Adaptive PID-Based Deep Reinforcement Learning for Load Frequency Control in Islanded Microgrids with Heterogeneous Resources and Energy Storage

Adaptive PID-Based Deep Reinforcement Learning for Load Frequency Control in Islanded Microgrids with Heterogeneous Resources and Energy Storage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Adaptive PID-Based Deep Reinforcement Learning for Load Frequency Control in Islanded Microgrids with Heterogeneous Resources and Energy Storage Dana Dana R. S. Suliman, Ali Nasser Hussain, Ahmed K.Ali, Mohammed Alruwaili, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8756353/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Load frequency control (LFC) is an essential measure in maintaining stability in power systems in islanded microgrids that include heterogeneous generation sources and energy storage systems. Conventional PID controllers frequently encounter constraints owing to their static parameters, which fail to accommodate fluctuating loads, indeterminate system parameters, and diverse generation conditions. In the presented paper, a reinforcement learning (RL)-based adaptive PID tuning methodology with the use of Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms is introduced. The proposed RL-PID controllers are trained to achieve an adaptive LFC controller by reducing the frequency variations and control effort in a variety of operating scenarios. Simulations of a heterogeneous-sources-landed microgrid demonstrate that both DDPG- and TD3-based controllers outperform conventional PID controllers in dynamic response, settling time, and robustness to disturbances. Besides, the TD3-PID controller shows better stability and reduced oscillations compared to the DDPG-PID controller, which can be explained by the fact that it improves the policy update mechanism. The results point out the importance of RL for adaptive and robust load–frequency control in modern microgrids. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Adaptive Load–Frequency Control Reinforcement learning Deep Deterministic Policy Gradient Deep Deterministic Policy Gradient PID tuning islanded microgrid energy storage Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 22 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor invited by journal 12 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 01 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8756353","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590887717,"identity":"8a2d9316-11c9-46a2-a920-1acaf9a71de7","order_by":0,"name":"Dana Dana R. S. 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Conventional PID controllers frequently encounter constraints owing to their static parameters, which fail to accommodate fluctuating loads, indeterminate system parameters, and diverse generation conditions. In the presented paper, a reinforcement learning (RL)-based adaptive PID tuning methodology with the use of Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms is introduced. The proposed RL-PID controllers are trained to achieve an adaptive LFC controller by reducing the frequency variations and control effort in a variety of operating scenarios. Simulations of a heterogeneous-sources-landed microgrid demonstrate that both DDPG- and TD3-based controllers outperform conventional PID controllers in dynamic response, settling time, and robustness to disturbances. Besides, the TD3-PID controller shows better stability and reduced oscillations compared to the DDPG-PID controller, which can be explained by the fact that it improves the policy update mechanism. The results point out the importance of RL for adaptive and robust load\u0026ndash;frequency control in modern microgrids.\u003c/p\u003e","manuscriptTitle":"Adaptive PID-Based Deep Reinforcement Learning for Load Frequency Control in Islanded Microgrids with Heterogeneous Resources and Energy Storage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 16:41:16","doi":"10.21203/rs.3.rs-8756353/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T08:05:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T19:35:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151296174089566516422827289135939434138","date":"2026-02-20T16:03:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T09:44:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133518764529194239050262706561316783251","date":"2026-02-13T12:23:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T06:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294596329911985684577188195029900931230","date":"2026-02-13T06:04:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155161846764923147381027074010578685604","date":"2026-02-13T04:00:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T03:55:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-13T02:45:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T05:25:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T05:24:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-01T12:57:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f3521e9-52c7-4124-91c2-7330bec113e2","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62882949,"name":"Physical sciences/Energy science and technology"},{"id":62882950,"name":"Physical sciences/Engineering"},{"id":62882951,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-23T09:10:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 16:41:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8756353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8756353","identity":"rs-8756353","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}