  Dynamic pricing is both an opportunity and a challenge to the demand side. It
is an opportunity as it better reflects the real time market conditions and
hence enables an active demand side. However, demand's active participation
does not necessarily lead to benefits. The challenge conventionally comes from
the limited flexible resources and limited intelligent devices in demand side.
The decreasing cost of storage system and the widely deployed smart meters
inspire us to design a data-driven storage control framework for dynamic
prices. We first establish a stylized model by assuming the knowledge and
structure of dynamic price distributions, and design the optimal storage
control policy. Based on Gaussian Mixture Model, we propose a practical
data-driven control framework, which helps relax the assumptions in the
stylized model. Numerical studies illustrate the remarkable performance of the
proposed data-driven framework.
