The resilience of transmission and distribution networks in the face of extreme events is increasingly becoming a priority for network operators, who are witnessing an increase in the frequency and severity of such events.

However, the present context shows significant fragmentation in approaches to resilience by TSOs and DSOs, despite the regulatory authority’s push for greater harmonization of resilience assessment methodologies. In fact, the lack of coordination of interventions between distribution and transmission can lead to sub-optimal solutions.

For this reason, this report first discusses some mutual support measures between DSOs and TSOs, identifying a promising interface between network operators in the capability curves equivalent in the active power/reactive power plane. In addition, we quantify the requirements of a distribution system in terms of storage and generation resources to enable intentional islanding. This is an active measure to improve the resiliency of distribution systems in the event of a loss of supply from the transmission system.

An important aspect of resilience is to identify solutions for minimizing system degradation in the face of multiple contingencies produced by extreme weather events, consistent with the CIGRE C4.47 definition of electricity system resilience. In this regard, the report presents a preliminary formulation for a control that minimizes the degradation of system performance at the level of operation scheduling, taking into account the forecast uncertainties that characterize the threats looming on the grid (intensity, trajectory, and travel speed) and the system operating conditions (load demand and renewable generation).

Several objective functions are used to describe the degradation condition to be minimized (considering either the maximum value, the CVAR index or the expected risk associated with the degradation, monetized in terms of resilience costs). The same formulation also allows the process of restoring failed components to be quantitatively taken into account.

The general formulation is applied on a simple test network: simulations demonstrate the effectiveness of the control in limiting degradation. In addition, sensitivity analyses conducted on the alpha parameter (which indicates the maximum allowable probability of system degradation in the face of extreme events) and recovery time indicate that the cost of resilience increases as the time to restore infrastructure increases and the probability associated with the CVAR indicator decreases.