2019/12/12 by Jingshi Cui, Cui, Jingshi, Haoxiang Wang +5
Computer Science · Engineering · #Computational Engineering #Electric Power System Optimization #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Optimal Power Flow Distribution #Smart Grid Energy Management #and Science (cs.CE) #cs.CE #cs.LG
paper · pdf · doi:10.48550/arxiv.1912.05731
arxiv created 2019/12/12 · openalex publication_date 2019/12/12 · arxiv updated 2019/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.