A Q-learning-based Multi-timescale Resilience Enhancement Approach for Power Grids with High Renewables-Reference-Cited by-同舟云学术

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A Q-learning-based Multi-timescale Resilience Enhancement Approach for Power Grids with High Renewables

Published:2024-05-09 Issue: Volume: Page:1919-1924
ISSN:
Container-title:2024 IEEE 2nd International Conference on Power Science and Technology (ICPST)
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Author:

Huang Yanting¹,Zhong Qing²,Wang Akang³,Lin Shunjiang²,Peng Chaoyi⁴,Lei Shunbo¹

Affiliation:

1. The Chinese University of Hong Kong, Shenzhen Shenzhen Research Institute of Big Data,School of Science and Engineering,Shenzhen,China

2. South China University of Technology,School of Electric Power Engineering,Guangzhou,China

3. Shenzhen Research Institute of Big Data,Shenzhen,China

4. China Southern Power Grid,Power Dispatch and Control Center,Guangzhou,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10601630/10601669/10602353.pdf?arnumber=10602353

Reference17 articles.

1. Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

2. How much demand flexibility could have spared texas from the 2021 outage?

3. Resilience and Stability of Ecological Systems

4. Building a resilient UK energy system;Chaudry,2011

5. Predicting Hurricane Power Outages to Support Storm Response Planning

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