An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation

Pertl, Michael; Carducci, Francesco; Tabone, Michaelangelo D.; Marinelli, Mattia; Kiliccote, Sila; Kara, Emre

doi:10.1109/tii.2018.2865433

Cited by 44 publications

(19 citation statements)

References 25 publications

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“…By bidding less than the full capacity, the aggregator of a large group of EVs can make sure that the departure of a few EVs does not compromise the delivery of the service, as the remaining capacity can be used to compensate. The overall charged energy or state of charge (SOC) of a large fleet is also more predictable than the SOC of individual EVs and the aggregator can distribute the charging and discharging between the EVs to align the SOC, so it is only necessary to estimate the average [14].…”

Section: Introductionmentioning

confidence: 99%

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Thingvad

Ziras

Marinelli

2019

Journal of Energy Storage

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Section: Introductionmentioning

confidence: 99%

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Thingvad

Ziras

Marinelli

2019

Journal of Energy Storage

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“…A multivariate probability analysis can improve the accuracy of the data if further interdependence information of individual probability is given [15]. Besides, a aggregated consideration of available data can be a interesting research initiative to reduce the uncertainty and complexity in the simulation model [16]. This is out of scope of the present paper.…”

Section: B Ev Fleet Setupmentioning

confidence: 99%

Impact of Uncontrolled Charging with Mass Deployment of Electric Vehicles on Low Voltage Distribution Networks

Shekhar

Mouli

et al. 2020

2020 IEEE Transportation Electrification Conference &Amp; Expo (ITEC)

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This study aims to quantify the impact of uncontrolled charging of Electric Vehicles (EVs) on the low voltage distribution networks with increasing EV penetration levels. For this objective, key indicators are developed to show the magnitude, scale and duration of the impact on the distribution network. The disseminated results are based on the case study with actual data from the existing distribution networks. The findings of this paper can serve as a benchmark for determining the potential of smart EV charging algorithms and/or the extent of necessary infrastructural reinforcement that the grid operators must incorporate.

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“…They can be differentiated according to the time horizon, for which a flexibility request is scheduled. While most disaggregate a profile comprised of multiple time slots [4,23,33,37,42,44], few papers schedule a single time slot [34,35,40], which is similar to our approach.…”

Section: Flexibility (Dis-)aggregationmentioning

confidence: 99%

Flexibility Disaggregation under Forecast Conditions

Danner

Seidemann

Lechl

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Stationary battery energy storage systems and electric vehicles become more and more popular at households with local photovoltaic generation. Besides improving self-consumption and autarchy, these batteries can provide flexibility to an external utility. Thereby, generation and demand uncertainty, as well as cost optimality, need to be considered when utilizing distributed flexibility.This paper discusses long short-term memory neural networks for photovoltaic generation forecast and persistence models for household load forecast with respect to their applicability in local energy management system optimization. Furthermore, a mixedinteger linear program is proposed to optimally utilize local flexible loads and storage systems. Its solution space yields the flexibility potential, which can be aggregated at flexibility pools. In order to disaggregate flexibility requests to a pool of distributed energy management systems, we propose a heuristic algorithm that can among others minimize the overall flexibility cost or maximize probability of flexibility delivery. The forecast models, the mixed integer linear program and the flexibility disaggregation are evaluated on realistic household photovoltaic and load profiles to demonstrate the full chain from local forecast to flexibility disaggregation under forecast conditions. Our experiments with flexibility disaggregation show that the probability to provide flexibility should not be neglected when it comes to distributed energy management optimization based on forecast models. CCS CONCEPTS• Hardware → Smart grid; • Computing methodologies → Uncertainty quantification; Neural networks.

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An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation

Cited by 44 publications

References 25 publications

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Impact of Uncontrolled Charging with Mass Deployment of Electric Vehicles on Low Voltage Distribution Networks

Flexibility Disaggregation under Forecast Conditions

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