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

arXiv: https://arxiv.org/abs/2106.05905 · HTML: https://mlsanity.com/paper/2106.05905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling](https://mlsanity.com/paper/2106.01021/) (2021)
- [Price discrimination for energy trading in smart grid: A game theoretic approach](https://mlsanity.com/paper/1512.03485/) (2015)

## Built on by (most cited first, 1 of 1)

- [CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data](https://mlsanity.com/paper/2601.10494/) (2026)

## Abstract (arXiv)

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.