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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">ipolytech</journal-id><journal-title-group><journal-title xml:lang="ru">iPolytech Journal</journal-title><trans-title-group xml:lang="en"><trans-title>iPolytech Journal</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2782-4004</issn><issn pub-type="epub">2782-6341</issn><publisher><publisher-name>Irkutsk National Research Technical University</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.21285/1814-3520-2026-3-466-480</article-id><article-id custom-type="edn" pub-id-type="custom">OCWIOD</article-id><article-id custom-type="elpub" pub-id-type="custom">ipolytech-1101</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЭНЕРГЕТИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>POWER ENGINEERING</subject></subj-group></article-categories><title-group><article-title>Управление электропотреблением зарядных станций электромобилей как альтернатива реконструкции распределительных сетей</article-title><trans-title-group xml:lang="en"><trans-title>Electric demand management for electric vehicle charging stations as an alternative to distribution network reinforcement</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7242-9100</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Воронин</surname><given-names>В. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Voronin</surname><given-names>V. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Воронин Вячеслав Андреевич, кандидат технических наук, старший научный сотрудник научно-исследовательской лаборатории цифровой трансформации предприятий минерально-сырьевого комплекса, доцент кафедры электроснабжения горных и промышленных предприятий</p><p>650000,г. Кемерово, ул. Весенняя, 28</p></bio><bio xml:lang="en"><p>Vyacheslav A. Voronin, Cand. Sci. (Eng.), Senior Researcher at the Mining Industry Digital Transformation Laboratory, Associate Professor of the Department of Power Supply of Mining and Industrial Enterprises</p><p>28 Vesennaya St., Kemerovo 650000 </p></bio><email xlink:type="simple">voroninva@kuzstu.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0003-0681-3774</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Замарин</surname><given-names>Д. О.</given-names></name><name name-style="western" xml:lang="en"><surname>Zamarin</surname><given-names>D. O.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Замарин Дмитрий Олегович, аспирант, ведущий специалист департамента технологического развития и инноваций ПАО «Россети Урал» </p><p>г. Екатеринбург, ул. Мира, 19;  620026, г. Екатеринбург, ул. Мамина-Сибиряка, 140 </p><p> </p></bio><bio xml:lang="en"><p>Dmitry O. Zamarin, Postgraduate Student,  Leading Specialist at the Department of Technological Development and Innovation, PJSC «Rosseti Ural» </p><p>19, Mira St., Yekaterinburg 620002; 140, Mamin-Sibiryak St., Yekaterinburg 620026 </p></bio><email xlink:type="simple">zamarin-do@rosseti-ural.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Кузбасский государственный технический университет имени Т.Ф. Горбачева</institution><country>Россия</country></aff><aff xml:lang="en"><institution>T.F. Gorbachev Kuzbass State Technical University</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Уральский федеральный университет им. первого Президента России Б.Н. Ельцина</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Ural Federal University named after the first President of Russia B.N. Yeltsin</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>02</day><month>10</month><year>2026</year></pub-date><volume>30</volume><issue>3</issue><fpage>466</fpage><lpage>480</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Воронин В.А., Замарин Д.О., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Воронин В.А., Замарин Д.О.</copyright-holder><copyright-holder xml:lang="en">Voronin V.A., Zamarin D.O.