Approximate Latent Factor Algorithm for Scenario Selection and Weighting in Transmission Expansion Planning

• Published in: IEEE transactions on power systems Vol. 35; no. 2; pp. 1099 - 1108
• Main Authors: Bukenberger, Jesse P., Webster, Mort D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; clustering algorithms; Mathematical analysis; Monte Carlo methods; Optimization; Planning; Power system reliability; principal component analysis; Reliability; sampling methods; Uncertainty
• ISSN: 0885-8950; 1558-0679; 1558-0679
• DOI: 10.1109/TPWRS.2019.2942925

One major difficulty in transmission expansion planning is selecting the representative scenarios to use to evaluate candidate transmission networks. The variability in demand and renewable generation makes the inclusion of several scenarios critical when calculating reliability and cost, but including too many scenarios in an optimization is computationally intractable. To reduce the number of representative operating conditions needed to obtain an accurate approximation of the system, we propose a method rooted in multivariate statistics that exploits the latent correlative structure between different scenarios and network configurations. The proposed algorithm includes an objective and rigorous way to select a subset of scenarios that provide as much information about the system as possible, and a method to accurately approximate the system cost from that scenario subset. The result is a set of scenarios and weights that are easily incorporated into traditional transmission expansion planning formulations. We apply this to a 312-bus WECC model with 8,736 distinct operating conditions. The transmission plans found with the proposed method are more reliable and have a lower total cost than those from other scenario reduction techniques, as well as a smaller error between the expected system performance from the optimization objective and the actual system performance.