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cs.LG2021★ 45 cited
Estimating Demand Flexibility Using Siamese LSTM Neural Networks
Guangchun Ruan, Daniel S. Kirschen, Haiwang Zhong +2
There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility usin…
cs.LG2017★ 3 cited
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
Yize Chen, Yishen Wang, Daniel Kirschen +1
Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scena…