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Cited by 169Optimal Power Flow DistributionSmart Grid Energy ManagementElectric Power System Optimization

The DOI matches its Crossref record; 81 of the 82 references it cites were found in a registry or an index.

Hosting Capacity of the Power Grid for Renewable Electricity Production and New Large Consumption Equipment

Open access
Bollen, Math, Rönnberg, Sarah·2017·Energies
DOI 10.3390/en10091325
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Abstract

After a brief historical introduction to the hosting-capacity approach, the hosting capacity is presented in this paper as a tool for distribution-system planning under uncertainty. This tool is illustrated by evaluating the readiness of two low-voltage networks for increasing amounts of customers with PV panels or with EV chargers. Both undervoltage and overvoltage are considered in the studies presented here. Probability distribution functions are calculated for the worst-case overvoltage and undervoltage as a function of the number of customers with PV or EV chargers. These distributions are used to obtain 90th percentile values that act as a performance index. This index is compared with an overvoltage or undervoltage limit to get the hosting capacity. General aspects of the hosting-capacity calculations (performance indices, limits, and calculation methods) are discussed for a number of other phenomena: overcurrent; fast voltage magnitude variations; voltage unbalance; harmonics and supraharmonics. The need for gathering data and further development of models for existing demand is emphasised in the discussion and conclusions.

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Crossref · 10.3390/en10091325

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Figures

Image 1
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Image 2
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Figure 3 Probability distribution (cumulative distribution function) for the highest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The red dashed vertical line is the overvoltage limit at 110% of the nominal voltage. Figure 3. Probability distribution (cumulative distribution function) for the highest voltage (worst-case voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The red dashed vertical line is the overvoltage limit at 110% of the nominal voltage.
Figure 3 Probability distribution (cumulative distribution function) for the highest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The red dashed vertical line is the overvoltage limit at 110% of the nominal voltage. Figure 3. Probability distribution (cumulative distribution function) for the highest voltage (worst-case voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The red dashed vertical line is the overvoltage limit at 110% of the nominal voltage.CC-BY · From the PDF
Figure 4 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network. Figure 4. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network. Figure 4. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network.
Figure 4 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network. Figure 4. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network. Figure 4. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network.CC-BY · From the PDF
Figure 5 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network; 4 kW production per PV installation. Figure 5. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network; 4 kW production per PV installation.
Figure 5 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network; 4 kW production per PV installation. Figure 5. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network; 4 kW production per PV installation.CC-BY · From the PDF
Figure 6 75th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network. Figure 6. 75th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network.
Figure 6 75th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network. Figure 6. 75th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 6-customer rural network.CC-BY · From the PDF
Figure 7 Probability distribution (cumulative distribution function) for the lowest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The black dashed vertical line is the nominal voltage. Figure 7. Probability distribution (cumulative distribution function) for the lowest voltage (worst-case voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The black dashed vertical line is the nominal voltage. Figure 7. Probability distribution (cumulative distribution function) for the lowest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The black dashed vertical line is the nominal voltage.
Figure 7 Probability distribution (cumulative distribution function) for the lowest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The black dashed vertical line is the nominal voltage. Figure 7. Probability distribution (cumulative distribution function) for the lowest voltage (worst-case voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The black dashed vertical line is the nominal voltage. Figure 7. Probability distribution (cumulative distribution function) for the lowest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 6-customer rural network. The different colours refer to the six different customers. The black dashed vertical line is the nominal voltage.CC-BY · From the PDF
Figure 8 Probability distribution (cumulative distribution function) for the highest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 28-customer suburban network. Figure 8. Probability distribution (cumulative distribution function) for the highest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 28-customer suburban network. Figure 8. Probability distribution (cumulative distribution function) for the highest voltage (worst-case voltage) for increasing amount of single-phase connected solar power in the 28-customer suburban network.
Figure 8 Probability distribution (cumulative distribution function) for the highest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 28-customer suburban network. Figure 8. Probability distribution (cumulative distribution function) for the highest voltage (worstcase voltage) for increasing amount of single-phase connected solar power in the 28-customer suburban network. Figure 8. Probability distribution (cumulative distribution function) for the highest voltage (worst-case voltage) for increasing amount of single-phase connected solar power in the 28-customer suburban network.CC-BY · From the PDF
Figure 9 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 28-customer suburban network. Figure 9. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 28-customer suburban network.
Figure 9 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 28-customer suburban network. Figure 9. 90th percentile of the worst-case overvoltage as a function of the number of customers with PV in the 28-customer suburban network.CC-BY · From the PDF
Figure 10 10th percentile of the lowest voltage (worst-case voltage) as a function of the number of EV chargers in a 28-customer suburban network. A consumption of 5750 W (25 A) per charger has been used in the calculations. Figure 10. 10th percentile of the lowest voltage (worst-case voltage) as a function of the number of EV chargers in a 28-customer suburban network. A consumption of 5750 W (25 A) per charger has been used in the calculations.
Figure 10 10th percentile of the lowest voltage (worst-case voltage) as a function of the number of EV chargers in a 28-customer suburban network. A consumption of 5750 W (25 A) per charger has been used in the calculations. Figure 10. 10th percentile of the lowest voltage (worst-case voltage) as a function of the number of EV chargers in a 28-customer suburban network. A consumption of 5750 W (25 A) per charger has been used in the calculations.CC-BY · From the PDF
Figure 11 Probability distribution (cumulative distribution function) of the highest single-phase current through the distribution transformer, with increasing number of customers with single-phase connected PV. Figure 11. Probability distribution (cumulative distribution function) of the highest single-phase current through the distribution transformer, with increasing number of customers with single-phase connected PV. Figure 11. Probability distribution (cumulative distribution function) of the highest single-phase current through the distribution transformer, with increasing number of customers with single-phase connected PV.
Figure 11 Probability distribution (cumulative distribution function) of the highest single-phase current through the distribution transformer, with increasing number of customers with single-phase connected PV. Figure 11. Probability distribution (cumulative distribution function) of the highest single-phase current through the distribution transformer, with increasing number of customers with single-phase connected PV. Figure 11. Probability distribution (cumulative distribution function) of the highest single-phase current through the distribution transformer, with increasing number of customers with single-phase connected PV.CC-BY · From the PDF
Figure 12 Four different percentiles (50th: red star; 75th: green circle; 90th: blue plus; 95th: black triangle) for the current through the transformer, as a function of the number of customers (out of 83) with single-phase connected PV. Figure 12. Four different percentiles (50th: red star; 75th: green circle; 90th: blue plus; 95th: black triangle) for the current through the transformer, as a function of the number of customers (out of 83) with single-phase connected PV. Figure 12. Four different percentiles (50th: red star; 75th: green circle; 90th: blue plus; 95th: black triangle) for the current through the transformer, as a function of the number of customers (out of 83) with single-phase connected PV.
Figure 12 Four different percentiles (50th: red star; 75th: green circle; 90th: blue plus; 95th: black triangle) for the current through the transformer, as a function of the number of customers (out of 83) with single-phase connected PV. Figure 12. Four different percentiles (50th: red star; 75th: green circle; 90th: blue plus; 95th: black triangle) for the current through the transformer, as a function of the number of customers (out of 83) with single-phase connected PV. Figure 12. Four different percentiles (50th: red star; 75th: green circle; 90th: blue plus; 95th: black triangle) for the current through the transformer, as a function of the number of customers (out of 83) with single-phase connected PV.CC-BY · From the PDF
Figure 13 Supraharmonics from a fluorescent lamp ( left ) and from a heat pump ( right ). Figure 13. Supraharmonics from a fluorescent lamp ( left ) and from a heat pump ( right ).
Figure 13 Supraharmonics from a fluorescent lamp ( left ) and from a heat pump ( right ). Figure 13. Supraharmonics from a fluorescent lamp ( left ) and from a heat pump ( right ).CC-BY · From the PDF
Image 14
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Show all 82 references

