<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="5.3.1" xmlns="http://www.crossref.org/schema/5.3.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schema/deposit/crossref5.3.1.xsd">
 <head>
  <doi_batch_id>aspg-41-3846-1792226215</doi_batch_id>
  <timestamp>20261017083655</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
  </depositor>
  <registrant>American Scientific Publishing Group</registrant>
 </head>
 <body>
  <journal>
   <journal_metadata language="en">
    <full_title>Metaheuristic Optimization Review</full_title>
    <abbrev_title>MOR</abbrev_title>
    <issn media_type="electronic">3066-280X</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>4</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>M.</given_name>
      <surname>El-Said</surname>
      <affiliations>
       <institution>
        <institution_name>Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Marwa M.</given_name>
      <surname>Eid</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Artiﬁcial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This work examines the transformational potential of AI-based decentralized energy systems: P2P renewable energy networks interconnect AI, blockchain technology, and multi-agent systems, thus circumventing the barriers of traditional centralized grids. This paper will trace how their latest trends in real-time energy optimization, secure smart contracts, and autonomous coordination of distributed resources can enhance grid resilience, minimize transmission losses, and democratize energy markets. However, it becomes evident that to enable mass adoption; significant challenges must be addressed regarding renewable energy intermittency, scalability limitations, regulatory loopholes, and cybersecurity threats. Through synthesizing current research and the analytical case of Brooklyn Microgrid, this paper discusses some of the barriers and potential future directions that must be emphasized, such as hybrid optimization models, standardized frameworks, and inclusive design for accelerating transitions towards sustainable and equitable energy systems.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>01</first_page>
     <last_page>04</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3846</item_number>
    </publisher_item>
    <ai:program name="AccessIndicators">
     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/MOR.040101</doi>
     <resource>https://americaspg.com/journal/41/article/3846</resource>
     <collection property="text-mining">
      <item>
       <resource mime_type="application/pdf">https://americaspg.com/storage/articles/manuscripts/01KX4BQAB3741FK4TCXABSNB9W.pdf</resource>
      </item>
     </collection>
    </doi_data>
   </journal_article>
  </journal>
 </body>
</doi_batch>
