Defining a synthetic data generator for realistic electric vehicle charging sessions

Lahariya, Manu; Benoit, Dries F.; Develder, Chris

doi:10.1145/3396851.3403509

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…Other recent studies complement this work by using Gaussian mixtures to model the triplet (arrival time, charging duration, energy consumed) in order to characterize EV load. In [123] the triplet is modelled by a multivariate Gaussian mixture while in [134] only the couple (charging duration, energy consumed) is modelled by a Gaussian mixture with the arrival time modelled by an exponential distribution. The results produced are more accurate than for elementary distributions.…”

Section: Statistical Characterizationmentioning

confidence: 99%

“…Instead, statistical characterization techniques with unimodal distributions could yield a sufficient approximation of the phenomenon as proposed in [13,14] along with LM [148][149][150]. The remaining statistical characterization models (mixtures [13,24,123,133,134] and KDEs [135][136][137][138][139]) can capture diverse patterns and thus could be applied to medium-sized datasets. The Paris dataset could also be used to ver-ify the consistency of simple queuing models as they usually struggle to find concrete applications [29,142].…”

Section: Suggested Matching Of Ev Load Models With the Datasets Consideredmentioning

confidence: 99%

“…In most research papers, simulators rely heavily on assumptions derived from travel surveys and not so much on real charging session data [14,15,140]. Nevertheless, a data-driven simulator has been recently proposed in [134] which was trained on real-world charging sessions and thus can represent more accurately real world charging behaviours.…”

Section: Other Types Of Relevant Datamentioning

confidence: 99%

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A Review of Electric Vehicle Load Open Data and Models

et al. 2021

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The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, to review electric vehicle charging load models with their strengths and weaknesses. Third, to provide suggestions on matching the models reviewed to six datasets found in this research that have not previously been explored in the literature. The open data search covered more than 860 repositories and yielded around 60 datasets that are relevant for modelling electric vehicle charging load. These datasets include information on charging point locations, historical and real-time charging sessions, traffic counts, travel surveys and registered vehicles. The models reviewed range from statistical characterization to stochastic processes and machine learning and the context of their application is assessed.

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Section: Statistical Characterizationmentioning

confidence: 99%

Section: Suggested Matching Of Ev Load Models With the Datasets Consideredmentioning

confidence: 99%

Section: Other Types Of Relevant Datamentioning

confidence: 99%

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A Review of Electric Vehicle Load Open Data and Models

et al. 2021

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“…The dataset shows important key figures, such as the time of the charging event or the idle time [10]. In Lahariya et al [13], the authors develop a synthetic data generator for EV-charging sessions based on the ElaadNL dataset. For the synthetic data generator, the authors assume that the EV inter-arrival time follows an exponential distribution.…”

Section: Literature Review 21 Electric Vehicle Charging Data Analysismentioning

confidence: 99%

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Barthel

Schlund

Landes³

et al. 2021

Energies

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A successful transformation of the energy and transportation sector is one of the main targets for our society today. Battery electric vehicles can play a key role in future renewable-based energy supply systems because of their ability to store electrical power. Additionally, they provide significant charging flexibility due to the long parking durations. In this paper, we provide insights into the temporal and power-specific flexibility behavior of three different vehicle fleets. These fleets are pool vehicles of office employees, a public authority, and a logistics company. Several parameters, such as the average charging power per charging event or the average plug-in duration per charging event, are discussed. Additionally, we investigate different charging rates and their impact on the temporal flexibility of the charging events. The data analysis shows that the logistics site has the most homogeneous charging profile as well as high charging flexibility, in contrast to the office and public agency site. The results are of significant importance for future applications in the field of smart charging and ancillary services provision.

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“…In this paper, we present a state of the art model for generating samples of EV session data that will generate synthetic samples of (i) arrival times, (ii) connection times and (iii) charging load, for each EV. We describe this model as synthetic data generator (SDG), as defined in our previous work [19]. This includes temporal statistical modeling of arrivals and modeling of conditional distributions for departures and the energy required for charging the EV.…”

Section: Contributionmentioning

confidence: 99%

Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data

2020

Self Cite

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Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets.

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Defining a synthetic data generator for realistic electric vehicle charging sessions

Cited by 12 publications

References 2 publications

A Review of Electric Vehicle Load Open Data and Models

A Review of Electric Vehicle Load Open Data and Models

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data

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