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    <journal-meta>
      <journal-id journal-id-type="issn">2411-3336</journal-id>
      <journal-id journal-id-type="eissn">2541-9404</journal-id>
      <journal-title-group>
        <journal-title xml:lang="ru">Записки Горного института</journal-title>
        <journal-title xml:lang="en">Journal of Mining Institute</journal-title>
      </journal-title-group>
      <publisher>
        <publisher-name xml:lang="ru">Санкт-Петербургский горный университет императрицы Екатерины ΙΙ</publisher-name>
        <publisher-name xml:lang="en">Empress Catherine II Saint Petersburg Mining University</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id custom-type="edn" pub-id-type="custom">JSNZWK</article-id>
      <article-id custom-type="pmi" pub-id-type="custom">pmi-16333</article-id>
      <article-id pub-id-type="uri">https://pmi.spmi.ru/pmi/article/view/16333</article-id>
      <article-categories>
        <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>Energy industry</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title xml:lang="en">Evaluation of the impact of the distance determination function  on the results of optimization of the geographical placement  of renewable energy sources-based generation using a metaheuristic algorithm</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Оценка влияния функции определения расстояния на результаты оптимизации географического размещения генерации  на основе возобновляемых источников энергии с применением метаэвристического алгоритма</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name name-style="eastern">
            <surname>Bramm</surname>
            <given-names>Andrei M.</given-names>
          </name>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Брамм</surname>
              <given-names>А. М.</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Bramm</surname>
              <given-names>Andrei M.</given-names>
            </name>
          </name-alternatives>
          <email>am.bramm@urfu.ru</email>
          <contrib-id contrib-id-type="orcid">0000-0002-1868-4389</contrib-id>
          <xref ref-type="aff" rid="aff1"/>
        </contrib>
        <aff-alternatives id="aff1">
          <aff>
            <institution xml:lang="ru">Уральский федеральный университет имени первого Президента России Б.Н.Ельцина (Екатеринбург, Россия)</institution>
          </aff>
          <aff>
            <institution xml:lang="en">Ural Federal University named after the first President of Russia B.N.Yeltsin (Yekaterinburg, Russia)</institution>
          </aff>
        </aff-alternatives>
        <contrib contrib-type="author">
          <name name-style="eastern">
            <surname>Eroshenko</surname>
            <given-names>Stanislav A.</given-names>
          </name>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Ерошенко</surname>
              <given-names>С. А.</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Eroshenko</surname>
              <given-names>Stanislav A.</given-names>
            </name>
          </name-alternatives>
          <email>s.a.eroshenko@urfu.ru</email>
          <contrib-id contrib-id-type="orcid">0000-0001-9617-2154</contrib-id>
          <xref ref-type="aff" rid="aff2"/>
        </contrib>
        <aff-alternatives id="aff2">
          <aff>
            <institution xml:lang="ru">Уральский федеральный университет имени первого Президента России Б.Н.Ельцина (Екатеринбург, Россия)</institution>
          </aff>
          <aff>
            <institution xml:lang="en">Ural Federal University named after the first President of Russia B.N.Yeltsin (Yekaterinburg, Russia)</institution>
          </aff>
        </aff-alternatives>
      </contrib-group>
      <pub-date pub-type="epub" iso-8601-date="2024-09-18">
        <day>18</day>
        <month>09</month>
        <year>2024</year>
      </pub-date>
      <pub-date date-type="collection">
        <year>2025</year>
      </pub-date>
      <volume>271</volume>
      <fpage>141</fpage>
      <lpage>153</lpage>
      <history>
        <date date-type="received" iso-8601-date="2023-10-29">
          <day>29</day>
          <month>10</month>
          <year>2023</year>
        </date>
        <date date-type="accepted" iso-8601-date="2024-04-08">
          <day>08</day>
          <month>04</month>
          <year>2024</year>
        </date>
        <date date-type="rev-recd" iso-8601-date="2025-02-25">
          <day>25</day>
          <month>02</month>
          <year>2025</year>
        </date>
      </history>
      <permissions>
        <copyright-statement xml:lang="ru">© 2024 А. М. Брамм, С. А. Ерошенко</copyright-statement>
        <copyright-statement xml:lang="en">© 2024 Andrei M. Bramm, Stanislav A. Eroshenko</copyright-statement>
        <copyright-year>2024</copyright-year>
        <copyright-holder xml:lang="ru">А. М. Брамм, С. А. Ерошенко</copyright-holder>
        <copyright-holder xml:lang="en">Andrei M. Bramm, Stanislav A. Eroshenko</copyright-holder>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0" xml:lang="ru">
          <license-p>Эта статья доступна по лицензии Creative Commons Attribution 4.0 International (CC BY 4.0)</license-p>
        </license>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0" xml:lang="en">
          <license-p>This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0)</license-p>
        </license>
      </permissions>
      <self-uri xlink:type="simple" xlink:href="https://pmi.spmi.ru/pmi/article/view/16333">https://pmi.spmi.ru/pmi/article/view/16333</self-uri>
