Cited by 166Power Quality and HarmonicsOptimal Power Flow DistributionElectric Vehicles and Infrastructure

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index; none of the 57 sources its claims cite is checked for support yet.

Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles

Open access
Brinkel, N.B.G., Gerritsma, M.K., AlSkaif, T.A., Lampropoulos, I., van Voorden, A.M., Fidder, H.A., van Sark, W.G.J.H.M.·2020·International Journal of Electrical Power & Energy Systems

Abstract

Cloud transients cause rapid fluctuations in the output of photovoltaic (PV) systems, which can significantly affect the voltage levels in a low-voltage (LV) grid with high penetration of PV systems. These voltage fluctuations may lead to violation of the existing power quality standards. This study estimates the impact of rapid PV output fluctuations on the power quality in an existing LV grid by performing load flow analyses for scenarios in the years 2017, 2030 and 2050 using PV data with 20-second resolution. In this study, we propose a system for the mitigation of PV output fluctuations by altering the charging processes of electric vehicles (EVs) and we assess the effectiveness of the proposed system. Results indicate that PV output fluctuations have minor impact on the voltage levels in the year 2030, but PV output fluctuations induce considerable voltage fluctuations in the year 2050. The magnitude of the voltage fluctuations is dependent on the location in the grid, the installed PV capacity and the grid configuration. These voltage fluctuations can induce visible and annoying light flicker for a significant part of the day in the year 2050. Implementing the proposed system shows that EV technology can contribute in reducing the amount of visible and annoying light flicker considerably, however at the expense of increased charging costs for EV owners.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This study uses 20-second PV generation data and load-flow analysis of an existing Utrecht low-voltage grid to estimate voltage fluctuations and flicker in 2017, 2030, and 2050, and tests EV smart charging/V2G to mitigate them. It finds minor impact until 2030 but considerable flicker risk in 2050, which EV charging can reduce at increased charging cost.

Key findings

  • PV output fluctuations have minor impact on voltage levels in 2030, but induce considerable voltage fluctuations in 2050; impact is highest at the end of feeder lines, minor near the transformer, and depends on installed PV capacity and grid configuration.
  • In 2050 on a summer day with high PV fluctuations, visible light flicker (>1.6 V/20 s) occurs up to 12.8% of the time between 9:00 and 17:00 and annoying flicker (>3.6 V/20 s) up to 2.1% at the end of a feeder line with high installed PV capacity; 2017 values are 0.0% for both, and 2030 summer high-fluctuation values are 1.4% visible and 0.0% annoying.
  • EV mitigation reduces visible and annoying flicker for all tested maximum allowable residual load ramp rates; least flicker occurs with a maximum allowable ramp rate between 20 and 25 kW/20 s. In 2050, EVs can mitigate residual load fluctuations to 1 kW/20 s in all model runs, but in some 2030 runs only to 89 kW/20 s without V2G and 64 kW/20 s with V2G due to low numbers of connected EVs.
  • Higher installed PV capacity increases flicker: at 440 kWp base, 12.8% visible and 2.1% annoying; at 886 kWp, 21.8% visible and 12.1% annoying. Increased charging costs are higher on days with high PV output fluctuations and for scenarios with V2G.

Methods

  • Case study of an existing LV grid in the Lombok district, Utrecht, the Netherlands, serving 340 grid connections with 19 feeder lines; scenarios for 2017, 2030, and 2050, including summer/winter days with high and low PV output fluctuations.
  • Inputs include 20-second PV generation profiles from the Dutch Solar Forecasting and Smart Grids project, EV charging transactions from the Smart Solar Charging database and a probabilistic model, and residential/heat pump load based on measured consumption, standardized profiles, and projections.
  • EV charging is formulated as a linear programming optimization solved with Gurobi/Python at 20-second time steps using Dutch aFRR prices; V2G roundtrip efficiency is 73% and V2G use is limited to 20% of battery capacity. Voltage levels are determined by load-flow analyses in DIgSILENT PowerFactory using the Newton-Raphson method; mitigation scenarios use eleven model runs per scenario.

Limitations

  • Stated: load-flow assumptions include a constant MV voltage, balanced three-phase load flow, and flicker thresholds based on incandescent light bulbs; the EV charging model assumes perfect foresight and full EV availability, and implementation requires short-timescale data exchange that current regulations may not permit for continuous smart-meter data collection.
  • Stated: the study reports voltage levels on a 20-second timescale, while flicker standards require measurements at a very short timescale (<1 s), so it cannot confirm EN-50160 violation; additional flicker standards or a very-short voltage variation index are suggested.
  • Evident: the paper does not directly compare EV mitigation with alternatives such as grid reinforcement, battery systems, or capacitors, and it states that further studies are required to determine the best mitigation technology.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.ijepes.2019.105741

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

37 Sources not found · 23 Own result · 20 Own interpretation · 16 No basis given · 2 Own method

Show all 48 claims

  • It may state two separate claims as one. 33
  • Probably not a substantial claim. 21
50 of 50
  • The authors state this as an assumption.

    All EV charging strategies are based on smart charging, in which aggregators try to minimize the charging costs of EV owners.

    “This study assumes that all EV charging strategies are based on smart charging, in which aggregators try to minimize the charging costs of EV owners.”
  • The authors state this as an assumption.

    Residential load, heat pump load and PV generation are inflexible.

    “As the residential load, heat pump load and PV generation are assumed to be inflexible, this constraint can only be met by alterations in EV charging behavior.”
  • The authors state this as an assumption.

    Perfect balance between all three phases is assumed.