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://ipolytech.elpub.ru/jour/article/view/1101">https://ipolytech.elpub.ru/jour/article/view/1101</self-uri><abstract><p>Целью настоящей работы является исследование эффективности различных методов управления электропотреблением домашних зарядных станций электромобилей для снижения максимумов мощности и сокращения объемов реконструкции распределительной сети при технологическом присоединении электрозарядной инфраструктуры. Исследование выполнено на примере распределительной сети 10 кВ, питающей более шести тыс. абонентов. Максимальные электрические нагрузки определены с помощью имитационного моделирования с учетом особенностей зарядного поведения владельцев электротранспорта, параметров зарядных станций и суточного профиля изменения нагрузки в распределительной сети. Для ограничения потребляемой мощности зарядных станций использованы метод отложенного заряда, а также одно- и двухуровневая система прямого управления электропотреблением. Для каждого метода управления электропотреблением выполнено моделирование профилей нагрузки зарядных станций электромобилей и распределительной сети. Показано, что широкое распространение электромобилей приводит к заметному росту (на 44 %) пиковой нагрузки в распределительной сети низкого и среднего напряжения, что создает риск перегрузки электросетевого оборудования и требует существенных инвестиций в реконструкцию и усиления электрической сети (порядка 70 млн руб.). Рассмотренные методы управления электропотреблением позволили снизить максимум мощности в распределительной сети на 13 % и обеспечить экономический эффект за счет снижения затрат на реконструкцию на сумму до 66,94 млн руб. Показано, что средний недозаряд электромобилей при использовании одно- и двухуровневой системы прямого управления электропотреблением составил всего 0,93–1,55 %. Таким образом, управление электропотреблением зарядных станций электромобилей позволяет значительно сократить затраты на усиление распределительной сети при незначительном влиянии на качество заряда электромобилей.</p></abstract><trans-abstract xml:lang="en"><p>This study aims to evaluate the effectiveness of various demand management methods for residential charging stations in reducing peak demand and the extent of distribution network reinforcement needed to accommodate electric vehicle charging infrastructure. The analysis is performed for a 10‑kV distribution network supplying more than 6,000 customers. Maximum electrical loads were determined using simulations that account for the charging behavior of electric vehicle owners, charging station parameters, and the daily load profile of the distribution network. To limit the power drawn by charging stations, a delayed charging strategy was applied, as well as single‑level and two‑level direct demand control schemes. For each demand management method, the resulting load profiles of both electric vehicle charging stations and the distribution network were modeled. The results show that widespread electric vehicle adoption leads to a substantial increase (44 %) in peak load in low‑ and medium‑voltage distribution networks, posing a risk of equipment overload and necessitating significant investment in distribution network reinforcement (approximately 70 million RUB). The examined demand management methods reduced the network peak demand by 13 %, offering an economic benefit by lowering reinforcement costs by up to 66.94 million RUB. The average incomplete charging associated with the single‑ and two‑level control schemes was only 0.93-1.55 %. Overall, demand management for electric vehicle charging stations can significantly reduce the cost of distribution network reinforcement while having only a minor impact on the charging quality of electric vehicles.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>электромобили</kwd><kwd>электрозарядные станции</kwd><kwd>управление электропотреблением</kwd><kwd>электрический режим</kwd><kwd>имитационное моделирование</kwd></kwd-group><kwd-group xml:lang="en"><kwd>electric vehicles</kwd><kwd>electric vehicle charging stations</kwd><kwd>power consumption management</kwd><kwd>electric mode</kwd><kwd>simulation modeling</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Воронин В.