New York State's 100% renewable electricity transition planning under uncertainty using a data-driven multistage adaptive robust optimization approach with machine-learning
Power system decarbonization is critical for combating climate change, and handling systems uncertainties is essential for designing robust renewable transition pathways. In this study, a bottom-up data-driven multistage adaptive robust optimization (MARO) framework is proposed to address the power...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-05-01
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| Series: | Advances in Applied Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666792421000123 |