In the first step, information of the currently active topology is retrieved from the energy management system (EMS) and network management system (NMS). The information from both sources is mapped onto each other in order to derive physical and cyber dependencies between both subsystems. This means in particular, with respect to physical dependencies it is derived which parts of the electrical system are delivering power to which parts of the ICT system. Regarding cyber dependencies it is derived which parts of the ICT system are involved to provide necessary data communication capabilities to the corresponding grid components. In the next step, the interdependency description is subject to expert analysis. In this step, in particular certain derived interdependencies might be removed as, e.g., independent power supplies (UPS) or redundant ICT components provide protection against physical or informational dependencies.
From that information an interdependency graph can be constructed containing for each component in one of the subsystems, from which components in the other subsystem it is dependent. An exemplary distribution system comprising of an ICT subsystem and an electrical subsystem is depicted in (Fig. 1). The electrical system consists of three transformer stations (T1 - T3), connected to a system of power lines, line switches (A1 - A3) and bus bars where power lines are interconnected. At the ICT side, the power system is equipped with several intelligent electronic devices (IEDs) denoted with M0 - M9, which are necessary for proper power grid control. In particular, IEDs are connected to the grid control systems by utilization of an ICT network comprising of four switches connected in a ring topology (S1 - S4). This network provides connection for the IEDs and the central control stations C1 and C2. Data transfer links are depicted as green lines, where solid lines represent active links and dashed lines represent inactive links. Finally, in the current grid configuration three supply regions (R1 - R3) are depicted, where R1 is supplied by transformer T3, region R2 is supplied by T2 and region R1 is supplied by transformer T1. The interdependency graph for component C2 (a control system) is shown in (Fig. 2). The graph shows for component C2 (a control system) which dependencies exist to other components in order to operate properly. The component denotations are taken from (Fig. 1). Dashed lines represent dependencies within the same subsystem, for instance, the dependency of C2 on proper functionality of Switch S3. Solid lines visualizing interdependencies into the other subsystem. In the given example, switch S3 has an interdependency on power supply provided by transformer T2. It gets apparent from the figure that all of the corresponding components are interdependent with the electrical system finally.
The resulting information about system structure and interdependencies is fed into a cascading failure simulation system. This provides a simulation model with the necessary data basis to derive how extracted physical, informational and geographical interdependencies affect the both subsystems. For incidents, appearing at one component of either subsystem it can be evaluated how they spread into the other subsystem (i. e. hit components in the other system). For both subsystems already (from literature) available simulation models for cascading failures might be applied. At this state, the simulation model comprises of static information valid for given configurations of the sub networks. However, for the robustness analysis important information on type and number of dependencies between the components or nodes of the subsystem can be derived. Within this model different configurations of the subsystems, resulting in different information flows and power flows in the subsystem can be evaluated in order to derive changes of interdependencies (in particular physical and informational) between the subsystems.
In the next steps, the model will be complemented with dynamic aspects of the smart distribution system. In addition to metrics such as node degrees (which can be derived from structural analysis) especially load flows and component capacities provide important measures for evaluating a nodes relevance. The corresponding robustness metric is developed as part of RQ2. By simulating certain load/consumption situations including load flows in the available topologies, the nodes’ relevance is evaluated and, thus, the robustness of the overall system can be derived.
By employing realistic load/consumption situations and corresponding system utilization including load flows (gathered, e. g., from historical data), the simulation can be used to train a machine-learning based evaluation system, which can derive system robustness using current subsystem configuration and current load/consumption situations without the need for computationally expensive and time consuming calculations.