Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908660449411072 |
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| author | Fang, Linhan Silva-Rodriguez, Jesus Li, Xingpeng |
| author_facet | Fang, Linhan Silva-Rodriguez, Jesus Li, Xingpeng |
| contents | Widespread electric vehicle (EV) adoption introduces new challenges for distribution grids due to large, localized load increases, stochastic charging behavior, and limited data availability. This paper proposes two data-driven methods to estimate residential EV charging profiles using real-world customer meter data from CenterPoint Energy serving the Houston area. The first approach applies a least-squares estimation to extract average charging rates by comparing aggregated EV and non-EV meter data, enabling a statistical method for starting and ending charge times. The second method isolates EV load from meter profiles and applies a kernel density estimation (KDE) to develop a probabilistic charging model. Both methods produce a distinct "u-shaped" daily charging profile, with most charging occurring overnight. The validated profiles offer a scalable tool for utilities to better anticipate EV-driven demand increases and support proactive grid planning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_13861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation Fang, Linhan Silva-Rodriguez, Jesus Li, Xingpeng Systems and Control Widespread electric vehicle (EV) adoption introduces new challenges for distribution grids due to large, localized load increases, stochastic charging behavior, and limited data availability. This paper proposes two data-driven methods to estimate residential EV charging profiles using real-world customer meter data from CenterPoint Energy serving the Houston area. The first approach applies a least-squares estimation to extract average charging rates by comparing aggregated EV and non-EV meter data, enabling a statistical method for starting and ending charge times. The second method isolates EV load from meter profiles and applies a kernel density estimation (KDE) to develop a probabilistic charging model. Both methods produce a distinct "u-shaped" daily charging profile, with most charging occurring overnight. The validated profiles offer a scalable tool for utilities to better anticipate EV-driven demand increases and support proactive grid planning. |
| title | Data-Driven EV Charging Load Profile Estimation and Typical EV Daily Load Dataset Generation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.13861 |