А., Непша Ф.С. Управление напряжением в низковольтных распределительных сетях в условиях внедрения домашних зарядных станций электромобилей // iPolytech Journal. 2024. Т. 28. № 2. С. 261–271. https://doi.org/10.21285/1814-3520-2024-2-261-271. EDN: ZWSVER.</mixed-citation><mixed-citation xml:lang="en">Voronin V.A., Nepsha F.S. Voltage control in low-voltage distribution networks in the context of introducing home charging stations for electric vehicles. iPolytech Journal. 2024;28(2):261-271. (In Russ.). https://doi.org/ 10.21285/1814-3520-2024-2-261-271. EDN: ZWSVER.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Sandström M., Huang P., Bales Ch., Dotzauer E. Evaluation of hosting capacity of the power grid for electric vehicles – a case study in a Swedish residential area // Energy. 2023. Vol. 284. P. 129293. https://doi.org/10.1016/j.energy.2023.129293. EDN: FLQPCT.</mixed-citation><mixed-citation xml:lang="en">Sandström M., Huang P., Bales Ch., Dotzauer E. Evaluation of hosting capacity of the power grid for electric vehicles – a case study in a Swedish residential area. Energy. 2023;284:129293. https://doi.org/10.1016/j.energy.2023.129293. EDN: FLQPCT.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Li Ya., Jenn A. Impact of electric vehicle charging demand on power distribution grid congestion // Proceedings of the National Academy of Sciences of the United States of America. 2024. Vol. 121. Iss. 18. P. e2317599121. https://doi.org/10.1073/pnas.2317599121. EDN: QKBFSW.</mixed-citation><mixed-citation xml:lang="en">Li Ya., Jenn A. Impact of electric vehicle charging demand on power distribution grid congestion. Proceedings of the National Academy of Sciences of the United States of America. 2024;121(18):e2317599121. https://doi.org/10.1073/pnas.2317599121. EDN: QKBFSW.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Lukuyu Ju., Shirley R., Taneja Ja. Managing grid impacts from increased electric vehicle adoption in African cities // Scientific Reports. 2024. Vol. 14. Iss. 1. P. 24320. https://doi.org/10.1038/s41598-024-75039-3. EDN: HLIIAY.</mixed-citation><mixed-citation xml:lang="en">Lukuyu Ju., Shirley R., Taneja Ja. Managing grid impacts from increased electric vehicle adoption in African cities. Scientific Reports. 2024;14(1):24320. https://doi.org/10.1038/s41598-024-75039-3. EDN: HLIIAY.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Visakh A., Selvan M.P. Smart charging of electric vehicles to minimize the cost of charging and the rate of transformer aging in a residential distribution network // Turkish Journal of Electrical Engineering and Computer Sciences. 2022. Vol. 30. Iss. 3. P. 927–942. https://doi.org/10.55730/1300-0632.3819. EDN: VQELDK.</mixed-citation><mixed-citation xml:lang="en">Visakh A., Selvan M.P. Smart charging of electric vehicles to minimize the cost of charging and the rate of transformer aging in a residential distribution network. Turkish Journal of Electrical Engineering and Computer Sciences. 2022;30(3):927-942. https://doi.org/10.55730/1300-0632.3819. EDN: VQELDK.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Van Sambeek H.L., Zweistra M., Hoogsteen G., Varenhorst I.A.M., Janssen S. Gridshield-optimizing the use of grid capacity during increased EV adoption // World Electric Vehicle Journal. 2023. Vol. 14. Iss. 3. P. 68. https://doi.org/10.3390/wevj14030068. EDN: SRPSMS.</mixed-citation><mixed-citation xml:lang="en">Van Sambeek H.L., Zweistra M., Hoogsteen G., Varenhorst I.A.M., Janssen S. Gridshield-optimizing the use of grid capacity during increased EV adoption. World Electric Vehicle Journal. 2023;14(3):68. https://doi.org/10.3390/ wevj14030068. EDN: SRPSMS.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Dreucci D., Yu Yu., Chandra Mouli G.R., Shekhar A., Bauer P. Centralised distribution grid congestion management through EV charging control considering fairness and priority // Applied Energy. 