Robust Data-driven Profile-based Pricing Schemes
This work addresses the problem of scalable and secure pricing for electricity market operators, offering a robust solution to prevent market loopholes, though it is incremental in improving existing data-driven methods.
The paper tackles the challenge of designing efficient and robust pricing schemes for electricity markets by comparing data-driven approaches based on load profiles and marginal system costs, finding that the latter ensures robustness and leads to the development of an efficient optimal k-means clustering algorithm for implementation.
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.