Probabilistic Joint State Estimation for Operational Planning

• Published in: IEEE transactions on smart grid 2019-01, Vol.10 (1), p.601-612
• Main Authors: Weng, Yang, Negi, Rohit, Ilic, Marija D.
• Format: Article
• Language: English
• Subjects: and message passing; Computational modeling; Computer simulation; Computing time; Confidence intervals; distributed algorithms; Generators; graphical model; Graphical models; Mathematical model; Message passing; operational planning; Planning; Probabilistic logic; probabilistic state estimation; Renewable penetration; State estimation; State variable; Statistical analysis; Strategic planning; Uncertainty
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2017.2749369

Due to a high penetration of renewable energy, power systems operational planning today needs to capture unprecedented uncertainties in a short period. Fast probabilistic state estimation (SE), which creates probabilistic load flow estimates, represents one such planning tool. This paper describes a graphical model for probabilistic SE modeling that captures both the uncertainties and the power grid via embedding physical laws, i.e., KCL and KVL. With such a modeling, the resulting maximum a posteriori (MAP) SE problem is formulated by measuring state variables and their interactions. To resolve the computational difficulty in calculating the marginal distribution for interested quantities, a distributed message passing method is proposed to compute MAP estimates using increasingly available cyber resources, i.e., computational and communication intelligence. A modified message passing algorithm is then introduced to improve the convergence and optimality. Simulation results illustrate the probabilistic SE and demonstrate the improved performance over traditional deterministic approaches via: 1) the more accuracy mean estimate; 2) the confidence interval covering the true state; and 3) the reduced computational time.