The electrification of end-energy use and the increasing integration of distributed energy resources (DERs) are significantly reshaping the landscape of low voltage (LV) distribution grids. However, many LV networks were originally designed without considering these transformative factors, potentially leading to congestion and overloads. Assessing the hosting capacity of these networks has become crucial, as it quantifies the ability of the distribution network to accommodate additional DERs while maintaining stable and reliable operations. In this context, we introduce the concept of remaining hosting capacity as a metric to evaluate LV distribution networks' capacity to absorb additional DERs, considering the existing DER deployment. We present two simulation methodologies: Gaussian mixture model-based stochastic power flow simulations that deliver a detailed network analysis, including specific current and voltage data but require substantial computational resources, and a data resampling simulation methodology that employs detailed load and DER profiles to rapidly approximate load demands at the transformer level. Furthermore, we conduct a sensitivity analysis for different levels of DER penetration to calculate the networks' capability to accommodate more DERs. The results obtained illustrate the effectiveness of GM models and the data resampling simulation methodology proposed in this work. The remaining hosting capacity concept provides essential insights into the networks' capabilities to accommodate additional DERs in the future, facilitating informed decisions for both Distribution System Operators (DSOs) and DER developers regarding grid operation, necessary upgrades, and sustainable DER expansion.

Transition from fossil fuels to sustainable sources of energy like wind and solar is the need of the hour. All over the globe, plans are in motion to achieve this goal. This implies addition of new elements to the grid in the form of Distributed Energy Resources (DERs). These affect the working of distribution grids and to ensure reliable as well as safe operation, it is important to keep a track on the grid’s state regularly which is essential to a Distribution System Operator (DSO). For this very reason, Distribution System State Estimator (DSSE) has been introduced and has been a prominent topic of interest. Because of lack in observability of the network owing to unavailability of measurements and the stochastic load profiles of the distribution network, DSSE poses its own challenges. Therefore, it is necessary to validate the working of a suitable DSSE that is affected by the continuous changes in the grid. By selecting a suitable algorithm, this paper attempts to solve the observability issue by introduction of pseudo-measurements. The work in this paper comprises of sensitivity analysis of Weighted Least Squares (WLS) algorithm tested on two medium voltage networks in the Netherlands with the help of modelling from Gaia software for an Enexis network and a part of Stedin’s distribution network with limited measuring devices data available. Different types of inputs are taken to test the working of the algorithm in case of Stedin’s network and in case of Enexis network, the peak load moment of the day is tested with the available month data comparing it with the mean value. The results obtained illustrate the effectiveness of the selected algorithm for DSSE and are important for the DSOs to make critical decisions when needed for grid operation.