Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties

Abstract

Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO.

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

“…They differ from the classical deterministic SE models in that the outputs of ISE are interval values indicating the "boundary" of states. To solve the ISE problems, the Krawczyk operator (KO) algorithm [220], the modified KO (MKO) algorithm [221], and the MKO in conjunction with the interval constraint-propagation (ICP) algorithm [222] are investigated and applied. In [223], the maximum and minimum values of states are estimated by solving an optimization problem with inequality constraints indicating the boundaries of estimated measurements derived from the measurement uncertainties.…”