A Data-Driven Storage Control Framework for Dynamic Pricing

Abstract

Dynamic pricing is both an opportunity and a challenge to the end-users. 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 on the demand side. The decreasing cost of the storage system and the widely deployed smart meters inspire us to design a data-driven storage control framework for dynamic prices. Our work first establishes a stylized model by assuming the knowledge on the structure of dynamic price distributions and designs 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.