Data-driven Clustering and Feature-based Retail Electricity Pricing with Smart Meters

43 Pages Posted: 22 Oct 2020

See all articles by N. Bora Keskin

N. Bora Keskin

Duke University - Fuqua School of Business

Yuexing Li

Duke University, Fuqua School of Business, Students

Nur Sunar

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School

Date Written: September 3, 2020

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. 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 measure the company's performance by its average regret, i.e., the profit loss per period per customer, relative to a clairvoyant who knows the underlying cluster structure and the consumption model in each cluster. We design a data-driven policy of joint spectral clustering and feature-based pricing, and show that its average regret converges to zero at the fastest achievable rate. We conduct case studies based on real-life smart meter data from Texas and simulation experiments. Relative to the company policy, our policy increases company profits by 146% over a three-month period.

Keywords: spectral clustering, feature-based dynamic pricing, data-driven analysis, retail electricity, smart meter, lasso regularization, exploration-exploitation

Suggested Citation

Keskin, N. Bora and Li, Yuexing and Sunar, Nur, Data-driven Clustering and Feature-based Retail Electricity Pricing with Smart Meters (September 3, 2020). Available at SSRN: https://ssrn.com/abstract=3686518 or http://dx.doi.org/10.2139/ssrn.3686518

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

Duke University, Fuqua School of Business, Students ( email )

Durham, NC
United States

Nur Sunar

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School ( email )

McColl Building
Chapel Hill, NC 27599-3490
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
57
Abstract Views
434
rank
415,294
PlumX Metrics