In Italy, as in most parts of Europe, the measures to control the spread of the COVID-19 pandemic has had relevant impacts in several sectors, last but not the least the electricity one. The changes in day-to-day routine has affected the national energy demand: from the 11th of March to the 8th of May in Italy the electricity demand was dropped by -16%, with a decrease of about 7.7 TWh in comparison with the same period in 2019 (source TERNA, see figure).

The control and automation in EPES assets rely on precise and reliable time information.  Current solution is to use a local NTP server using as reference GPS signal receiver. The GPS (GNSS) signal can be spoofed easily now and the GPS receiver can be placed on the cyber-attack surface of the EPES assets.

With regards to the ongoing transformation of power grids into intelligent, automated systems – called smart grids – equipped with several assets for managing power flux and energy balance, such as measurement devices (Phasor Measurement Units, Smart Meters), actuators (circuit breakers, disconnectors) and network equipment (gateways, control nodes), the question of how to comprehensively perceive and understand complex, multi-staged attacks on smart grids is gaining increasingly more significance.

Artificial Intelligence (AI) is becoming vital for today’s increasingly complex utility business. Machine learning plays an integral part in the PHOENIX project, as well. In an earlier blog post, I discussed how digital technologies, such as AI, are essential to addressing the sustainability challenge[1]. Franck Freycenon recently published a blog exploring how AI can help energy and utility companies².

Following the advent of distributed generation, the electric grid underwent an impressive change in power flows. In fact, the grid had been designed and developed based on the fact that energy had a unidirectional power flow; electricity had to be produced in massive power plants from which, through the high voltage (HV) network, the energy had to be transmitted into the consumption areas, where it would reach the end user, through the medium voltage (MV) network and the low voltage (LV) network.

Energy networks are becoming ever more decentralized with an increasing number of renewable resources connected to them. Due to the volatile nature of this generation, distribution grids will need to ensure that controllable loads and renewable generation are synchronized, to avoid peaks and prevent over-loading. Achieving this will help enable renewable generation, ensure security of supply, and mitigate expensive grid upgrades.

­­In the energy domain, cybersecurity incidents information sharing [1] among the electrical power and energy systems (EPES) utilities is crucial for the successful mitigation of critical cybersecurity such events.

The electrical grid constitutes of legacy systems that were built with no security in mind. As we move towards the Industry 4.0 area though a high-degree of automation and connectivity provides: 1) fast and flexible configuration and updates as well as 2) easier maintenance and handling of misconfigurations and operational errors.

Nowadays, the demand for designing efficient, end-to-end Network Intrusion Detection Systems (NIDS) for Cyber Physical Systems (CPS) has grown tremendously.