An interval arithmetic-based state estimation for unbalanced active distribution networks

An austere challenge for state estimation (SE) in active distribution networks lies in how to deal with the increasing uncertainties. To solve this problem, an interval arithmetic-based algorithm for state estimation is proposed in this study, with consideration of uncertainty modeling for pseudo-measurements and measurement error of real-time measuring devices. For active distribution networks, the representation of measurement uncertainty as confidence intervals can offer significant advantages over the previous approaches with probabilistic noise. By analogy to the unknown-but-bounded theory, the proposed uncertain problem is formulated as two nonlinear optimization models, then the nonlinear optimization models are transformed into simple linear programming problems by using linear approximation technique. Case studies have been discussed to demonstrate the performance of the proposed methodology.