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Data-Driven Stochastic Scheduling for Energy Integrated Systems
China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycles Rive, Beijing 100038, Peoples R China..
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycles Rive, Beijing 100038, Peoples R China..
China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycles Rive, Beijing 100038, Peoples R China..
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2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 12, article id 2317Article in journal (Refereed) Published
Abstract [en]

As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed data-driven model converges to a risk-neutral model with historical data.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 12, no 12, article id 2317
Keywords [en]
data-driven, stochastic optimization, scheduling optimization, unit commitment
National Category
Astronomy, Astrophysics and Cosmology
Identifiers
URN: urn:nbn:se:kth:diva-255442DOI: 10.3390/en12122317ISI: 000473821400090Scopus ID: 2-s2.0-85068388886OAI: oai:DiVA.org:kth-255442DiVA, id: diva2:1344265
Note

QC 20190820

Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-20Bibliographically approved

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Jin, Ziliang

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