Multiple dynamic pricing for demand response with adaptive clustering-based customer segmentation in smart grids

• Published in: Applied energy Vol. 333; p. 120626
• Main Authors: Meng, Fanlin, Ma, Qian, Liu, Zixu, Zeng, Xiao-Jun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023
• Subjects: Adaptive customer segmentation; Clustering; Demand response; Multiple dynamic pricing; Smart grids; Multiple dynamic pricing; Demand response; Smart grids; Clustering; Adaptive customer segmentation
• ISSN: 0306-2619
• DOI: 10.1016/j.apenergy.2022.120626

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. •A novel multiple pricing approach is proposed for demand response in smart grids.•Create an integrated machine learning and optimization decision making framework.•An adaptive clustering based customer segmentation is developed.•The adaptive clustering can capture customers’ behaviour changes.•Multiple pricing can achieve better profits for retailers than uniform pricing.