The accuracy of modelling distribution networks plays a very important role in analysing the stability of transmission systems. In recent years, due to the surge in the integration of renewable energy resources via power electronic interfaces, distribution networks are evolving from passive networks to modern active networks. This means that their characteristics are time varying following the operating condition of renewable energy sources. Real Time Digital Simulator (RTDS) is a specially designed hardware and software integrated computer system used to study Electromagnetic Transient (EMT) Phenomena in power systems. As the name implies, it can perform power system simulations at computational speeds equal to real-time operation. However, modelling of detailed distribution networks in RTDS would require many hardware resources. The goal of this thesis is to develop an equivalent dynamic load model for the stability analysis of transmission networks in RTDS, which should not only enhance RTDS’s capability of simulating large power systems, but more importantly, improve the accuracy of model distribution networks. Most studies about transmission systems do not require a full representation of distribution networks. However, the dynamic behaviour of distribution networks still needs to be preserved. Therefore, the dynamic equivalent parameters of distribution networks must be sufficient to ensure an accurate representation for the analysis of transmission networks. In this thesis, the identification and optimization of parameters are done using Mean Variance Mapping Optimisation (MVMO), a unique heuristic optimisation technique. The IEEE 34-Bus distribution system is used as a reference model for data collection and is developed in RSCAD, the RTDS software. An external grid is used to represent the transmission system wherein, several disturbances are simulated in order to compare the responses of the reference detailed model and the dynamic equivalent model. Furthermore, the validity of the developed DE load model is confirmed by comparing its behaviour to disturbances that were not
implemented during the parameter optimization process.