Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response

Simple item page
dc.contributor.authorLi, Yang
dc.contributor.authorHan, Meng
dc.contributor.authorShahidehpour, Mohammad
dc.contributor.authorLi, Jiazheng
dc.contributor.authorLong, Chao
dc.date.accessioned2023-02-13T12:45:01Z
dc.date.available2023-02-13T12:45:01Z
dc.date.issued2023-02-04
dc.description.abstractA community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.en_UK
dc.identifier.citationLi Y, Han M, Shahidehpour M, et al., (2023) Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response, Applied Energy, Volume 335, April 2023, Article number 120749en_UK
dc.identifier.eissn1872-9118
dc.identifier.issn0306-2619
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2023.120749
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19171
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCommunity integrated energy systemen_UK
dc.subjectDistributionally robust optimizationen_UK
dc.subjectUncertainty modelingen_UK
dc.subjectIntegrated demand responseen_UK
dc.subjectRenewable energyen_UK
dc.subjectScenario generationen_UK
dc.titleData-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand responseen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Community_integrated_energy_systems-2023.pdf
Size:
4 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)