# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 8 Future of Electrical Substations, Challenges, and Advancing Towards Autonomous Network Management  
These innovations enable end-users and industries to better manage production and consumption. However, the variability of renewable energy sources greatly challenges the DSO's ability to maintain the stability of the grid. Traditional methods, such as upgrading cable sections, cannot be good enough to address such complexities that arise in modern grids [119]. One of the major trends being followed is the shift toward an AI/ML-based autonomous network management approach to improve the grid operations [120, 121]. It also involves smart metering, substation virtualization, and higher data analytics on the data arriving from smart meters, PMUs, and GIS-network topology in order to develop predictive models for fault detection and real-time management. The beta version of ABB's \begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Protocol/Tool**} & \multicolumn{1}{c}{**Description**} \\ \hline GOOSE & Facilitates communication between protection and control devices in substations. \\ \hline SV (sampled values) & Transmits voltage and current readings within substations. \\ \hline PRP & \\ (Parallel Redundancy Protocol) & Ensures uninterrupted communication by providing network redundancy. \\ \hline TPM (Trusted Platform Module) & Secures the integrity of devices through hardware-based security. \\ \hline \end{tabular}
\end{table} 
Table 26: Protocols and tools in cybersecurity. ADAM AI/ML fault prediction module is a prime example. It deals with the problem of incipient fault detection and increasing situational awareness, all without the need for dedicated sensors or devices. This cloud-based tool offers high-precision fault predictions that enable proactive maintenance to be performed, thus reducing downtime to a near negligible value. Electrical substation development in technology is increasingly driving the development of electrical substations, including centralized and virtualized protection and control systems, such as the ADAM 2.0 fleet management software [122]. In the recent industry survey [123], involving over 3200 firms, it emerged that for 92%, reliability remains an issue for their operations, while 60% want to increase investments in maintenance over the next three years. This trend moves toward outcome-based agreements in maintenance, where monetary incentives are tied to the performance outcomes of improved uptimes and energy efficiency. The integrated AI/ML-based Fault Prediction module enhances situational awareness and proactive fault detection. This brought massive improvements to maintenance planning while reducing downtimes. The system uses existing infrastructure, with capabilities for current and potential transformers, which positions it as cost-effective. Indeed, it integrates well with current operations [124; 125]. Concentrating on reliability not only guards against potential losses, but also positively influences the business's reputation and its financial performance. As the industry shifts further in the direction of predictive and autonomous network management systems, solutions like ADAM 2.0 will prove to be essential instruments for optimizations in performance, operation cost reduction, and overall network stability development, along with future definitions for electrical substations. Ongoing activities based in the University of Belgrade include implementing a real-time simulation platform making use of HIL technology [126; 127] by Q1-2025. The initiative will simulate the entire transmission network of Serbia for the validation of Autonomous Grid Management concepts, and it will explore new technologies. There are utilities like Enedis and Hydro-Quebec that are embracing the advantages for a more resilient grid, moving from existing infrastructure management to adopting innovative technologies. Challenges in the transition to virtualized P&C systems include interoperability, strengthening cybersecurity, and managing huge data sets associated with predictive analytics.