    “Perfect balance between all three phases is assumed, as (i) this saves computational time and (ii) there is high uncertainty about to which phase future PV systems and EV charging stations will be connected.”
  • The authors state this as an assumption.

    In all scenarios, the aggregator aims to minimize the total charging costs of all charging transactions in the assessment timeframe.

    “In all scenarios, the aggregator aims to minimize the total charging costs of all charging transactions n N in the assessment timeframe:”
  • The paper relies on this without support.

    PV output fluctuations are higher in summer than in winter.

    “Although PV output fluctuations are higher in summer than in winter”
  • The paper relies on this without support.

    The grid strength varies between different residential areas in the Netherlands.

    “The grid strength varies between different residential areas in the Netherlands.”
  • The authors state this as an assumption.

    This study assumed a constant voltage level in the MV grid.

    “This study assumed a constant voltage level in the MV grid, while the MV voltage levels fluctuate considerably in practice.”
  • The authors state this as an assumption.

    A balanced load flow model was used, assuming perfect distribution of loads and PV systems among all three phases.

    “Additionally, a balanced load flow model was used in the load flow simulations, assuming perfect distribution of loads and PV systems among all three phases.”
  • The authors state this as an assumption.

    The used EV charging model assumed perfect foresight; PV generation profile for whole simulation period is already known.

    “The used EV charging model assumed perfect foresight; when optimizing the model, the PV generation profile for the whole simulation period is already known to the model.”
  • The paper relies on this without support.

    Cloud transients take a few seconds, while flicker standards values are for a large extent based on voltage fluctuations with a shorter duration.

    “Cloud transients take a few seconds, while the flicker standards values are for a large extent based on voltage fluctuations with a shorter duration.”
  • The paper relies on this without support.

    Cloud transients can cause rapid fluctuations in the output of PV systems, which may affect the power quality in LV grids.

    “Cloud transients can cause rapid fluctuations in the output of PV systems, which may affect the power quality in LV grids.”
  • The authors state this as an assumption.

    The number of local passenger cars remains constant, using the car possession rate in [80].

    “The number of local passenger cars remains constant, using the car possession rate in [80].”
  • The authors state this as an assumption.

    All passenger cars in 2050 will be BEVs, based on the Dutch governmental target to have a 100% zero-emission car fleet by 2050 [81].

    “All passenger cars in 2050 will be BEVs, based on the Dutch governmental target to have a 100% zero-emission car fleet by 2050 [81].”
  • The authors state this as an assumption.

    The EV fleet (BEV + PHEV) will increase linearly between 2017 and 2050.

    “The EV fleet (BEV + PHEV) will increase linearly between 2017 and 2050.”
  • The authors state this as an assumption.

    The ratio PHEV/BEV will decrease linearly from the ratio in 2017 to a ratio of 0 by 2050.

    “The ratio PHEV/BEV will decrease linearly from the ratio in 2017 to a ratio of 0 by 2050.”
  • The authors state this as an assumption.

    The share of the total number of local EVs performing a charging transaction in the investigated grid in a week remains constant over time for each type of EV.

    “The share of the total number of local EVs performing a charging transaction in the investigated grid in a week remains constant over time for each type of EV.”
  • The authors state this as an assumption.

    The growth rate of the number of visiting BEVs and PHEVs per week is assumed to be the same as the growth rate of local BEVs and local PHEVs respectively.

    “The growth rate of the number of visiting BEVs and PHEVs per week is assumed to be the same as the growth rate of local BEVs and local PHEVs respectively.”
17 of 17

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

3 of 3

Citations

Cites

Show all 54 references

Research works printed but not linked: 4

  1. IEEE Power and Energy Society, IEEE 1453 – IEEE Recommended Practice for the Analysis of Fluctuating Installations on Power Systems, vol. 2015. 2015.
  2. van Melle, 2014, Waarde van slimme netten - Welke waarde creëren slimme oplossingen in het distributienetwerk?, Ecofys
  3. Bletterie, 2007, Impact of Photovoltaic generation on voltage variations-how stochastic is PV, CIRED 19th Int Conf Electr, 0513, 21
  4. J. Quirós-Tortós, L. Ochoa, and T. Butler, “How Electric Vehicles and the Grid Work Together,” IEEE Power Energy Mag., no. October 2018, 2018.

Cited by

Sorted by citations

See all 166

Metadata

Authors
Brinkel, N. B. G., Gerritsma, M. K., AlSkaif, T. A., Lampropoulos, I., van Voorden, A. M., Fidder, H. A., & van Sark, W. G. J. H. M.
Affiliations
Utrecht University (NL)
Corresponding author
Brinkel, N.B.G.
Venue
International Journal of Electrical Power & Energy Systems
Publisher
Elsevier BV
Published
2020-06
Volume
118
Pages
105741
ISSN
0142-0615 (ISSN-L)
OpenAlex
W2997806641
Cited by
166
References
54
Citations by year
2026: 12 · 2025: 17 · 2024: 24 · 2023: 30 · 2022: 28 · 2021: 34 · 2020: 21
FWCI
7.31
Type
journal article
Language
en
Open access
Locations
  • International Journal of Electrical Power & Energy Systems (publishedVersion, cc-by-nc-nd)
  • Socio-Environmental Systems Modeling (acceptedVersion, cc-by-nc-nd)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
Funders
European Regional Development Fund
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Power Quality and Harmonics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
Keywords
photovoltaic output fluctuation, cloud transients, low-voltage distribution networks, power quality, voltage fluctuation, electric vehicle charging, light flicker, EV charging cost, photovoltaic penetration, power flow analysis, grid configuration

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review