Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114755
DC FieldValueLanguage
dc.contributorDepartment of Building Environment and Energy Engineering-
dc.creatorTan, H-
dc.creatorYang, A-
dc.creatorLin, Z-
dc.creatorGe, L-
dc.creatorWang, Q-
dc.creatorGao, Y-
dc.date.accessioned2025-08-25T03:35:38Z-
dc.date.available2025-08-25T03:35:38Z-
dc.identifier.issn0306-2619-
dc.identifier.urihttp://hdl.handle.net/10397/114755-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectCollaborative schedulingen_US
dc.subjectElectricity‑hydrogen virtual power planten_US
dc.subjectMulti-parametric programming theoryen_US
dc.subjectUncertain operation regionen_US
dc.titleUncertain operation region of electricity-hydrogen virtual power plant : concept and description methoden_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume396-
dc.identifier.doi10.1016/j.apenergy.2025.126210-
dcterms.abstractThe Electric-Hydrogen Virtual Power Plant aggregates internal distributed energy resources to achieve joint electricity and hydrogen output, providing a new pathway for accommodating surplus renewable energy generation. However, precise modeling methods for the uncertain operating region of EH-VPP remain challenging. In this regard, this paper first defines the uncertain operating region of EH-VPP and constructs its internal optimization model. Then, based on multi-parameter programming theory and the cutting-plane method, the mapping relationship of the EH-VPP electricity‑hydrogen joint output curve is analytically derived. Based on this, the probability density function of the projection points of the electricity‑hydrogen joint curve onto the hydrogen production rate axis is derived, incorporating the known probability distributions of input random variables, thereby enabling probabilistic modeling of the uncertain operating region boundary. Finally, the opportunity constraint method is applied to construct the uncertain operating region of EH-VPP, which is then used in the coordinated scheduling optimization of the electric‑hydrogen integrated energy system. Simulation results show that the proposed method efficiently characterizes the uncertain operating region with an error of less than 0.05 %, supports flexible modeling based on confidence levels, and ensures both scheduling security and computational efficiency in large-scale collaborative scheduling scenarios.-
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationApplied energy, 15 Oct. 2025, v. 396, 126210-
dcterms.isPartOfApplied energy-
dcterms.issued2025-10-15-
dc.identifier.scopus2-s2.0-105008010077-
dc.identifier.eissn1872-9118-
dc.identifier.artn126210-
dc.description.validate202508 bcch-
dc.description.oaNot applicableen_US
dc.identifier.SubFormIDG000078/2025-07en_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by Natural Science Foundation of Hubei Province (No. 2025ABF064).en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2027-10-15en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2027-10-15
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