Resilience of the Italian electricity grid: objectives and future challenges

The activity conducted in the period from 15/03/2022 to 15/03/2024 was carried out in collaboration between the University of Cassino and Lazio Meridionale, Tesmec Energy Automation and Terna S.p.A. In particular, studies were conducted on the resilience of the Italian electricity grid of the future, in terms of objectives and challenges, both for operational resilience (short-term) and planning resilience (long-term). In order to be able to combat climate change, the national electricity grid, and grids around the world, are undergoing a major change due to the large presence of renewable resources and new electrical loads such as charging stations for electric vehicles, heat pumps, etc. The combination of these new resources and electrical loads is called DG. This evolution has led to the definition of the distribution network as a smart grid. The smart grid is a combination of computer networks and electricity distribution networks. It is called 'smart' because it optimises the distribution of electricity, decentralises power plants and minimises overloads and voltage fluctuations. Thus, we move from a system of large power plants to a green economy and renewable energy sources. One of the fundamental pillars of smart grids is electricity grid resilience. Electricity grid resilience, as defined by CIGRE WG C4.47, refers to the ability of an electrical system to limit the magnitude, severity and duration of its degradation. Specifically, resilience is the ability of a system to absorb and withstand stresses that exceed the resilience limits of the system itself and to return to its normal operating state, quickly and efficiently, possibly through temporary interventions, including the preservation, restoration or improvement of essential system structures and functions. In order to be able to increase network resilience, it is important to gather adequate information from the field that reflects the true state of the electricity grid. The information gathered from the field, however, can often be affected by measurement errors, which can mislead the end user. For this reason, in the initial phase of this study, measurement problems in the MV/LV cabin were addressed. This measurement is carried out using Rogowski coils, very ductile, inexpensive and easy-to-use current sensors, which, however, do not have a high measurement resolution and their measurements can often be affected by errors related to their installation or by adjacent magnetic fields that interfere with the main magnetic field to be measured. In particular, the Rogowski coil prototypes, owned by Tesmec, were tested through the use of a measuring instrument that is the T-PMR, also owned by Tesmec and found in most MV/LV transformer substations. The increasing presence of DG within the electricity grid means that voltage quality, understood as the maintenance of technical parameters characterising voltages within defined limits, may suffer. In this case too, as seen for current measurements, it is crucial to have a measurement that is as accurate as possible. For this reason, between 01/10/2022 and 15/02/2023, studies were carried out at the E-ON Research Centre of the RWTH in Aachen, Germany, on the impact of the measurement error of the phasor units on the distributed control of online voltage. One of the possible future developments of this research, therefore, is to increase the number of measurements used in the network, also using those of the T-PMR, a device already present in the cabin and which would save on the number of PMUs installed in the network. Resilience is evaluated not only on the transmission and distribution grid, but also on the user side. For example, an industrial plant aims to have an automated network of production devices that is as resilient as possible. For this reason, predictive maintenance and load disaggregation techniques have been investigated during the period from 15/03/2021 to 01/10/2022, which can increase the resilience of a household or industrial plant and also reduce the costs associated with the installation of smart meters. In particular, load disaggregation by means of non-intrusive load monitoring (NILM) allows artificial intelligence algorithms to derive the electrical behaviour of a load from the aggregated data of all the electrical loads in a household, from which it is possible to make diagnoses on the health of these loads. To this end, the accuracy of an optimal combinatorial disaggregation technique was studied, which aims to understand how the algorithm behaves as the type of electrical characteristic analysed, the number of loads to be disaggregated and the accuracy of the meters vary. However, given the scarcity of household electrical datasets in the literature that take into account different types of electrical characteristics, a simulated dataset of electrical loads was created from real electrical profiles. Specifically, it consists of 36 different electrical loads and a complete electrical load signature, which can be used for various applications in the field of smart energy to test and improve artificial intelligence algorithms useful for predictive diagnostics. Climate change is increasingly becoming a threat to the world, with serious consequences that can also be caused to the electricity transmission system, a key infrastructure for modern society. The main objective of TSOs is to make the grid increasingly resilient to such phenomena. The main critical weather threats include wet snow, strong winds and hydrogeological phenomena, i.e. floods and landslides due to high levels of precipitation. For this reason, during the period between 01/03/2023 and 15/03/2024, the research carried out focused on issues related to grid resilience in the face of climate change, thanks to the work done with Terna S.p.A.