Hybrid Multi-Agent Reinforcement Learning and Consensus Control for Dynamic V2G-Enabled Voltage Regulation in Active Distribution Networks

Hybrid Multi-Agent Reinforcement Learning and Consensus Control for Dynamic V2G-Enabled Voltage Regulation in Active Distribution Networks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2026 V1 Latest version Share on Hybrid Multi-Agent Reinforcement Learning and Consensus Control for Dynamic V2G-Enabled Voltage Regulation in Active Distribution Networks Authors : mojtaba ajoudani 0000-0002-6094-1916 [email protected] , Seyed Reza Mosayyebi 0000-0002-6892-3229 , and Ramazan Teimouri Yansari Authors Info & Affiliations https://doi.org/10.22541/au.176792248.87278763/v1 185 views 78 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This Manuscript presents a hybrid, online model free decentralized control framework for coordinated voltage regulation in active low voltage distribution networks (ALVDNs) with high penetrations of photovoltaic (PV) generation and electric vehicle (EV) charging. The proposed three layer architecture integrates multi agent proximal policy optimization (MAPPO) with a modified consensus based coordination layer, enabling cooperative control of both stationary battery energy storage systems (BESS) and dynamic vehicle to grid (V2G) resources. Conventional model based or purely reactive methods often fail to mitigate severe over and under voltage events caused by stochastic, bidirectional power flows and dynamic EV participation. The proposed Hybrid MARL Consensus (HMC) approach leverages a centralized training, decentralized execution paradigm to allow each agent to anticipate voltage excursions based only on local measurements and limited peer to peer communication. Simulation studies on a modified IEEE 33 bus feeder with realistic stochastic profiles of PV generation, demand, and EV mobility show that HMC reduces voltage violation duration by 82.9%, system losses by 18.0%, and BESS daily cycling by 36.8% compared with state of the art benchmarks, while maintaining a 94.7% user satisfaction rate for participating EVs. The framework remains robust under large forecast errors and intermittent communication failures, outperforming baseline schemes by 35–66 percentage points under such uncertain conditions. An accompanying techno economic analysis indicates a payback period of 2.8 years, confirming the practical viability of the proposed HMC framework for future LV distribution systems. Supplementary Material File (new-ver1.docx) Download 2.52 MB Information & Authors Information Version history V1 Version 1 09 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords active networks distribution networks economic forecasting electric vehicle charging multi-agent systems photovoltaic power systems sensitivity analysis vehicle-to-grid voltage regulators Authors Affiliations mojtaba ajoudani 0000-0002-6094-1916 [email protected] Islamic Azad University View all articles by this author Seyed Reza Mosayyebi 0000-0002-6892-3229 Islamic Azad University View all articles by this author Ramazan Teimouri Yansari Islamic Azad University View all articles by this author Metrics & Citations Metrics Article Usage 185 views 78 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation mojtaba ajoudani, Seyed Reza Mosayyebi, Ramazan Teimouri Yansari. Hybrid Multi-Agent Reinforcement Learning and Consensus Control for Dynamic V2G-Enabled Voltage Regulation in Active Distribution Networks. Authorea . 09 January 2026. DOI: https://doi.org/10.22541/au.176792248.87278763/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176792248.87278763/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe40b878e55593a',t:'MTc3OTIwMzYwMA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();