2025. Vol. 384. P. 125417. https://doi.org/10.1016/j.apenergy.2025.125417.</mixed-citation><mixed-citation xml:lang="en">Dreucci D., Yu Yu., Chandra Mouli G.R., Shekhar A., Bauer P. Centralised distribution grid congestion management through EV charging control considering fairness and priority. Applied Energy. 2025;384:125417. https://doi. org/10.1016/j.apenergy.2025.125417.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Aoun A., Adda M., Ilinca A., Ghandour M., Ibrahim H. Dynamic charging optimization algorithm for electric vehicles to mitigate grid power peaks // World Electric Vehicle Journal. 2024. Vol. 15. Iss. 7. P. 324. https://doi.org/10.3390/wevj15070324. EDN: NWECFF.</mixed-citation><mixed-citation xml:lang="en">Aoun A., Adda M., Ilinca A., Ghandour M., Ibrahim H. Dynamic charging optimization algorithm for electric vehicles to mitigate grid power peaks. World Electric Vehicle Journal. 2024;15(7):324. https://doi.org/10.3390/ wevj15070324. EDN: NWECFF.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Воронин В.А. Исследование влияния электрозарядной инфраструктуры электротранспорта на электроэнергетическую систему России: анализ значимости факторов // Известия высших учебных заведений. Проблемы энергетики. 2026. Т. 28. № 1. С. 70–88. https://doi.org/10.30724/1998-9903-2026-28-1-70-88. EDN: YKDHOC.</mixed-citation><mixed-citation xml:lang="en">Voronin V.A. Investigation of the impact of electric vehicle charging infrastructure on the Russian power system: feature importance analysis. Power Engineering: Research, Equipment, Technology. 2026;28(1):70-88. (In Russ.). https://doi.org/10.30724/1998-9903-2026-28-1-70-88. EDN: YKDHOC.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Elsayed M., Zafar K., Esa Yu., Soujad T., Mohamed A. Impact of 100% vehicle electrification on the distribution grid in dense urban regions // Energy Reports. 2024. Vol. 11. P. 5315–5322. https://doi.org/10.1016/j.egyr.2024.05.030. EDN: IRVEMB.</mixed-citation><mixed-citation xml:lang="en">Elsayed M., Zafar K., Esa Yu., Soujad T., Mohamed A. Impact of 100% vehicle electrification on the distribution grid in dense urban regions. Energy Reports. 2024;11:5315-5322. https://doi.org/10.1016/j.egyr.2024.05.030. EDN: IRVEMB.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Roy P., Ilka R., He J., Liao Yu., Cramer A.M., Mccann Ju., et al. Impact of electric vehicle charging on power distribution systems: a case study of the grid in Western Kentucky // IEEE Access. 2023. Vol. 11. P. 49002–49023. https://doi.org/10.1109/ACCESS.2023.3276928. EDN: WURZAL.</mixed-citation><mixed-citation xml:lang="en">Roy P., Ilka R., He J., Liao Yu., Cramer A.M., Mccann Ju., et al. Impact of electric vehicle charging on power distribution systems: a case study of the grid in Western Kentucky. IEEE Access. 2023;11:49002-49023. https:// doi.org/10.1109/ACCESS.2023.3276928. EDN: WURZAL.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Lee Ja.H., Chakraborty D., Hardman S.J., Tal G. Exploring electric vehicle charging patterns: mixed usage of charging infrastructure // Transportation Research Part D: Transport and Environment. 2020. Vol. 79. P. 102249. https://doi.org/10.1016/j.trd.2020.102249. EDN: HLLYVI.</mixed-citation><mixed-citation xml:lang="en">Lee Ja.H., Chakraborty D., Hardman S.J., Tal G. Exploring electric vehicle charging patterns: mixed usage of charging infrastructure. Transportation Research Part D: Transport and Environment. 2020;79:102249. https://doi.org/10.1016/j.trd.2020.102249. EDN: HLLYVI.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Sørensen Å.L., Sartori I., Lindberg K.B., Andresen I. A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study // Sustainable Energy, Grids and Networks. 2023. Vol. 36. P. 101195. https://doi.org/10.1016/j.segan.2023.101195. EDN: XUFMMB.