Data-driven Clustering and Feature-based Retail Electricity Pricing with Smart Meters
66 Pages Posted: 22 Oct 2020 Last revised: 3 Dec 2023
N. Bora Keskin
Duke University - Fuqua School of Business
Yuexing Li
Johns Hopkins University - Carey Business School
Nur Sunar
University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School
Date Written: December 2, 2023
Abstract
We consider an electric utility company that serves retail electricity customers over a discrete-time horizon. In each period, the company observes the customers' consumption as well as high-dimensional features on customer characteristics and exogenous factors. A distinctive element of our work is that these features exhibit three types of heterogeneity—over time, customers, or both. Based on the consumption and feature observations, the company can dynamically adjust the retail electricity price at the customer level. The consumption depends on the features: there is an underlying structure of clusters in the feature space, and the relationship between consumption and features is different in each cluster. Initially, the company knows neither the underlying cluster structure nor the corresponding consumption models. We design a data-driven policy of joint spectral clustering and feature-based pricing and show that our policy achieves near-optimal performance, i.e., its average regret converges to zero at the fastest achievable rate. This work is the first to theoretically analyze joint clustering and feature-based pricing with different types of feature heterogeneity. Our case study based on real-life smart meter data from Texas illustrates that our policy increases company profits by more than 100% over a three-month period relative to the company policy and is robust to various forms of model misspecification.
Keywords: spectral clustering, feature-based dynamic pricing, data-driven analysis, retail electricity, smart meter, lasso regularization, exploration-exploitation
Suggested Citation: Suggested Citation
N. Bora Keskin (Contact Author)
Duke University - Fuqua School of Business ( email )
100 Fuqua Drive
Durham, NC 27708-0120
United States
HOME PAGE: http://faculty.fuqua.duke.edu/~nk145/
Yuexing Li
Johns Hopkins University - Carey Business School ( email )
100 International Drive
Baltimore, MD 21202
United States
4102344761 (Phone)
HOME PAGE: http://https://yuexing-li.com
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