Kullback-Leibler Divergence-Based Distributionally Robust Unit Commitment Under Net Load Uncertainty
| العنوان: | Kullback-Leibler Divergence-Based Distributionally Robust Unit Commitment Under Net Load Uncertainty |
|---|---|
| المؤلفون: | Sahin Albayrak, Ogun Yurdakul, Fikret Sivrikaya |
| المصدر: | 2021 IEEE Madrid PowerTech. |
| بيانات النشر: | IEEE, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Mathematical optimization, Kullback–Leibler divergence, Computer science, media_common.quotation_subject, Probabilistic logic, Ambiguity, Set (abstract data type), Electric power system, Power system simulation, Optimization and Control (math.OC), FOS: Mathematics, Probability distribution, Divergence (statistics), Mathematics - Optimization and Control, media_common |
| الوصف: | The deepening penetration of renewable resources into power systems entails great difficulties that have not been surmounted satisfactorily. An issue that merits special attention is the short-term planning of power systems under net load uncertainty. To this end, we work out a distributionally robust unit commitment methodology that expressly assesses the uncertainty associated with net load. The principal strength of the proposed methodology lies in its ability to represent the probabilistic nature of net load without having to set forth its probability distribution. This strength is brought about by the notion of ambiguity set, for the construction of which the Kullback-Leibler divergence is employed in this paper. We demonstrate the effectiveness of the proposed methodology on real-world data using representative studies. The sensitivity analyses performed provide quantitative answers to a broad array of what if questions on the influence of divergence tolerance and dataset size on optimal solutions. Keywords: data-driven optimization, distributionally robust optimization, uncertainty, unit commitment. arXiv admin note: text overlap with arXiv:2011.05314 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::639c322fd45d3d813b742473bb8b7ac0 https://doi.org/10.1109/powertech46648.2021.9494918 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....639c322fd45d3d813b742473bb8b7ac0 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |