Probabilistic Joint State Estimation for Operational Planning

Yang Weng, Rohit Negi, Marija D. Ilic

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Article number8026175
Pages (from-to)601-612
Number of pages12
JournalIEEE Transactions on Smart Grid
Issue number1
StatePublished - Jan 2019


  • Renewable penetration
  • and message passing
  • distributed algorithms
  • graphical model
  • operational planning
  • probabilistic state estimation

ASJC Scopus subject areas

  • Computer Science(all)


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