In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid.

The green innovations in the energy sector are smart solutions to meet the excessive power requirements through renewable energy resources (RERs). These resources have forwarded the revolutionary relief in control of carbon dioxide gaseous emissions from traditional energy resources. The use of RERs in a heuristic manner is necessary to meet the demand side management in microgrids (MGs). The pricing scheme limitations hinder the profit maximization of MG and their customers. In addition, recent pricing schemes lack mechanistic underpinning. Therefore, a dynamic electricity pricing scheme through linear regression is designed for RERs to maximize the profit of load customers (changeable and unchangeable) in MG. The demand response optimization problem is solved through the particle swarm optimization (PSO) technique. The proposed dynamic electricity pricing scheme is evaluated under two different scenarios. The simulation results verified that the proposed dynamic electricity pricing scheme sustained the profit margins and comforts for changeable and unchangeable load customers as compared to fixed electricity pricing schemes in both scenarios. Hence, the proposed dynamic electricity pricing scheme can readily be used for real microgrids (MGs) to grasp the goal for cleaner energy production.

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Infrastructure sharing is a practical resource utilization strategy among mobile network operators (MNOs). The rapid growth of data flow and technology migration has increased the costs for both infrastructure deployment and management. Hence, to ensure effective resource utilization, researchers propose a joint venture towers construction for the mobile network in Tanzania. Cost reduction analysis for tower construction has been done through game theory. It has been observed that there is significant cost saving by the MNOs sharing the infrastructure. Based on game theory, it was found that 50% of the cost can be saved when the MNOs cooperate in tower construction, which in turn improves services to the users and increase the network rollout to the remote area. In general, network infrastructure sharing (NIS) benefits both operators and the public at large. The MNOs can have a significant financial saving by avoiding the costs of construction or upgrading the overlapping sites, consolidating the existing sites, and by reducing expenses related to rents, maintenance, and transmission.