A Data-driven Storage Control Framework for Dynamic Pricing
This work addresses dynamic pricing management for energy consumers, but it appears incremental as it builds on existing models with a data-driven adaptation.
The paper tackled the challenge of limited flexible resources and intelligent devices in demand-side dynamic pricing by designing a data-driven storage control framework, which demonstrated remarkable performance in numerical studies.
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.