Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems
Author:
Affiliation:
1. University of Houston,Department of Electrical and Computer Engineering,Houston,TX,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10012119/10012131/10012162.pdf?arnumber=10012162
Reference22 articles.
1. Stochastic Unit Commitment in Low-Inertia Grids
2. Economic valuation and pricing of inertia in inverter-dominated power systems;paturet;Master thesis,2019
3. Conditions for Regional Frequency Stability in Power System Scheduling—Part I: Theory
4. Inertia location and slow network modes determine disturbance propagation in large-scale power grids
5. Data‐driven transient frequency stability assessment: A deep learning method with combined estimation‐correction framework
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1. Unit Commitment Considering the malfunctioned Operation of Hybrid Energy Storage Systems in Frequency Stability Support;Journal of Association of Electrical and Electronics Engineers;2023-12-01
2. Unit Commitment Considering the malfunctioned Operation of Hybrid Energy Storage Systems in Frequency Stability Support;The Payam-e-Marefat-Kabul Education University;2023-12-01
3. Selectively Linearized Neural Network Based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems;2023 North American Power Symposium (NAPS);2023-10-15
4. Annual Benefit Analysis of Integrating the Seasonal Hydrogen Storage into the Renewable Power Grids;2023 IEEE Power & Energy Society General Meeting (PESGM);2023-07-16
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