Preventing Cascadings Failures in Power Grid Power grids around the world are decentralizing at a rapid pace as renewable energy production vastly reshapes where electricity is produced. This necessitates a massive systems overhaul in how our power grids are designed, maintained, and protected. Instead of centralized grid management, more software and hardware developments are occurring at the grid edge, as defined by Wood Mackenzie as "an umbrella term to cover all the distributed hardware, software and business innovations that exist in proximity to the end user, rather than at the centre of a traditional generation network." Industry experts agree that moving grid intelligence away from central hubs and toward the grid edge is crucial to adapt to the undeniable shift from a centralized energy system to a next-generation distributed energy system, as highlighted by Greentech Media. The increasing demands on our power grid, driven by electric vehicles and data centers, coupled with two-way energy flows, are creating significant vulnerabilities in our aging grid system. Historically, electricity flowed from utilities to consumers on demand. Now, residential solar panels have transformed many consumers into producer-consumers, resulting in bidirectional energy flows to the grid. Renewable sources, such as solar and wind, generate power when the sun shines and the wind blows, not in response to consumer demand. These factors contribute to substantial vulnerabilities in our aging grid. While artificial intelligence (AI) presents opportunities for power grids due to increasing energy demand, it can also mitigate some of these pressures, particularly when deployed at the grid edge, where split-second sensor detection and decision-making are paramount. As Power reports, "Milliseconds matter" regarding grid-edge computing. The speed of decision-making at the grid edge is critical for maintaining stability, preventing cascading failures, optimizing efficiency, and integrating intermittent renewable resources. The proliferation of distributed energy resources (DERs), electric vehicles, and smart loads has transformed grid edge intelligence from a luxury to a necessity. This is leading to a revolution in real-time computing powers placed at the grid edge that can detect and respond to grid anomalies and emergencies like outages and power surges, addressing them before the utility’s central Supervisory Control and Data Acquisition (SCA) system even registers the event, protecting the broader grid from cascading failures. The United States Department of Energy (DoE) recognizes intelligent computing as a critical tool for managing smart grids capable of handling large inflows and outflows of variable energies like wind and solar. The DoE states that "machine learning could help electric utilities improve permitting and siting, reliability, resilience and grid planning." However, the government also acknowledges the inherent risks associated with this nascent and disruptive system. As Power notes, "the transition from centralized to distributed intelligence represents a fundamental change in thinking" and an essential shift in the design of massive, entrenched systems.

Our electric infrastructure is aging and is being pushed to do more than it was originally designed to do. Modernizing the grid to make it ‘smarter’ and more resilient through the use of cutting-edge technologies, equipment, and controls that communicate and work together to deliver electricity more reliably and efficiently can greatly reduce the frequency and duration of power outages, reduce storm impacts, and restore service faster when outages occur. These cutting-edge technologies will have to be developed at the edge, where split-second decisions need to happen most. By Haley Zaremba for Oilprice.com