Research works printed but not linked: 7

  1. Dugan, R.C., McGranaghan, M.F., Santoso, S., and Beaty, H.W. (2012). Electric Power Systems Quality, McGraw-Hill.
  2. McGranaghan, M.F. (2017, January 18–22). Grid modernization challenges for the integrated grid. Proceedings of the IEEE PowerTech Manchester, Manchester, UK.
  3. Bahramirad, S. (2017, January 18–22). Design and planning of the grid of the future. Proceedings of the IEEE PowerTech Manchester, Manchester, UK.
  4. (2017, July 24). Distribution Test Feeders, IEEE PES Distribution System Analysis Subcommittee’s Distribution Test Feeder Working Group. Available online: https://ewh.ieee.org/soc/pes/dsacom/testfeeders/.
  5. Wiczynski, G. (October, January 28). Description of voltage fluctuations in LV power network with the use of PST indicator and voltage fluctuation indices. Proceedings of the 13th International Conference on Harmonics and Quality of Power (ICHQP), Wollongong, Australia.
  6. Ackermann, F., Moghadam, H., Meyer, J., Mueller, S., Domagk, M., Santjer, F., Athamna, I., and Klosse, R. (2016, January 14–15). Characterization of harmonic emission of individual wind turbines and pv inverters—Part II: Photovoltaic inverters. Proceedings of the 6th Solar Integration Workshop, Vienna, Austria.
  7. Meyer, J., Klatt, M., and Grevener, A. (2017, January 4–6). Supraharmonics—Future challenges in the frequency range 2–150 kHz, Keynote presentation. Proceedings of the International Conference on Renewable Energy and Power Quality (ICREPQ), Malaga, Spain.

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Metadata

Authors
Bollen, M., & Rönnberg, S.
Affiliations
Luleå University of Technology (SE)
Corresponding author
  • Bollen, Math
  • Rönnberg, Sarah
ORCID
Venue
Energies
Publisher
MDPI AG
Published
2017-09-02
Volume
10
Issue
9
Pages
1325
ISSN
1996-1073 (ISSN-L)
DOI
10.3390/en10091325
OpenAlex
W2752735451
Cited by
169
References
82
Citations by year
2026: 10 · 2025: 14 · 2024: 17 · 2023: 32 · 2022: 23 · 2021: 19 · 2020: 23 · 2019: 19 · 2018: 12
FWCI
6.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Energies (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Energiforsk
  • Skellefteå Kraft Elnät
  • Umeå Energi
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
Keywords
hosting capacity, distribution network planning, uncertainty, photovoltaic generation, electric vehicle charging stations, low-voltage distribution networks, undervoltage, overvoltage, performance index, overcurrent, voltage unbalance, harmonics, supraharmonics

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