According to the true definition, VU is a division of two complex numbers. Such a division in decision variable leads to a nonlinear and nonconvex constraint, which steer researchers towards heuristic solving approaches. In recent years, numerous articles presented heuristics with optimal power flow, where VU was one out of several constraints. A study on VU optimization with heuristic algorithm was presented in [13]. The nonlinear nature of the problem formulation is tackled by the particle swarm optimization. Electric vehicle dispatch strategies and their efficiency on VU mitigation were presented. A hybrid method of stochastic optimization, genetic algorithm and fuzzy logic was proposed in [14]. Losses and VU of a three-phase distribution network were minimized with respect to voltage and ampacity limits. A dynamic scenario generation was proposed to tackle the probabilistic nature of PV generation. The presented VU calculation is based on the true definition of VU based on sequence components, and thus the model is nonconvex. The genetic algorithm was utilized in [15] to tackle the nonconvexity of the load flow and VU formulation. While minimizing VU, load flow algorithm was executed with the holomorphic embedding method. As a part of multiobjective electric network optimization, VU minimization was presented in [16]. A novel differential evolution algorithm was employed for the optimization and VU was calculated based on nonconvex sequence components, that were provided by nonlinear load flow algorithm. An empirical continuous metaheuristic was compared with several other heuristic algorithms in [17]. VU minimization was a part of multiobjective function, and, as in previously mentioned works, VU formulation retained nonconvexity. Heuristic algorithms are resilient to tackle nonlinear problems easily. They provide good balance between optimality and runtime. However, they do not guarantee a global solution. Increased computational efficiency of modern computers have allowed researchers to deploy alternative optimization algorithms based on mathematical programming. Contrary to heuristics, mathematical optimization grants global solution for a convex problem. Therefore, the convex formulation of a problem becomes more crucial for mathematical optimization. Network optimization by mathematical programming was conducted in several works. A three-phase network optimization for optimal energy storage dispatch algorithm was presented in [18]. To convexify the model, the VU formulation was relaxed by substituting voltage positive sequence component by the nominal voltage value. In such a way, the division of two decision variables was excluded. Another convex model was presented in [19]. The model was adopted in VU compensation mechanism in microgrids, and formulated the VU constraint as a quadratically constrained programming. However, the model lacks integer variables for the phase selection of single-phase prosumers. A comprehensive robust optimization of day-ahead operation planning was presented in [20]. The problem is formulated as a mixed-integer nonlinear model, which is then linearized and applied in two-stage optimization. The VU is formulated with sequence components, however the positive sequence component is substituted in a similar manner as in [18]. The negative sequence component is calculated on a branch current basis, and the real and imaginary parts of the current are split into two separate variables, before they are merged in a quadratic constraint. The semidefinite programming was utilized to optimize the operation of three-phase network in [21]. The method was then applied for soft open point device, that balanced network voltages by reactive power injection. The original nonlinear, nonconvex problem was reformulated into convex semidefinite programming task. The VU was advocated as the maximum deviation of the average voltage levels, thus abandoning the commonly considered true VU definition. As mentioned previously, holding on to the true definition is vital for VU studies.