[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118353-en":3,"doc-seo-118353-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118353,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Social Welfare evaluation during demand response programs execution considering machine learning-based load profile clustering - Applied Energy","Demand response programs are widely adopted in smart grids to balance generation and consumption, but they can also reshape customer consumption behaviors and influence social welfare. This paper develops a mathematical model to evaluate social welfare during DRP execution, using customer satisfaction as the core driver. The framework integrates linear and nonlinear DRP formulations and incorporates the Coefficient of Participation (CoP) to reflect active participation share. Load patterns from distribution network customers are clustered via an Affinity Propagation machine-learning method to tailor DRPs by cluster similarity, then social welfare levels are computed from equipment usage and time-of-day switching. Real distribution-network data validate the model’s effectiveness.","Applied Energy 357 (2024) 122518  \nContents lists available at ScienceDirect  \nApplied Energy  \njournal [homepage: www.elsevier.com/locate/apenergy](homepage: www.elsevier.com/locate/apenergy)  \nSocial welfare evaluation during demand response programs execution considering machine learning-based load profile clustering  \nFarid Moazzena, Majid Alikhanib, Jamshid Aghaei c, *, M.J. Hossaina  \na School of Electrical and Data Engineering, University of Technology Sydney, Australia b Independent Researcher  \nc School of Engineering and Technology, Central Queensland University, Australia  \nH I G H L I G H T S  \n• Development of a mathematical model for assessing Social Welfare during DRPs execution, considering customer satisfaction.  \n• Integration of Coefficient of Participation (CoP) as an important factor in evaluating SW during DRP implementation.  \n• Development of Affinity Propagation algorithm to cluster load patterns, enabling tailored DRPs to maximize effectiveness.  \nA R T I C L E I N F O  \nKeywords:  \nDemand response programs Smart grids  \nAffinity propagation algorithm Machine learning  \nLoad classification  \nG R A P H I C A L A B S T R A C T  \n\n|  |\n| --- |\n| A B S T R A C T |\n\nIn the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster  \n* Corresponding author.  \nE-mail address: [j.aghaei@cqu.edu.au](j.aghaei@cqu.edu.au) (J. Aghaei).  \n[https://doi.org/10.1016/j.apenergy.2023.122518](https://doi.org/10.1016/j.apenergy.2023.122518)  \nReceived 23 December 2022; Received in revised form 2 December 2023; Accepted 15 December 2023 Available online 23 December 2023  \n0306-2619/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nF. Moazzen et al.  \nApplied Energy 357 (2024) 122518  \nin terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model.  \n\n| Nomenclature\u003Cbr>Indices\u003Cbr>i,j,t Index of time\u003Cbr>s Index of scenario\u003Cbr>z,z’ Index of case point\u003Cbr>x,x’ Index of candidate example\u003Cbr>k,v Index of number of electrical devices\u003Cbr>Parameters and variables\u003Cbr>A(i) Incentive of DRPs in the ith hour\u003Cbr>B0 (i) Customer's income considering load amount equal to d0","cbCaiaXX4FA9COMZ","https://ap.wps.com/l/cbCaiaXX4FA9COMZ","pdf",2826729,1,15,"English","en",105,"# Social Welfare Evaluation in DRP Execution\n## Mathematical Model and Customer Satisfaction\n## Coefficient of Participation (CoP)\n## Load Profile Clustering with Affinity Propagation\n## Social Welfare Computation and Validation","[{\"question\":\"How does the paper define social welfare in demand response program execution?\",\"answer\":\"It formulates a mathematical model where customer satisfaction is assumed the primary factor contributing to social welfare during DRP implementation.\"},{\"question\":\"What role does the Coefficient of Participation (CoP) play?\",\"answer\":\"CoP measures the percentage of customers who actively participate in each DRP and is used as a significant indicator in assessing the social welfare level.\"},{\"question\":\"Why are load patterns clustered, and how is clustering performed?\",\"answer\":\"Clustering accounts for the sparsity and variety of customers by grouping similar load patterns so satisfaction can be aligned per cluster. The paper uses an Affinity Propagation (AP) machine-learning clustering algorithm.\"}]","Social Welfare evaluation during demand response programs execution considering machine learning-based load profile clustering - Applied Energy | PDF",1785683249,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"social-welfare-evaluation-during-demand-response-programs-execution-considering-machine-learning-based-load-profile-clustering-applied-energy","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/social-welfare-evaluation-during-demand-response-programs-execution-considering-machine-learning-based-load-profile-clustering-applied-energy/118353/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the paper define social welfare in demand response program execution?","Question",{"text":75,"@type":76},"It formulates a mathematical model where customer satisfaction is assumed the primary factor contributing to social welfare during DRP implementation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does the Coefficient of Participation (CoP) play?",{"text":80,"@type":76},"CoP measures the percentage of customers who actively participate in each DRP and is used as a significant indicator in assessing the social welfare level.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are load patterns clustered, and how is clustering performed?",{"text":84,"@type":76},"Clustering accounts for the sparsity and variety of customers by grouping similar load patterns so satisfaction can be aligned per cluster. The paper uses an Affinity Propagation (AP) machine-learning clustering algorithm.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]