Enhanced power grid evaluation through efficient stochastic model-based analysis

Masetti G.

Smart Grid  model-based analysis  Stochastic Activity Networks  Dependability 

The electrical infrastructure can be considered nowadays as a meta-critical infrastructure: in fact it is the basis for almost all the critical infrastructures a modern nation can have, such as water, oil, gas and transportation. This implies that its correct operation is a fundamental requirement to the correct operation of the critical infrastructures that depend on it. To allow pervasive control and monitoring towards resilience and performance enhancements, the Smart Grid is emerging as a convergence of information and commu- nication technology with power system engineering. In particular, the ever increasing level of distributed energy resources penetration calls for more and more sophisticated monitoring and control facilities. So, studying the influence of distributed energy resources, of new control policies and ICT on the dependability of distribution grids offers valuable insights on how to improve the design of Smart Grids. In addition to standard dependability measures such as reliability and availability, among greatly relevant measures specifically defined for electrical distribution systems there are the voltage quality and the energy required, but not supplied, by the distribution system. A popular approach to assess electrical distribution specific measures, in presence of failures or attacks to the ICT system and/or to the electric in- frastructure, is the stochastic model-based analysis. Although several studies have been already proposed, the research in this context still faces a number of challenges, mainly due to the need: i) to consider both the ICT sub- system and the controlled electrical infrastructure, to properly account for (inter)dependencies through which operations (and failures/attacks) propa- gate; ii) to model and analyze the SG components at a sufficiently detailed level of abstraction, targeting realistic representation of their structure and behavior in view of promoting accuracy of the assessment itself. Both nomi- nal and a variety of faulty behaviors are to be investigated, since the interest is on assessing resilience and quality related attributes; iii) to tackle realistic segments of SG in terms of topology size, to make the evaluation study of real interest to stakeholders involved in the field. To cope with all these needs results in huge and complex models, to be typically defined in a modular fashion and requiring sophisticated composi- tional operators. Moreover, model solution through simulation-based eval- uation becomes unavoidable in presence of non-Markovian behavior of the involved components, thus preventing the use of analytical approaches. Given the above premises, the stochastic model-based analysis of realistic SG topologies is a research area where further investigations and enhance- ments are highly desirable. In this context, this thesis offers contributions in the direction of promoting efficient evaluation of SG in realistic scenarios from a resilience perspective.