Quantification of peak shaving capacity in electric vehicle charging – findings from case studies in Helsinki Region

Fenner, Pascal; Rauma, Kalle; Rautiainen, Antti; Supponen, Antti; Rehtanz, Christian; Järventausta, Pertti

doi:10.1049/iet-stg.2020.0001

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…The measured average parking time at the site where the charging data is measured is 3 h 53 min and the average charged energy is 11.3 kWh, which is much higher in comparison with other studies, such as [30–34]. From the data set, it is not possible to see when the charging is finished, which means that exact average charging powers cannot be directly concluded.…”

Section: Used Data Modelling and Laboratory Setupmentioning

confidence: 81%

“…The reason why 2 h is selected in categories (2) to ( 4) is that the shorter the charging session, the more likely it is that the estimated average charging power is close to the real maximum charging power. In other words, the shorter the charging session, the more likely it is to be inflexible [30]. The mode of the BMW i3 ('low', 'reduced' or 'maximum' mode) is selected randomly.…”

Section: Used Charging Data and Modellingmentioning

confidence: 99%

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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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Section: Used Data Modelling and Laboratory Setupmentioning

confidence: 81%

Section: Used Charging Data and Modellingmentioning

confidence: 99%

Network‐adaptive and capacity‐efficient electric vehicle charging site

Rauma

Simolin

Järventausta

et al. 2021

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

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“…While in the case of most EVs, the load curve starts to decrease during the last 1%–3% of the SoC. Keeping in mind the average daily distances, a large part of the time, the EVs are charged during this decreasing phase [19]. As one EV releases network capacity because of the decreasing current phase of the charging process, this free capacity is reallocated to other EVs.…”

Section: Discussionmentioning

confidence: 99%

“…This is because it may not be economically feasible to size the network of such site to be able to cover the peak demand. Apart from that, many large charging sites, such as shopping centres, have a high percentage of short charging sessions [19]. Thus, an efficient charging management is likely to be reflected as higher SoCs of the batteries and improved customer experience.…”

Section: Discussionmentioning

confidence: 99%

Overcoming non‐idealities in electric vehicle charging management

Rauma

Simolin

Rautiainen

et al. 2021

IET Electrical Syst in Trans

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The inconvenient nature of non-ideal charging characteristics is demonstrated from a power system point of view. A new adaptive charging algorithm that accounts for nonideal charging characteristics is introduced. The proposed algorithm increases the local network capacity utilization rate and reduces charging times. The first unique element of the charging algorithm is exploitation of the measured charging currents instead of ideal or predefined values. The second novelty is the introduction of a short-term memory called expected charging currents. This makes the algorithm capable of adapting to the unique charging characteristics of each vehicle individually without the necessity to obtain any information from the vehicle or the user. The proposed algorithm caters to various non-idealities, such as phase unbalances or the offset between the current set point and the real charging current but is still relatively simple and computationally light. The algorithm is compatible with charging standard IEC 61851 and is validated under different test cases with commercial electric vehicles.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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“…For a history size of 30, the peak power usage reduces by as much as 31% in the evening period compared to raw charging. As a comparison, a recent case study in Finland based on real data from 25,000 charging sessions collected over 2 years from 8 charging sites shows that the peak loads at charging sites can be reduced by up to 55% [20]. However, the optimization strategy used in the study computes the peak load as a ratio of dispensed energy to plugin duration, which requires the knowledge of the latter and corresponds to the hypothetical scenario in the presented study.…”

Section: The Impact Of History Sizementioning

confidence: 99%

Behavior-Neutral Smart Charging of Plugin Electric Vehicles: Reinforcement Learning Approach

Dyo

2022

IEEE Access

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High-powered electric vehicle (EV) charging can significantly increase charging costs due to peak-demand charges. This paper proposes a novel charging algorithm which exploits typically long plugin sessions for domestic chargers and reduces the overall charging power by boost charging the EV for a short duration, followed by low-power charging for the rest of the plugin session. The optimal parameters for boost and low-power charging phases are obtained using reinforcement learning by training on EV's past charging sessions. Compared to some prior work, the proposed algorithm does not attempt to predict the plugin session duration, which can be difficult to accurately predict in practice due to the nature of human behavior, as shown in the analysis. Instead, the charging parameters are controlled directly and are adapted transparently to the user's charging behavior over time. The performance evaluation on a UK dataset of 3.1 million charging sessions from 22,731 domestic charge stations, demonstrates that the proposed algorithm results in 31% of aggregate peak reduction. The experiments also demonstrate the impact of history size on learning behavior and conclude with a case study by applying the algorithm to a specific charge point. INDEX TERMS electric vehicle, smart charging, reinforcement learning, prediction, big data I. INTRODUCTION

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Quantification of peak shaving capacity in electric vehicle charging – findings from case studies in Helsinki Region

Cited by 5 publications

References 18 publications

Network‐adaptive and capacity‐efficient electric vehicle charging site

Network‐adaptive and capacity‐efficient electric vehicle charging site

Overcoming non‐idealities in electric vehicle charging management

Behavior-Neutral Smart Charging of Plugin Electric Vehicles: Reinforcement Learning Approach

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