Probabilistic-based Optimization for PV Hosting Capacity with Confidence Interval Restrictions

While offering a cost-competitive option and system support benefits. However, its intermittent nature and the stochastic grid environment pose operational and technical challenges, increasing complexity for a secure and reliable grid operation and planning. In this regard, the need for harvesting potential energy from an intermittent source without compromising the actual grid infrastructure and operation is crucial. Thus, this work proposes a novel probabilistic framework for PV hosting capacity assessment and enhancement considering the voltage and current probabilistic restrictions as well as PV inverter power factor control. This work uses the Particle Swarm Optimization (PSO) algorithm that embeds the probabilistic approach based on Monte Carlo simulation (MCS). A sensitivity analysis of the PSO parameters was performed to achieve the best possible results. Tests in the IEEE 33-bus radial distribution system with four PV units with power factor control yield a more realistic PV hosting capacity.