As conventional power generation units are being replaced with distributed energy resources, operational practices such as voltage regulation and congestion management are expected to be challenging. To address these challenges, regular and automated inter-control centre operational coordination will be needed between Transmissions and Distributions System Operators (TSO and DSO). In this study, the data exchange required for the near-real-time operational coordination is investigated for a reactive power management use case. A realistic ICCP/TASE 2 protocol is implemented in laboratory environment where the physical network is simulated in a real-time simulator while optimal set-points are communicated from the control centers to simulated assets being communicated through the IEC 60870-5-104. In addition, the sufficiency of the Common Information Model (CIM) and Common Grid Model Exchange Standard (CGMES) is evaluated for TSO-DSO network data exchange. The results from the cyber-physical test setup show that the detailed DSO grid model knowledge by the TSO results in lower system losses. Such data can be difficult to prepare, exchange and compute. However, results show that simplified equivalent models can be acceptable if they are properly tailored to the specific use case. The experiences from the CIM implementation with the CGMES profile are found out to be sufficient for such operational data exchange.

Optimization tools for long-term grid planning considering flexibility resources require aggregated flexibility models that are not too computationally demanding or complex. Still, they should capture the operational benefits of flexibility sufficiently accurately for planning purposes. This paper investigates the sufficiency of an aggregated flexibility model for planning tools by comparing it against a detailed flexibility model. Two different constraint formulations, namely based on recovery period and temporal proximity were tested to account for post activation dynamics of flexibility resources. The results show that the recovery period based formulation results in excessive demand reduction. The proximity constraint formulation on the other hand results in realistic activation of flexibility resources which represents an improvement over the base formulation without constraints for post activation dynamics.