## 3 Major Applications of the Smart Grid Served by 6G
### Time-Critical Operations  
The real-time operations of smart grid systems, such as fault detection, outage management, island detection and management, energy management, and load balancing, can be supported by 6G. The network slicing based on non-real-time, close-to-real-time, and real-time operations is an effective solution for smart grid systems [118]. The time-critical applications of smart grid systems require area traffic capacity, spectral efficiency, latency, user data rate, and reliability. The novel 6G-enabled D2D/cooperative NOMA-OAM-MIMO, along with IRS, BF, THz, AI-based edge computing, and the real-time smart grid operational algorithms (e.g., EDGE/PMU-based voltage stability/phase-angle based island detection/learning-to-infer), can fulfill the KPI requirements and can also perform the time-critical operations of smart grids [119; 120; 121]. Furthermore, wireless power transfer (SWIPT/NOMA-SWIPT) can energize edge/cloud devices for time-critical operational purposes, which is discussed in the earlier subsection of this paper. Moreover, the emBB and mMTC are suitable for non-real-time applications, and mMTC is appropriate for semi-real-time applications and ultra-reliable-low-latency-communication (URLLC). Both are appropriate for time-critical (real-time) smart grid operations [118]. The time-sensitive communication service-based AI-enabled 6G network slicing approach is a possible solution for accomplishing the time-critical smart grid operations [118].  
For the time-critical smart grid operations, there are several novel algorithms that have been proposed for time-critical energy management, island detection, and outage detection and recovery. The energy management of the smart grid system regarding the demand response is a time-critical issue that can be overcome by a hybrid EDGE algorithm [119]. Edge is evolved by combining a genetic algorithm (GA) along with an enhanced differential evolution (EDE) algorithm [119]. The phase angle-based island detection method is much more resilient, more secure, computationally simple, and takes less time to detect the island of a smart grid system [123]. The PMU-based voltage instability indexed method was introduced in [120]. The proposed index can also detect the outage of lines/gens in an unsupervised manner [120]. The AI-enabled network slicing and intelligent cloud/edge computing can also overcome the computational issue within the provided time constraint for the proposed technique. The real-time detection of line outages is an immensely challenging task, particularly in scenarios where unknown line outages rapidly accumulate, thus leading to large-scale blackouts [121]. Hence, a pioneering learning-to-infer approach has been devised to detect the time-critical multi-line outages in a smart grid [122]. Additionally, the integration of AI-edge computing and 6G wireless communication techniques can effectively tackle the computational complexity involved in the process. Moreover, 6G-based novel communication techniques, which are mentioned above along with AI-enabled network slicing and intelligent edge/cloud computing, can process the algorithms for computational offloading, as well as effectively perform the time-critical operations of smart grids.  
Based on the above discussion, Table 4 summarizes the major applications and respective suitable techniques for 6G-based smart grid communication networks.