Psychological modeling for community energy systems

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dc.contributor.authorZhao, Alexis Pengfei
dc.contributor.authorAlhazmi, Mohannad
dc.contributor.authorHuo, Da
dc.contributor.authorLi, Wanzi
dc.date.accessioned2025-03-04T09:38:04Z
dc.date.available2025-03-04T09:38:04Z
dc.date.freetoread2025-03-04
dc.date.issued2025-06-01
dc.date.pubOnline2025-02-04
dc.description.abstractThis paper introduces a novel framework for community energy system (CES) optimization that integrates Social Cognitive Theory (SCT) with Distributionally Robust Optimization (DRO) to address both behavioral and technical challenges. The rapid integration of renewable energy resources and the proliferation of peer-to-peer (P2P) trading platforms necessitate solutions that balance economic efficiency, environmental sustainability, and user engagement under uncertainty. While existing studies focus predominantly on technical optimization, they often neglect the significant influence of behavioral dynamics on community energy systems. This research bridges the gap by explicitly incorporating peer influence, observational learning, and engagement incentives into a robust optimization framework. The proposed methodology models user behavior through SCT, enabling dynamic adjustments to trading patterns and energy-sharing decisions. A DRO model is employed to handle uncertainties in renewable energy generation and demand, ensuring system reliability and resilience. To enhance computational efficiency, a primal–dual algorithm is developed, offering faster convergence compared to traditional DRO methods. The framework is validated through a comprehensive case study on a 50-household microgrid equipped with solar, wind, and storage systems, alongside a P2P trading platform. Results demonstrate the framework's ability to reduce carbon emissions by up to 30%, improve renewable energy utilization to over 90%, and increase trading participation by 25%, compared to baseline scenarios. This study makes four key contributions: (1) introducing SCT-based behavioral modeling to enhance user engagement in CES operations, (2) leveraging DRO to address uncertainties in renewable energy and demand profiles, (3) proposing a primal–dual algorithm for scalable and efficient optimization, and (4) presenting a unified framework that balances economic, environmental, and social objectives. The findings highlight the transformative potential of integrating behavioral and technical approaches for sustainable and resilient community energy management.
dc.description.journalNameEnergy Reports
dc.description.sponsorshipThe authors would like to acknowledge the support provided by Researchers Supporting Project (Project number: RSPD2025R635), King Saud University, Riyadh, Saudi Arabia.
dc.format.extentpp. 2219-2229
dc.identifier.citationZhao AP, Alhazmi M, Huo D, Li W. (2025) Psychological modeling for community energy systems. Energy Reports, Volume 13, June 2025, pp. 2219-2229en_UK
dc.identifier.eissn2352-4847
dc.identifier.elementsID564661
dc.identifier.issn2352-4847
dc.identifier.urihttps://doi.org/10.1016/j.egyr.2025.01.031
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23540
dc.identifier.volumeNo13
dc.languageEnglish
dc.language.isoen
dc.publisherElsevieren_UK
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S2352484725000332?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject40 Engineeringen_UK
dc.subject4008 Electrical Engineeringen_UK
dc.subjectBehavioral and Social Scienceen_UK
dc.subject7 Affordable and Clean Energyen_UK
dc.subject13 Climate Actionen_UK
dc.subject11 Sustainable Cities and Communitiesen_UK
dc.subjectSocial Cognitive Theoryen_UK
dc.subjectDistributionally robust optimizationen_UK
dc.subjectPeer-to-peer tradingen_UK
dc.subjectCommunity energy systemsen_UK
dc.subjectRenewable energy integrationen_UK
dc.subjectBehavioral modelingen_UK
dc.subjectPrimal-dual algorithmen_UK
dc.titlePsychological modeling for community energy systemsen_UK
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2025-01-13

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