      <abstract xml:lang="ru">
        <p>Обеспечение электроснабжения труднодоступных и удаленных территорий Российской Федерации является актуальной проблемой с тех пор, как появилась единая электроэнергетическая система. С учетом положений доктрины энергетической безопасности РФ, трендов по декарбонизации экономики и снижения стоимости оборудования для реализации электроустановок на базе возобновляемых источников энергии электроснабжение удаленных территорий при помощи такой генерации является прямой альтернативой установке дизельной генерации. Геологоразведочные работы преимущественно проводятся на удаленных территориях, где отсутствует возможность централизованного электроснабжения. В связи с развитием промышленных комплексов по добыче сырья, а также сопутствующей бытовой нагрузки актуальной задачей является размещение крупных объектов возобновляемой генерации в районах проведения геологоразведочных работ. Для решения задачи оптимального размещения возобновляемой генерации активно используются метаэвристические методы оптимизации, на результаты которых влияет корректная настройка их внутренних параметров, совместно с большими объемами метеорологических и климатических данных. В статье приводятся результаты исследования влияния функций, определяющих расстояние между агентами метаэвристического алгоритма оптимизации, на результаты оптимизации географического размещения фотоэлектрических и ветровых электростанций. Для сравнения влияния на итоговые результаты в качестве функций расстояния рассмотрены функция евклидова расстояния и функция гаверсинуса. Оценка результатов оптимизации проводилась для солнечных и ветровых электростанций – мощности 45 и 25 МВт на территории Вагайского района Тюменской обл. и Тунгокоченского района Забайкальского края. Полученные результаты показывают низкую степень влияния изменения функции определения расстояния между географическими точками, но обуславливают необходимость ее корректного выбора при оптимизации размещения ветровых электростанций для исключения пропуска локальных оптимумов.</p>
      </abstract>
      <abstract xml:lang="en">
        <p>Since the United Power System was created electrical supply of remote and hard-to-reach areas remains one of the topical issues for the power industry of Russia. Nowadays, usage of various renewable energy sources to supply electricity at remote areas has become feasible alternative to usage of diesel-based generation. It becomes more suitable with world decarbonization trends, the doctrine of energy security of Russia directives, and equipment cost decreasing for renewable energy sources-based power plants construction. Geological exploration is usually conducted at remote territories, where the centralized electrical supply can not be realized. Placement of large capacity renewable energy sources-based generation at the areas of geological expeditions looks perspective due to development of industrial clusters and residential consumers of electrical energy at those territories later on. Various metaheuristic methods are used to solve the task of optimal renewable energy sources-based generation geographical placement. The efficiency of metaheuristics depends on proper tuning of that methods hyperparameters, and high quality of big amount of meteorological and climatic data. The research of the effects of the calculation methods defining distance between agents of the algorithm on the optimization of renewable generation placement results is presented in this article. Two methods were studied: Euclidean distance and haversine distance. There were two cases considered to evaluate the effects of distance calculation method change. The first one was for a photovoltaic power plant with installed capacity of 45 MW placement at the Vagaiskii district of the Tyumen region. The second one was for a wind power plant with installed capacity of 25 MW at the Tungokochenskii district of the Trans-Baikal territory. The obtained results show low effects of distance calculation method change at average but the importance of its proper choose in case of wind power optimal placement, especially for local optima’s identification.</p>
      </abstract>
      <kwd-group xml:lang="ru">
        <title>Ключевые слова</title>
        <kwd>ФЭС</kwd>
        <kwd>ВЭС</kwd>
        <kwd>КИУМ</kwd>
        <kwd>роевой интеллект</kwd>
        <kwd>искусственный интеллект</kwd>
        <kwd>прогнозирование</kwd>
      </kwd-group>
      <kwd-group xml:lang="en">
        <title>Keywords</title>
        <kwd>PV power plants</kwd>
        <kwd>wind power plants</kwd>
        <kwd>Capacity factor</kwd>
        <kwd>swarm intelligence</kwd>
        <kwd>artificial intelligence</kwd>
        <kwd>forecasting</kwd>
      </kwd-group>
      <funding-group>
        <funding-statement xml:lang="ru">Исследование выполнено при финансовой поддержке Министерства науки и высшего образования Российской Федерации в рамках Программы развития Уральского федерального университета имени первого Президента России Б.Н.Ельцина в соответствии с программой стратегического академического  лидерства «Приоритет-2030».</funding-statement>
        <funding-statement xml:lang="en">The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University named after the first President of Russia B.N.Yeltsin within the Priority-2030 Program) is gratefully acknowledged.</funding-statement>
      </funding-group>
    </article-meta>
  </front>
  <body/>
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