A Data-driven Storage Control Framework for Dynamic Pricing

TL;DR

This paper introduces a data-driven storage control framework that leverages smart meter data and Gaussian Mixture Models to optimize storage management under dynamic pricing, enhancing demand response and market efficiency.

Contribution

It proposes a novel practical control framework based on Gaussian Mixture Models that relaxes prior assumptions and improves storage control under dynamic prices.

Findings

The framework achieves significant performance improvements in numerical simulations.

It effectively relaxes assumptions of prior stylized models.

The approach enhances demand side participation in dynamic pricing markets.

Abstract

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.