Assessing the grid impact of Electric Vehicles, Heat Pumps &amp; PV generation in Dutch LV distribution grids

Damianakis, Nikolaos; Mouli, Gautham Ram Chandra; Bauer, Pavol; Yu, Yunhe

doi:10.1016/j.apenergy.2023.121878

Cited by 18 publications

(2 citation statements)

References 31 publications

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“…In [34], a study of emerging technologies in residential load profiles is proposed. The work conducted in [35] analyzes the impacts of low-carbon technologies, such as electric vehicles and photovoltaics, on the network.…”

Section: Introductionmentioning

confidence: 99%

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

Milis,

Gay,

Alvarez-Herault

et al. 2024

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In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

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Section: Introductionmentioning

confidence: 99%

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

Milis,

Gay,

Alvarez-Herault

et al. 2024

Information

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show abstract

“…Sengor et al [19] presented a variety of voltage constraint-oriented management algorithms to identify potential under-voltage problems caused by the widespread use of HPs, EVs, PVs, and BSS and, eventually, increase the grid's hosting capacity; the investigation was conducted in a real MV/LV grid located in Ireland, considering different penetration levels of these loads. Damianakis et al [20] evaluated the influence of diverse grid regions (urban, suburban, and rural), seasons (winter/summer), and both singular and combined EV-HP-PV penetrations on the voltage deviations and line/transformer overloadings in six real Dutch LV networks. Oliyide et al [21] ran power flow simulations using GridLAB-D for a real urban LV grid in Great Britain to look into the impact of seasonal variation and HP-EV penetrations on the voltage profiles and line/transformer loadings for the years 2020, 2030, 2040, and 2050.…”

Section: Introductionmentioning

confidence: 99%

Systematic Evaluation of Possible Maximum Loads Caused by Electric Vehicle Charging and Heat Pumps and Their Effects on Common Structures of German Low-Voltage Grids

Fakhrooeian,

Pitz,

Scheppat

2024

WEVJ

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In this paper, we present a comprehensive assessment of the effects of residential loads, electric vehicles (EVs), and electric heat pumps (HPs) on low-voltage (LV) grids in urban, suburban, and rural areas of Germany. Firstly, real data are used to determine the typical structures for each LV grid region. Secondly, nine scenarios are defined with different levels of EV and HP penetration. Thirdly, the Low Voltage Load Flow Calculation in the DIgSILENT PowerFactory is performed for all scenarios while taking the simultaneity factor (SF) for each load type into consideration to calculate the minimum voltage and maximum loadings of transformer and lines in each grid; this allows for the grid’s potential bottlenecks to be identified. The network simulations are carried out with the consideration of charging powers of 11 kW and 22 kW in order to evaluate how an increasing EV load in the future may affect the grid’s parameters. To the best of our knowledge, no study in the literature has simultaneously addressed all of the aforementioned topics. The results of this study provide a useful framework that distribution system operators (DSOs) may apply to anticipate the forthcoming challenges and figure out when grid reinforcement will be required.

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Electric vehicle charging technology and its control

Qin,

Soeiro,

Dong

et al. 2024

Control of Power Electronic Converters and Systems: Volume 4

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Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids

Cited by 18 publications

References 31 publications

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

Systematic Evaluation of Possible Maximum Loads Caused by Electric Vehicle Charging and Heat Pumps and Their Effects on Common Structures of German Low-Voltage Grids

Electric vehicle charging technology and its control

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