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The paper proposes an integrated demand response (DR) optimization strategy that explicitly models load uncertainty. It builds a probabilistic copula-based model for temporal correlation, fits load distributions via kernel density estimation, and generates scenarios with Monte Carlo sampling. A coordinated DR model uses distinct energy storage devices, and an improved column-and-constraint generation algorithm validates performance through simulations, showing improved operational flexibility and overall performance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/an-optimization-method-for-integrated-demand-response-strategies-for-electricity-and-heat-considering-the-uncertainty-of-user-side-loads/450244/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/an-optimization-method-for-integrated-demand-response-strategies-for-electricity-and-heat-considering-the-uncertainty-of-user-side-loads/450244.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-01","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is dispatch optimization of integrated electric-heat systems difficult?","Question",{"text":112,"@type":113},"Because user-side electrical and thermal loads fluctuate and contain time-varying uncertainties, which significantly affect scheduling outcomes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the paper model uncertainty in user-side loads?",{"text":117,"@type":113},"It formulates a probabilistic model using Copula functions to capture temporal correlation, fits distributions with kernel density estimation, and generates randomized uncertainty data using Monte Carlo sampling.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the core mechanism of the proposed integrated demand response strategy?",{"text":121,"@type":113},"It introduces a DR model coordinated through distinct energy storage devices for electricity and heating, and evaluates it using an improved column-and-constraint generation algorithm.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},450244,1790864323,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAn optimization method for integrated demand response strategies for electricity and heat considering the uncertainty of userside loads  \nJiaqi Li1, Delong Zhang1􀀍, Yuheng Wei1, Xuesong Zhou1, Xiangyu Kong1,2, Xianxu Huo3 & Chao Pang3  \nOptimizing the scheduling of integrated electric-heat systems (IEHS) is complex due to fluctuating user-side loads and their associated uncertainties. To address this, this paper proposes an integrated demand response (DR) optimization strategy for IEHS that accounts for load uncertainty. First, a probabilistic model leveraging Copula functions was formulated to capture the temporal correlation of load uncertainties. A non-parametric Kernel Density Estimation method was then employed to fit the load distribution, and randomized load fluctuation data were generated using Monte Carlo sampling to simulate uncertainty. Second, a DR model that incorporates the characteristics of the electric-heat system is introduced. The electrical and heating load are coordinated through distinct energy storage devices. Finally, the effectiveness of the strategy is validated through the application of an improved column-and-constraint generation algorithm. Simulation outcomes indicate that the presented optimization approach substantially improves the operational flexibility and performance of IEHS.  \nKeywords Integrated electric-heat system, Integrated demand response, Uncertainty, Energy storage  \nBackground  \nWith the increasing integration of power systems and heating systems, “electricity substitution” has emerged as a key strategy for promoting the efficient use of clean energy1. In this context, IEHS, as a novel form of integrated energy system, has been widely adopted in energy sector, offering a promising solution to facilitate the green and low-carbon transition2. The integrated demand response for electricity and heat (IDR-EH) plays a crucial role in optimizing energy use, balancing electricity and heat demand, and enhancing system flexibility. However, as system scale continues to expand and user needs diversify, the dynamic changes and uncertainties of user-side loads have increasingly become critical factors influencing the dispatch optimization of electricheat systems. Load uncertainty encompasses not only fluctuations in electrical load but also the time-varying nature of thermal load and the impact of external factors such as weather conditions. Therefore, addressing the uncertainty of user-side loads by proposing a targeted optimization method for demand response strategies in combined heat and power systems is of great significance for improving system scheduling efficiency, reducing operating costs, and ensuring the reliability of heat and power supply3.  \nMotivation for the research  \nAs a typical multi-energy collaborative system, IEHS enhances the operating efficiency of the energy system and enables flexible energy distribution through the optimization of combined heat and power and energy storage. Literature4 applied IEHS to a building group to achieve globally optimal energy management for the  \n1Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin University of Technology, Xiqing District, Tianjin 300384, China. 2School of Electrical and Information Engineering, Tianjin University, Nankai District, Tianjin 300072, China. 3Tianjin Electric Power Company Electric Power Research Institute, Xiqing District, Tianjin 300384, China. 􀀍 email: [zhangdelong@email.tjut.edu.cn](zhangdelong@email.tjut.edu.cn)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nintegrated building cluster. Literature5 optimized a three-layer distributed robust optimization scheduling model for a multi-region interconnected IEHS, which significantly reduces the scheduling cost. Literature6 proposed a robust and proactive scheduling method for IEHS, introduced a wind po","cbCaiaVcmdNyos9I","https://ap.wps.com/l/cbCaiaVcmdNyos9I","pdf",6780862,28,"English","# Background\n# Motivation for the research","[{\"question\":\"Why is dispatch optimization of integrated electric-heat systems difficult?\",\"answer\":\"Because user-side electrical and thermal loads fluctuate and contain time-varying uncertainties, which significantly affect scheduling outcomes.\"},{\"question\":\"How does the paper model uncertainty in user-side loads?\",\"answer\":\"It formulates a probabilistic model using Copula functions to capture temporal correlation, fits distributions with kernel density estimation, and generates randomized uncertainty data using Monte Carlo sampling.\"},{\"question\":\"What is the core mechanism of the proposed integrated demand response strategy?\",\"answer\":\"It introduces a DR model coordinated through distinct energy storage devices for electricity and heating, and evaluates it using an improved column-and-constraint generation algorithm.\"}]","An optimization method for integrated demand response strategies for electricity and heat considering the uncertainty of user-side loads | PDF",1790732623,71]