</mixed-citation><mixed-citation xml:lang="en">Sørensen Å.L., Sartori I., Lindberg K.B., Andresen I. A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study. Sustainable Energy, Grids and Networks. 2023;36:101195. https://doi.org/10.1016/j.segan.2023.101195. EDN: XUFMMB.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Almaghrebi A., James K., Al Juheshi F., Alahmad M. Insights into household electric vehicle charging behavior: analysis and predictive modeling // Energies. 2024. Vol. 17. Iss. 4. P. 925. https://doi.org/10.3390/en17040925. EDN: AMGFBH.</mixed-citation><mixed-citation xml:lang="en">Almaghrebi A., James K., Al Juheshi F., Alahmad M. Insights into household electric vehicle charging behavior: analysis and predictive modeling. Energies. 2024;17(4):925. https://doi.org/10.3390/en17040925. EDN: AMGFBH.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Huang P., Ma Zh. Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden // Renewable Energy. 2024. Vol. 236. P. 121511. https://doi.org/10.1016/j.renene.2024.121511. EDN: ZMHMEX.</mixed-citation><mixed-citation xml:lang="en">Huang P., Ma Zh. Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden. Renewable Energy. 2024;236:121511. https://doi. org/10.1016/j.renene.2024.121511. EDN: ZMHMEX.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Baek K., Lee E., Kim J. A dataset for multi-faceted analysis of electric vehicle charging transactions // Scientific Data. 2024. Vol. 11. Iss. 1. P. 262. https://doi.org/10.1038/s41597-024-02942-9. EDN: TLFFID.</mixed-citation><mixed-citation xml:lang="en">Baek K., Lee E., Kim J. A dataset for multi-faceted analysis of electric vehicle charging transactions. Scientific Data. 2024;11(1):262. https://doi.org/10.1038/s41597-024-02942-9. EDN: TLFFID.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Voronin V., Nepsha F., Ilyushin P. Demand factor analysis for electric vehicle charging infrastructure // World Electric Vehicle Journal. 2025. Vol. 16. Iss. 9. P. 537. https://doi.org/10.3390/wevj16090537. EDN: YGLYGG.</mixed-citation><mixed-citation xml:lang="en">Voronin V., Nepsha F., Ilyushin P. Demand factor analysis for electric vehicle charging infrastructure. World Electric Vehicle Journal. 2025;16(9):537. https://doi.org/10.3390/wevj16090537. EDN: YGLYGG.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Ganz K., Vollmuth P. The Impact of DSO Grid-Integration Measures on EV Users in Germany // World Electric Vehicle Journal. 2025. Vol. 16. Iss. 2. P. 110. https://doi.org/10.3390/wevj16020110. EDN: KRJAFI.</mixed-citation><mixed-citation xml:lang="en">Ganz K., Vollmuth P. The Impact of DSO grid-integration measures on EV users in Germany. World Electric Vehicle Journal. 2025;16(2):110. https://doi.org/10.3390/wevj16020110. EDN: KRJAFI.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Kroener N., Förderer K., Lösch M., Schmeck H. State-of-the-art integration of decentralized energy management systems into the German Smart Meter Gateway infrastructure // Applied Sciences. 2020. Vol. 10. Iss. 11. P. 3665. https://doi.org/10.3390/app10113665. EDN: JAAAKU.</mixed-citation><mixed-citation xml:lang="en">Kroener N., Förderer K., Lösch M., Schmeck H. State-of-the-art integration of decentralized energy management systems into the German Smart Meter Gateway infrastructure. Applied Sciences. 2020;10(11):3665. https://doi. org/10.3390/app10113665. EDN: JAAAKU. 20. Buchnev A.O., Rybina K.A. Intelligent electricity metering systems as a factor in the development of “smart cities”. Public Administration. 2025;27(4):43-51. (In Russ.). EDN: EUPAUY.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Бучнев А.О., Рыбина К.А. Интеллектуальные системы учета электроэнергии как фактор развития «умного города» // Государственная служба. 2025. Т. 27. № 4. С. 43–51. EDN: EUPAUY.</mixed-citation><mixed-citation xml:lang="en">Бучнев А.О., Рыбина К.А. Интеллектуальные системы учета электроэнергии как фактор развития «умного города» // Государственная служба. 2025. Т. 27. № 4. С. 43–51. EDN: EUPAUY.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
