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

Fanlin Meng, Qian Ma, Zixu Liu, Xiaojun Zeng

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the electricity 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. The proposed multiple pricing framework is evaluated via simulations based on real-world datasets. We find that: 1) the adaptive clustering based approach can capture the dynamically changing consumption patterns of customers, and enable the dynamic group based demand modelling; and 2) the multiple pricing strategy could achieve better profit gain for the retailer compared with the uniform pricing due to its reduced electricity purchasing cost in the wholesale market.
Original languageEnglish
JournalApplied Energy
Publication statusAccepted/In press - 29 Dec 2022

Keywords

  • multiple dynamic pricing
  • demand response
  • adaptive customer segmentation
  • clustering
  • smart grids

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