, Shengyi;Du, Liang;Ye, Jin;Zhao, Dongbo
电动汽车的普及给电网带来了环境效益和技术挑战。一种能够从广泛使用的智能电表测量数据中准确提取单个电动汽车充电曲线的识别算法引起了人们的广泛关注。提出了一种基于深度生成模型（DGM）的电动汽车充电剖面提取非侵入式识别框架。首先，将DGM设计为嵌入马尔可夫过程的表示层，并用于对可用时间序列数据的联合概率分布进行建模。一个新的贡献是通过变分推理和监督学习获得参数的神经网络来近似后验分布。其次，通过动态规划从DGM中推断电动汽车充电状态。最后，根据电动汽车模型的额定功率和推断状态，重构出期望的电动汽车充电曲线。与基准隐马尔可夫模型相比，该框架能够更好地处理数据中的噪声，计算复杂度更低，召回率更小，整体精度性能更好。通过在Pecan Street数据集上的数值实验验证了该框架的有效性。
The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.