Smart Grid Technology Lab – A Full-Scale Low Voltage Research Facility at TU Dortmund University

Spina, Alfio; Rauma, Kalle; Aldejohann, Christoph; Holt, Mara; Maasmann, Jonas; Berg, Patrick; Häger, Ulf; Rettberg, Fritz; Rehtanz, Christian

doi:10.23919/aeit.2018.8577378

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…The experiment was carried out at the Smart Grid Technology Lab [34] at TU Dortmund University. The HIL simulations used the same EVs described in Table 1.…”

Section: Methodsmentioning

confidence: 99%

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Foundation for adaptive charging solutions: Optimised use of electric vehicle charging capacity

et al. 2021

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As electric vehicles (EVs) are emerging, smart and adaptive charging algorithms have become necessary to ensure safe and efficient operation of the grid. In the scientific literature, most of the proposed charging control algorithms focus solely on EV usagerelated behaviour, while the charging characteristics of EVs are overlooked. Herein, realistic charging characteristics are illustrated and discussed. More notably, to overcome the issues caused by the non-idealities in charging characteristics, a new adaptive charging characteristics expectation algorithm is proposed. The objective of this algorithm is to enable accurate estimation of the non-ideal charging characteristics. This can be used to reallocate any unused charging capacity and to ensure that the intended total capacity is used effectively. The effectiveness of the proposed algorithm is demonstrated using hardware-in-the-loop simulations with commercial EVs and real charging data. The results show that the proposed algorithm achieves an 88%-97% capacity usage rate, while the current benchmark solution achieves only 45%.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The experiment was carried out at the Smart Grid Technology Lab [34] at TU Dortmund University. The HIL simulations used the same EVs described in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…The same Smart Grid Technology laboratory [34] and equipment (including the two EVs, the charging point and two energy analysers) are used to measure the realistic non-ideal charging characteristics and to conduct the HIL simulation experiment. The equipment is described in more detail in Section 4.3.…”

Section: Nonideal Charging Characteristicsmentioning

confidence: 99%

Foundation for adaptive charging solutions: Optimised use of electric vehicle charging capacity

et al. 2021

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“…The algorithms are tested through hardware-in-the-loop simulations carried out at TU Dortmund University [36]. The laboratory setup resembles a public parking and charging site with eight AC charging stations (22 kW, 32 A each) and one fast charging station (45 kW, 65 A), that are connected to the same 400 V 3-phase feeder.…”

Section: Laboratory Setupmentioning

confidence: 99%

Network‐adaptive and capacity‐efficient electric vehicle charging site

Rauma

Simolin

Järventausta

et al. 2021

IET Generation Trans & Dist

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The adaptive charging algorithms of today divide the available charging capacity of a charging site between the electric vehicles without knowing how much current each vehicle draws in reality. Thus, they are not able to detect deviations between the current set point at the charging station and the real charging current. This leads to a situation where the charging capacity of the charging site is not used optimally. This paper presents an algorithm including a novel feature, Expected Characteristic Expectation and tested under realistic circumstances. It is demonstrated that the proposed algorithm enhances the adaptability of the charging site, increasing the efficiency of the used network capacity up to about 2 kWh per charging point per day in comparison with the previous benchmark algorithm. The algorithm is able to increase the average monetary benefits of the charging operators by up to around 5.8%, that is 0.6 € per charging point per day. No input, such as departure time, is required from the user. The proposed algorithm has been tested with real electric vehicles and charging stations and is compatible with the IEC 61851 charging standard. The charging algorithm is applicable in practice as it is described in this paper.

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“…The practical implementation of the EV charging control system is carried out at the Smart Grid Technology Lab [29] at TU Dortmund University. The lab included Nissan Leaf (A), BMW i3 (B), and a charging station, which made it possible to conduct the study.…”

Section: Case Descriptionmentioning

confidence: 99%

Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

et al. 2020

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Smart Grid Technology Lab – A Full-Scale Low Voltage Research Facility at TU Dortmund University

Cited by 12 publications

References 20 publications

Foundation for adaptive charging solutions: Optimised use of electric vehicle charging capacity

Foundation for adaptive charging solutions: Optimised use of electric vehicle charging capacity

Network‐adaptive and capacity‐efficient electric vehicle charging site

Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

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