The article is focused on the “demonstration” activities carried out by the University of Genoa at Savona Campus facilities in order to implement the “Living Lab Smart City”. The idea is to transform the Savona Campus in a Living Lab of the City of the Future: smart technologies in Information and Communication Technology (ICT) and energy sectors were installed in order to show a real application of the Smart City concept to population and external stakeholders. Moreover, special attention was given to the environment, personal wellbeing, and social equalities. The sustainable energy Research Infrastructures (RIs) of Savona Campus allowed enhancement of the applied research in degree programs and the collaboration with several companies. In particular, an important partnership with the Italian electric Distribution System Operator (DSO), ENEL S.p.A., started in 2017 to test the capability of these RIs to operate disconnected from the National Grid, relying only on the supply of renewables and storage systems. The “Living Lab Smart City” is an important action to reduce the carbon footprint of the Savona Campus and to increase the awareness of students, teachers and researchers towards Sustainable Development in Higher Education Institutes.

Assessment of dynamic stability in a modern power system (PS) is becoming a stringent requirement both in operational planning and in on-line operation, due to the increasingly complex dynamics of a PS. Further, growing uncertainties in forecast state and in the response to disturbances suggests the adoption of risk-based approaches in Dynamic Security Assessment (DSA). The present paper describes a probabilistic risk-based DSA, which provides instability risk indicators by combining an innovative probabilistic hazard/vulnerability analysis with the assessment of contingency impacts via time domain simulation. The tool implementing the method can be applied to both current and forecast PS states, the latter characterized in terms of renewable and load forecast uncertainties, providing valuable results for operation and operational planning contexts. Some results from a real PS model are discussed.

Model-based control techniques have been gaining more and more interest these days. These complex control systems are mostly based on theories, such as feedback linearization, model predictive control, adaptive and robust control. In this paper the latter approach is investigated, in particular, sliding mode (SM) control is analyzed. While several works on the description and application of SM control on single-input single-output systems can easily be found, its application on multi-input multi-output systems is not examined in depth at the same level. Hence, this work aims at formalizing some theoretical complements about the necessary conditions for the feasibility of the SM control for multi-input-multi-output systems. Furthermore, in order to obtain the desired performance from the control system, a method for parameter tuning is proposed in the particular case in which the relative degree of the controlled channels is equal to one. Finally, a simple control problem example is shown with the aim of stressing the benefits derived from the application of the theoretical complements described here.