necessitating a phase locked loop (PLL) for synchronizing to the grid. In weak grid conditions, a grid following scheme, also referred to as vector control, cannot operate properly [6]. For this, _direct control_ mechanisms have been refined and extended towards grid-forming control [7]. This emerging control strategy can counteract changes in voltage and frequency, and can be extended/modified to provide standalone and black-start capability. To ensure grid code compliance of converter-based generators, a wide range of transient stability studies is needed in the planning and development phase of grid extensions [8]. For that purpose, the dynamic response of converter-based generators under different network conditions needs to be analyzed, which is traditionally done by time-domain simulation. These type of simulations are commonly dedicated to and numerically optimised for electromagnetic transient analysis or transient stability, which limits the system size and type of the applications. The incorporation of sophisticated power electronic converters and DC transmission into such tools is challenging, and commonly leads to situation-specific workarounds, such as multi-rate simulations or drastic model simplifications of the considered subsystems, often causing the results to be inaccurate or overestimated [9; 10; 11]. In general, traditional monolithic (single solver, single domain) simulation tools face difficulties in handling the increasing complexity (heterogeneity) of cyber-physical energy systems [10; 12; 13]. A number of solutions exist, among which (1) accepting that the monolithic, simplified simulation takes longer and might be less accurate, (2) real-time simulation of power systems with the possibility to insert hardware-in-the-loop [14], and (3) co-simulations. The latter is the focus of this paper. The co-simulation is a composition of a set of coupled simulators, each modelling a subsystem of the overall system under test, working together in order to assess the system response [10; 15]. This way, the power system can be modeled in a specialised simulator while power electronic converters and/or their controllers can be modeled with the appropriate level of detail in another simulator. By coupling these independent simulators by a co-simulation framework, the whole system modeling effort will decrease [16; 17]. There is a large area for the application of co-simulation from grid monitoring to ICT system security [18; 19; 20; 21; 22; 23]. An extensive survey and statistical analysis using smart grid co-simulation application is given in [24; 25; 26]. There exists a reasonable level of standardisation for co-simulations in terms of interfacing between the subsystems, the synchronisation, and overall simulation coordination. The functional mock-up interface (FMI) is the de facto low-level standard for co-simulator interfacing [27; 28; 29]. FMI components are called functional mock-up units (FMUs). Each simulator or model can be represented as an FMU and, by doing so, the intellectual property is protected as models can be shared without sharing internal information (grey or black box encapsulation) [30; 31; 32; 33; 34; 35]. Simulation synchronisation and coordination, especially in a distributed fashion, is standardised under the high level architecture (HLA) and related concepts such as HELICS [36]. Moreover, a number of co-simulation platforms exist that offer a top-down (server-client) coordination of co-simulation and offer simulation coupling towards proof-of-concept [37]. mosaik is a tool dedicated to smart-grid scenario development and co-simulation assessment [38]. Originally being developed for multi-agent systems, it has been extended for synchronisation between time-domain simulations and FMI support [34; 39; 40]. The interfaces and synchronisation methods have been tested in a small-scale context in [34]. This paper will focus on more contemporary controls for power electronics interfaced RES and test their applicability to large-scale assessment in co-simulations.