Robust Data-driven Profile-based Pricing Schemes

TL;DR

This paper proposes a robust, data-driven electricity pricing scheme that leverages load profiles and marginal system costs, ensuring market robustness and efficiency through an optimized clustering algorithm.

Contribution

It introduces a novel robust pricing scheme based on user marginal costs and develops an efficient optimal k-means clustering algorithm tailored for this application.

Findings

The load profile-based scheme may cause market loopholes.

The marginal cost-based scheme guarantees robustness.

An efficient optimal k-means algorithm was designed.

Abstract

To enable an efficient electricity market, a good pricing scheme is of vital importance. Among many practical schemes, customized pricing is commonly believed to be able to best exploit the flexibility in the demand side. However, due to the large volume of consumers in the electricity sector, such task is simply too overwhelming. In this paper, we first compare two data driven schemes: one based on load profile and the other based on user's marginal system cost. Vulnerability analysis shows that the former approach may lead to loopholes in the electricity market while the latter one is able to guarantee the robustness, which yields our robust data-driven pricing scheme. Although k-means clustering is in general NP-hard, surprisingly, by exploiting the structure of our problem, we design an efficient yet optimal k-means clustering algorithm to implement our proposed scheme.