Prediction of availability and charging rate at charging stations for electric vehicles

Bikcora, Can; Refa, Nazir; Verheijen, Lennart; Weiland, S.

doi:10.1109/pmaps.2016.7764216

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…Space heating non-dimensional demand time series for all European countries are obtained using the empirical methodology proposed by Ruhnau et al [56]. Subsequently, these nondimensional and country-specific time series are rescaled to the total amount of each country's heat demand (residential and commercial) that is met by either P2HT or DH-CHP technologies, according to the ProRES scenario.…”

Section: Heatingmentioning

confidence: 99%

The potential of sector coupling in future European energy systems: Soft linking between the Dispa-SET and JRC-EU-TIMES models

et al. 2020

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The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines

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Section: Heatingmentioning

confidence: 99%

The potential of sector coupling in future European energy systems: Soft linking between the Dispa-SET and JRC-EU-TIMES models

et al. 2020

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show abstract

“…There are three types of chargers in the study area: slow chargers (7kW), fast chargers (22kW), and rapid chargers (≥43kW). 3 In the present study, a three-month charging session dataset from March 5, 2018, to June 4, 2018 (91 days) is used. Table 1 reports the descriptive statistics.…”

Section: Datasetmentioning

confidence: 99%

“…from 30 minutes to several hours). However, there are still few studies that address this issue, and they are mainly based on conventional econometric or time series methodologies with limited accuracy (Bikcora et al, 2016;Motz et al 2021; Soldan et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

Faye

2022

Energy

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“…Such forecasts can inform the smart charging algorithms at a spatially aggregated scale. For the local management were developed predictions of charging station occupancy [26,27] and day ahead charging probabilities for individual EVs [28] in the form of time series.…”

Section: Prediction Problemsmentioning

confidence: 99%

The role of asymmetric prediction losses in smart charging of electric vehicles

Straka¹,

Buzna²,

Refa³

et al. 2022

International Journal of Electrical Power & Energy Systems

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Prediction of availability and charging rate at charging stations for electric vehicles

Cited by 17 publications

References 18 publications

The potential of sector coupling in future European energy systems: Soft linking between the Dispa-SET and JRC-EU-TIMES models

The potential of sector coupling in future European energy systems: Soft linking between the Dispa-SET and JRC-EU-TIMES models

Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

The role of asymmetric prediction losses in smart charging of electric vehicles

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