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The traditional electrical grid is currently undergoing a range of modernization efforts and becoming a smarter grid . In the traditional electrical grid, energy is distributed from the generation plants to the consumers via large nationwide transmission and distribution networks. Information monitoring and management in these traditional electrical networks is typically limited to the distribution networks that distribute electrical power within a city to the individual consumers. Due to rising demands, aging infrastructure, reliability concerns, and the emergence of renewable energy sources, the smart grid (SG) concept is being introduced . Typically, there are three architectural building blocks of the smart grid. First, Home Area Networks (HANs), which connect the devices within the consumer premises, such as smart meters, distributed renewable energy sources, and Plug-in Electric Vehicles. Second Neighborhood Area Networks (NANs), which interconnect multiple HANs, and communicate the collected information to Wide Area Networks (WANs). Third, WANs, which serve as communication backbone.
There are several applications of cognitive radio in the context of the smart grid. One important application is smart home management and residential load management. This application will not only help to reduce the power consumption at the consumer’s end, but also help to optimize the scheduling and usage of electrical equipment for customer satisfaction. To deal with these aspects, we include two articles. The article entitled “Application of hierarchical and distributed cognitive architecture management for the smart grid” by Jacques Palicot, Christophe Moy, Benoit Résimont, and Rémi Bonnefoi proposes a hierarchical and distributed cognitive architecture management for the smart grid. To illustrate the efficiency and benefits of proposed architecture, Palicot et al. used it for smart home management. The proposed architecture was shown to help reduce the power consumption in the smart home context. The article entitled “Iterative learning for optimal residential load scheduling in smart grid” by Bo Chai, Zaiyue Yang, Kunlun Gao, and Ting Zhao proposes a residential load scheduling scheme. Through convex optimization, Chai et al. optimize the power consumption expenses, customer satisfaction, and robustness of schedule subject to uncertain electricity price, in the context of residential loa