Mixed-integer Quadratic Programming Based Rounding Technique for Power System State Estimation with Discrete and Continuous Variables

Abstract

As a basic energy management system function that processes real-time measurements, state estimation deals with both continuous and discrete variables to estimate states in a power system. One possible source for the discrete parameters is the transformer taps, whose positions should be estimated with high confidence. Given that the tap estimation error causes a network topological modeling inaccuracy, its range should be minimized. Motivated to accurately estimate a state vector including transformer taps, this research presents an estimator based on mixed-integer quadratic programming and a comparison to a recently proposed ordinal optimization formulation based on a sensitivity analysis. Experimental results on the IEEE 30-, 57-, and 118-bus benchmarks reveal a slight superiority of the mixed-integer quadratic programming algorithm in terms of overall accuracy and motivate follow-up research.