Hierarchical energy profile characterization of electric vehicle charging stations integrated with photovoltaic systems based on clustering techniques

被引：0
作者：
Bracale, Antonio [1 ]
Caramia, Pierluigi [1 ]
De Falco, Pasquale [1 ]
Di Noia, Luigi Pio [2 ]
Rizzo, Renato [2 ]
机构：
[1] Department of Engineering, University of Naples Parthenope, Naples
[2] Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples
来源：
IET Renewable Power Generation | 2025年 / 19卷 / 01期
关键词：
electric vehicles; pattern clustering; photovoltaic power systems;
D O I：
10.1049/rpg2.70006
中图分类号：
TM [电工技术];
学科分类号：
0808 ;
摘要：
Secondary and primary substations of networks with electric vehicle (EV) chargers and photovoltaics (PVs) experience net loads characterized by uncertainty. Accurate characterization of EV, PV and net load energy profiles is necessary to plan new installations and to develop forecasting methodologies. This paper provides a novel contribution to the energy profile characterization of EVs and PVs, exploiting clustering techniques in a hierarchical framework to eventually characterize the overall net load profiles. In the proposal, the lower levels of the hierarchy identify clusters of EV load and PV generation profiles at individual installations, alternatively using one clustering technique among DBSCAN, Gaussian mixture models (GMMs), K-means algorithm (KMA), and spectral clustering (SC). The intermediate levels of the hierarchy reconstruct the overall EV load and PV generation profiles through a proposed frequentist combination of the lower-level profiles. The upper level of the hierarchy characterizes the overall net load through a novel approach based on the quantile convolution of the intermediate-level EV and PV profiles. Real EV load and PV generation data are used to evaluate the performance of the presented hierarchical methodology, with relative fitting improvements between 1% and 8% (compared to a two-level hierarchical benchmark) and between 16% and 29% (compared to a direct, non-hierarchical benchmark). © 2025 The Author(s). IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
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