{"id":1112,"date":"2025-04-21T13:28:22","date_gmt":"2025-04-21T12:28:22","guid":{"rendered":"https:\/\/www2.isep.ipp.pt\/softcps\/?p=1112"},"modified":"2025-05-06T09:53:46","modified_gmt":"2025-05-06T08:53:46","slug":"release-of-pulsecharge-iot-system","status":"publish","type":"post","link":"https:\/\/www2.isep.ipp.pt\/softcps\/?p=1112","title":{"rendered":"EnergAIze: AI-Powered Energy Management for Renewable Energy Communities"},"content":{"rendered":"\n<p>As part of the EU-funded <u><a href=\"https:\/\/www2.isep.ipp.pt\/softcps\/?p=433\">OPEVA<\/a><\/u><a href=\"https:\/\/www2.isep.ipp.pt\/softcps\/?p=433\">\u00a0<\/a>\u00a0project and his PhD research, \u00a0SoftCPS researcher <u><a href=\"https:\/\/www2.isep.ipp.pt\/softcps\/?p=420\">Tiago Fonseca<\/a><\/u>\u00a0is developing <em>EnergAIze<\/em>, a Multi-Agent Reinforcement Learning (MARL) energy management system designed to optimize the integration of Electric Vehicles (EVs) and Renewable Energy Sources (RES) within Renewable Energy Communities (RECs). The system enables prosumers to optimize energy flexibility from assets like EVs (V2G\/V1G), heat pumps, and batteries, helping both individuals and communities reduce costs, balance demand, and maximize self-consumption.<\/p>\n\n\n\n<figure class=\"wp-block-image alignleft size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"654\" height=\"329\" src=\"https:\/\/www2.isep.ipp.pt\/softcps\/wp-content\/uploads\/2025\/02\/energaize.png\" alt=\"\" class=\"wp-image-1133\" srcset=\"https:\/\/www2.isep.ipp.pt\/softcps\/wp-content\/uploads\/2025\/02\/energaize.png 654w, https:\/\/www2.isep.ipp.pt\/softcps\/wp-content\/uploads\/2025\/02\/energaize-300x151.png 300w\" sizes=\"auto, (max-width: 654px) 100vw, 654px\" \/><\/figure>\n\n\n\n<p>Unlike traditional centralized approaches, <em>EnergAIze<\/em>&nbsp;adopts a decentralized architecture where each household acts as an autonomous agent, making energy decisions based on real-world IoT sensor data. Using a MARL algorithm (MADDPG), prosumers can tailor their energy strategy to minimize costs, prioritize self-consumption, or reduce carbon emissions. Initial tests with real-world data showed reductions in grid energy consumption, lower costs, and improved REC-wide energy sharing, making the system scalable and user-centric.<\/p>\n\n\n\n<p>Evaluated using the <em>CityLearn<\/em>&nbsp;framework and the <em>EVLearn<\/em>&nbsp;extension, <em>EnergAIze<\/em>&nbsp;demonstrated up to 7.24% cost savings at the household level, a 6.45% increase in zero-net energy, and a 9% reduction in peak demand at the community level. Future developments will focus on scaling the system to larger RECs, real-world deployment using edge computing, and integration with platforms like <em>Home Assistant<\/em>.<\/p>\n\n\n\n<p>For more details, refer to the <a href=\"https:\/\/ieeexplore.ieee.org\/document\/10738095\">IEEE SmartGridComm paper<\/a>\u00a0and Tiago&#8217;s <a href=\"https:\/\/recipp.ipp.pt\/entities\/publication\/1972a077-b222-4ae1-a15b-5da964b25193\">master\u2019s thesis<\/a>, or request access to the <a href=\"https:\/\/github.com\/Soft-CPS-Research-Group\">SoftCPS GitHub<\/a>\u00a0repository. More information, please contact <u><a href=\"https:\/\/www2.isep.ipp.pt\/softcps\/?p=420\">Tiago Fonseca<\/a><\/u> or <a href=\"https:\/\/www2.isep.ipp.pt\/softcps\/?p=361\">Luis Lino Ferreira<\/a>.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>As part of the EU-funded OPEVA\u00a0\u00a0project and his PhD research, \u00a0SoftCPS researcher Tiago Fonseca\u00a0is developing EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management system designed to optimize the integration of Electric Vehicles (EVs) and Renewable Energy Sources (RES) within Renewable Energy Communities (RECs). The system enables prosumers to optimize energy flexibility from assets like EVs [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":1113,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-1112","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"blocksy_meta":"","_links":{"self":[{"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=\/wp\/v2\/posts\/1112","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1112"}],"version-history":[{"count":3,"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=\/wp\/v2\/posts\/1112\/revisions"}],"predecessor-version":[{"id":1134,"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=\/wp\/v2\/posts\/1112\/revisions\/1134"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=\/wp\/v2\/media\/1113"}],"wp:attachment":[{"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1112"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1112"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www2.isep.ipp.pt\/softcps\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1112"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}