Hourly characterization of the integration of DER in a network from deterministic and probabilistic approaches using Co-simulation PowerFactory-Python

Abstract

Distributed energy resources (DER) can have significant effects on distribution networks. Their growing integration makes it necessary to conduct characterisation studies on the effects of several operating scenarios. Such studies apply deterministic or probabilistic techniques to the modelling and simulation of networks and DER. Nevertheless, the lack of studies that integrate three DER within the same research to quantify the potential effects that each one singly and all jointly could have on the networks is perceptible. In addition, the analysis of the most significant number of parameters studied for correctly interpreting the results and using inferential statistics can be helpful. Moreover, we examined various modelling and simulation approaches to evaluate the differences and similarities among multiple scenarios. Accordingly, this paper presents and details deterministic and probabilistic analysis approaches to characterise the hourly impact of photovoltaic systems, storage units, and electric vehicle charging stations in a 13-node IEEE test feeder. This elucidates DER integration in networks and could guide future studies. Estimating the individual or collective effects of a DER consists of quantification and hourly comparison of RMS values of voltages and currents, voltage unbalance, chargeability of transformers, and power losses. We collected the data for analysis using a co-simulation PowerFactory-Python for eight operating scenarios. The hourly stochastic comparison between scenarios consisted of calculating the difference in sampling means and applying hypothesis testing of inferential statistics. This comparative analysis verifies that the applied approach (deterministic or probabilistic), specific hour of analysis and simultaneous number of DER in a simulated scenario influence the results.