Estimating Demand Flexibility Using Siamese LSTM Neural Networks
Guangchun Ruan,
Daniel S. Kirschen,
Haiwang Zhong
et al.
Abstract: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 using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as de… Show more
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