  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.
