Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience-Reference-Cited by-同舟云学术

Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience

Published:2021-11-13 Issue: Volume: Page:263-293
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Goodman Carolyn,Thornburg Jesse,Ramaswami Shankar Koduvayur,Mohammadi Javad

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-3357-7_11

Reference73 articles.

1. Thornburg, J., Krogh, B., Ustun, T.S.: “Stochastic simulator for smart microgrid planning. In: Association for Computing Machinery Symposium on Computing and Development. Nairobi, Kenya (2016)

2. Keshan, J, Thornburg, and T. S. Ustun, “Comparison of lead-acid and lithium ion batteries for stationary storage in off-grid energy systems. in IET International Conference on Clean Energy and Technology, Nov. 2016

3. J. Mohammadi, “Distributed computational methods for energy management in smart grids,” Ph.D. dissertation, Carnegie Mellon University, 2016

4. J. Thornburg, “A tool for probabilistic evaluation of microgrid operating strategies with demand side management,” Ph.D. dissertation, Carnegie Mellon University, Dec. 2018

5. Thornburg, J., Krogh, B.: “Simulating energy management strategies for microgrids with smart meter demand management,” in 2017 IEEE PES PowerAfrica Conference. Accra, Ghana (2017).Jun

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