Abstract
Due to the increasing adoption of solar power generation, voltage unbalance estimation gets more attention in sparsely populated rural networks. This paper presents a Monte Carlo simulation augmented with convex mixed-integer quadratic programming to estimate voltage unbalance and maximum photovoltaic penetration. Additionally, voltage unbalance attenuation by proper phase allocation of photovoltaic plants is analysed. Single-phase plants are simulated in low-voltage distribution networks and voltage unbalance is evaluated as a contribution of measured background and photovoltaic-caused unbalance. Voltage unbalance is calculated in accordance with EN 50160 and takes into account 10-minute average values with 5% tolerance condition. Results of the optimization revealed substantial unbalance attenuation with optimal phase selection and increased potential of local generation hosting capacity in case of higher background unbalance.
Highlights
The widespread increase in distributed energy generation has raised much attention in planning and operation of distribution networks
Results of voltage unbalance (VU) assessment with Monte Carlo simulation and optimization paired with Monte Carlo simulations are presented
Monte Carlo simulation was programmed in MATLAB R2020a, while both the quadratic programming (QP) and mixed-integer quadratic programming (MIQP) formulations of the problem were programmed in GAMS
Summary
The widespread increase in distributed energy generation has raised much attention in planning and operation of distribution networks. The importance of the cumulative duration of violations was emphasized in the article, since network standards set the violation tolerance on one week basis Another probabilistic analysis of VU based on the Monte Carlo simulation is presented in [5]. Voltage unbalance estimation by Monte Carlo simulation can predict possible VU levels and is useful in determining solar HC of distribution networks. The presented VU calculation is based on the true definition of VU based on sequence components, and the model is nonconvex. 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.
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