CLASSIFICATION AND AGGREGATION OF RISKS IN SMART GRIDS

Abstract

The article discusses approaches to the classification of information systems in the energy sector and the systemic aggregation of cyber risks in smart grids. The authors identify the main architectural groups - from distributed smart grids to centralized ICS/SCADA and integrated microgrids - and analyze the specifics of protective measures for each of them.

Further, risk assessment methodologies are described: static models (e.g., FMEA), multi-criteria MCDM approaches (AHP, TOPSIS), probabilistic methods (Bayesian networks, Monte Carlo), and resilience metrics with their advantages, disadvantages, and data requirements.

To model the systemic aggregation of risks, graph-based approaches, agent-based modeling, and the “failure propagation” scheme in the network are presented, which allows to assess the cumulative effect of cascading attacks.

In addition, a multi-criteria indicator for ranking countermeasures by “return per unit cost” and an extended indicator that takes into account the absolute and relative risk reduction within a given budget are proposed.

The conclusions emphasize the need to implement adaptive IAM solutions and the Zero Trust concept to minimize the human factor and increase the resilience of smart grids.