Two-Layer Distributionally Robust Planning for Hydro-Wind-Solar-Storage Systems Based on Reinforcement Learning

Two-Layer Distributionally Robust Planning for Hydro-Wind-Solar-Storage Systems Based on Reinforcement Learning | 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 Research Article Two-Layer Distributionally Robust Planning for Hydro-Wind-Solar-Storage Systems Based on Reinforcement Learning Xiaodong Zhang, Kang Yu, Jingwei Zhu, Yongcheng Yu, Yun Ao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6837141/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Energy Informatics → Version 1 posted 13 You are reading this latest preprint version Abstract Under the large-scale integration of wind turbine and photovoltaic into the grid, the power system faces the challenge of insufficient flexibility for regulation. Coordinated planning of hydro-wind-solar-storage systems can effectively mitigate the output volatility of renewable energy sources. This paper proposes a distributionally robust planning method for hydro-wind-solar-storage systems based on the Wasserstein distance. First, taking into account the spatiotemporal correlations of factors such as wind speed and solar irradiance, an auxiliary classifier generative adversarial network (AC-GAN) is employed to generate a set of wind turbine and photovoltaic output scenarios. Then, a bilevel capacity planning model is constructed for the integrated system. The upper level aims to minimize investment costs by determining the optimal energy storage capacity, while the lower level focuses on minimizing operational costs through optimizing storage operation states and the output of various devices. Subsequently, an improved proximal policy optimization (PPO) algorithm, grounded in the Markov decision process framework, is used to solve the model. Finally, an actual case study based on a hydro-wind-solar system in Qinghai China is conducted to validate the effectiveness of the proposed method. Capacity planning Hydro-wind-solar-storage systems Reinforcement learning Distributionally robust Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Energy Informatics → Version 1 posted Editorial decision: Revision requested 24 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviewers invited by journal 18 Jun, 2025 Editor assigned by journal 18 Jun, 2025 Submission checks completed at journal 18 Jun, 2025 First submitted to journal 06 Jun, 2025 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. 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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-6837141","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473141268,"identity":"a3b2c286-3e29-4f19-895a-f6bf48b910fe","order_by":0,"name":"Xiaodong Zhang","email":"","orcid":"","institution":"CHN Energy Digital Intelligence Technology Development Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Zhang","suffix":""},{"id":473141269,"identity":"0f6ebd0d-f98c-47fe-b9bd-fa9205332503","order_by":1,"name":"Kang Yu","email":"","orcid":"","institution":"CHN Energy I\u0026C Interconnection Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Yu","suffix":""},{"id":473141270,"identity":"b3d6721e-8cd9-466f-a895-2145af74147e","order_by":2,"name":"Jingwei Zhu","email":"","orcid":"","institution":"CHN Energy I\u0026C Interconnection Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jingwei","middleName":"","lastName":"Zhu","suffix":""},{"id":473141271,"identity":"09a7c6e3-b274-4e2d-a6db-47d2bc746c9c","order_by":3,"name":"Yongcheng Yu","email":"","orcid":"","institution":"CHN Energy I\u0026C Interconnection Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yongcheng","middleName":"","lastName":"Yu","suffix":""},{"id":473141272,"identity":"a4fa222d-5994-46c0-87d8-8c18ddb0527d","order_by":4,"name":"Yun Ao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYNCCChiDjWgtZ0jWwthGihb+9h4z6cJ5d+T5pXsMGD6UHWbgn92AX4vEmTNm0jO3PTOcOeeMAeOMc4cZJO4cIGDNjRwzad5thxMMbuQYMPO2HWYwkEjAr0MerGXO4QR7kJa/xGgxAGtpANoiAdTCSIwWwzPHiq15jh02nHHnWMHBnnPpPBI3CGiRO9688TZPzWF5/tnNGx/8KLOW459BQAsDA4cBhJZgYDgApHgIqQcC9gdwLaNgFIyCUTAKsAIAdU1AYPwa1fkAAAAASUVORK5CYII=","orcid":"","institution":"North China Electric Power University","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Ao","suffix":""}],"badges":[],"createdAt":"2025-06-06 12:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6837141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6837141/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42162-025-00580-y","type":"published","date":"2025-11-03T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95564203,"identity":"fd9a297b-5e7d-49a4-ac5f-92593340e00b","added_by":"auto","created_at":"2025-11-10 16:08:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":674354,"visible":true,"origin":"","legend":"","description":"","filename":"snarticlerevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6837141/v1_covered_1df9fc07-5f1f-483c-8dfc-b9c30e6cf3e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Two-Layer Distributionally Robust Planning for Hydro-Wind-Solar-Storage Systems Based on Reinforcement Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"energy-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"einf","sideBox":"Learn more about [Energy Informatics](https://energyinformatics.springeropen.com)","snPcode":"42162","submissionUrl":"https://submission.nature.com/new-submission/42162/3","title":"Energy Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Capacity planning, Hydro-wind-solar-storage systems, Reinforcement learning, Distributionally robust","lastPublishedDoi":"10.21203/rs.3.rs-6837141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6837141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnder the large-scale integration of wind turbine and photovoltaic into the grid, the power system faces the challenge of insufficient flexibility for regulation. 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