Comparison of Metaheuristic Algorithms for Photovoltaic Systems Allocation in a Power Distribution Feeder

Abstract

The government's endorsement of renewable energy objectives and the requirement to use carbon-free energy sources to keep up with the growth in energy consumption have expanded the integration of solar photovoltaic (PV) systems in distribution networks. However, an excessive PV penetration may lead to operational threshold violations. PV system allocation that is optimal in terms of placement and sizing can enhance power quality and grid performance. We formulate the allocation of PV systems as a combinatorial mixed-integer nonlinear model to maximize the distribution network PV hosting capacity (PVHC). We chose three differential evolution (DE) mutation strategies, namely DE/rand/1/bin, DE/current-to-best/1/bin, and DE/rand/1/either-or, and the vortex search (VS) algorithm to solve that optimization problem. This study aims to identify the method that solves the PV allocation problem with higher quality. We performed manual parameter tuning to set both the population and iteration numbers for each algorithm. In addition, for the DE mutation strategies, we set the scale factor and crossover rate parameters. The results show that the VS provides the highest grid PVHC.