Probabilistic Joint State Estimation for Operational Planning

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.