## 1 Introduction, Positioning
[16] draws attention to the fact that conventional loads are very different from demand-response-enabled loads whose profiles are sensitive to energy price. [PERSON] et al. [16] formulate an optimization model to represent the self-adjusting actions of these special loads in distribution systems to help SE. Most research uses historical smart meter data to make pseudo measurements [9; 17; 18]. In Refs. [19; 20], it is shown that in the process of finding the optimal place for smart metering devices, the examination of spatial and temporal dependencies of pseudo data generation can be useful. In addition to impedance parameters, node voltages and branch currents, as well as the knowledge of topology, also play an important role in DSSE. Among other things, [PERSON] [5] reviews the impacts of topology on DSSE and draws attention to the importance of knowing the proper status of switches and circuit breakers. To handle the problem of the knowledge of topology, energy management systems have been used in transmission networks for a long time [21], but in LV DSSE, this can be taken as a new approach [22]. The details of the typical network topology of distribution systems are discussed in detail in Ref. [7]. Commonly, the R/X ratio is high in LV distribution systems, the level of automation is low, the structure is radial, and there are only few measurements available. Thus, observability is one of the biggest challenges of DSSE. A wide collection of pilot projects of distribution systems can be found in Ref. [7]. It is also important to consider what kind of elements are modeled in these projects. Focusing on LV networks, the first paper to be mentioned is Ref. [23]. Here, the LV network is part of a larger distribution network, and the MV/LV transformer is modeled. This network consists of radial feeders, and some of them supply a greater number of consumers. [PERSON] et al. [24] validate the ADMS4 LV solution in a laboratory before the real network demonstration, with a case study of a typical Portuguese LV system. This is a four-wire multi-grounded network with consumers that have randomly generated power factors. Another well-documented project is the German SmartSCADA [25; 26; 27] test project, which was funded by the German Federal Ministry for Economic Affairs and Energy. In this project, a semi-urban LV grid was modeled with PV systems and smart meters. Thus, transformers, lines, buses, consumers and PV systems have typically been modeled in most of the papers. This paper presents a WLS-based DSSE algorithm developed by the MTA-BME Lendulet FASTER Research Group, using the future pilot sites of four Hungarian LV supply areas. The DSO, E.ON North Transdanubia Electricity Network Ltd. (E.ON) provided single-phase models from GIS and INIS data. The modeling was performed in Python, using the pandapower package. The load profiles, measurement data and pseudo-measurement data were generated using AACs and SLPs by MATLAB. All four networks are LV supply areas with radial topology and a commonly high R/X ratio. The main contribution of our work is twofold. First, the methodology is developed for modeling real LV networks with emphasis on the difficulties of finding information on network topology, on automatically building the network models starting from files of DSO information systems and on managing lines characterized by uncertain or inconsistent lengths. Second, the DSSE algorithm was implemented to perform numerical experiments to demonstrate the applicability of such network models even in the case of low meter penetration. The rest of the paper is organized as follows. Section 2 introduces the methods and data used for the work. Results are presented and discussed in Section 3, and finally, conclusions are drawn in Section 4.