{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-fully-adaptive-dro-multistage-framework","title":"A Fully Adaptive DRO Multistage Framework Based on MDR for Generation Scheduling under Uncertainty","arxiv_id":null,"date":"2023-01-17","proceeding":"Engineering, Environmental Science 2023 1","authors":["Ying Yang","Linfeng Yang","ZhaoYang Dong"],"abstract":"Abstract—The growing proliferation of wind power into the power grid achieves a low-cost sustainable electricity supply while introducing technical challenges with associ-ated intermittency. This paper proposes a fully adaptive distributionally robust multistage framework based on mixed decision rules (MDR) for generation scheduling un-der uncertainty to adapt wind power respecting non-anticipativity in quick-start unit status decision and dispatch process. Compared with existing multistage mod-els, the proposed framework introduces improved MDR to handle all decision variables to expand the feasible region. Therefore, our model can find a feasible solution to some problems that are not feasible in the traditional models while finding a better solution to feasible problems, so as to better exploit wind energy and accordingly fall consump-tion of fossil fuels. Besides, the proposed model is refor-mulated with advanced optimization methods and im-proved MDR to the mixed integer linear programming (MILP) to address computational intractability. The effec-tiveness and superiority of the proposed model have been validated with case studies using IEEE benchmark systems.","url_abs":"https://api.semanticscholar.org/CorpusID:261060834","url_pdf":"https://optimization-online.org/wp-content/uploads/2023/01/MDR_MS_DRO_UGS_optimization.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-fully-adaptive-dro-multistage-framework","repo_url":"https://github.com/linfengYang/MDR_MS_DRO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"scheduling","task_name":"Scheduling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}