Study of the hosting capacity of photovoltaic distributed generators in low voltage distribution networks: A probabilistic approach using Monte Carlo simulations

This paper aims to present the study and quantification of the hosting capacity (HC) of a low voltage distribution network composed by photovoltaic distributed generation. The methodology used becomes possible through the implementation of the Probabilistic Method of Monte Carlo with the use of the Python programming language, where the connection point of the distributed photovoltaic generation, power of the generators and the amount of generation systems is randomly chosen. To perform the calculation of the power flow, it is used the OpenDSS software (Open Distribution System Simulator) integrated into the tool in Python from a DLL (Dynamic Link Library). After all the scenarios created, the power, imbalance and RMS voltage are measured in the loads that were installed at the photovoltaic generators and also in the secondary of the transformer. A statistical analysis is performed in order to determine the hosting capacity of the network, that establishes minimum parameters for the imbalance and RMS voltage level to maintain a good Power quality. And finally, since the hosting capacity is dynamic, and cannot be generalized, because at each point of the system has its characteristics and thus it is necessary to identify the HC separately for each busbar. However, it can be pre-established around 60% or 70% of photovoltaic penetration level, since power utilities can install battery banks or relocate consumers to other phases in order to mitigate impacts on the network.