Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids

TL;DR

This paper introduces a scalable demand response method in smart grids that uses adaptive customer segmentation and customized pricing models to optimize profits while considering market constraints.

Contribution

It presents a novel adaptive clustering-based customer segmentation and a multiple dynamic pricing framework for demand response in smart grids.

Findings

Effective customer segmentation improves demand modeling accuracy.

The proposed pricing strategy enhances profit maximization under market constraints.

Simulation results validate the approach using real-world data.

Abstract

In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the retail market. First, an adaptive clustering-based customer segmentation framework is proposed to categorize customers into different groups to enable the effective identification of usage patterns. Second, customized demand models with important market constraints which capture the price-demand relationship explicitly, are developed for each group of customers to improve the model accuracy and enable meaningful pricing. Third, the multiple pricing based demand response is formulated as a profit maximization problem subject to realistic market constraints. The overall aim of the proposed scalable and practical method aims to achieve 'right' prices for 'right' customers so as to benefit various stakeholders in the system such as grid operators, customers and retailers. The proposed multiple pricing framework is evaluated via simulations based on real-world datasets.