Enabling remote-control for the power sub-stations over LTE-A networks

In recent years, smart grid (SG) applications have been proven a sophisticated technology of immense aptitude, comfort and efficiency not only for the power generation sectors but also for other industrial purposes. The term SG is used to describe a set of systems customized to rapidly and automatically monitor user demand, restore power, isolate faults and maintain stability for more efficient transmission, generation and delivery of electric power. Nevertheless, the quality of service (QoS) guarantee is essential to maintain the networking technology used in different stages and communication of the SG for efficient distribution, which may be drastically obstructed as the sensors of the application increases. Undoubtedly, receiving and transmitting of this information requires two-way, high speed, reliable and secure communication infrastructure. In this paper, we have proposed a scheduling approach guarantees the efficient utilization of existing network resources that satisfy the sensors’ demands sufficiently. The proposed approach is based on hierarchical adaptive weighting method, which helps to overcome the issues of studied scheduling approach and intended to aid SG sensors applications, based on its QoS demands. We have employed four enabler SG applications for remote power control, namely demand response, advanced metering infrastructure, video surveillance and wide area situational awareness applications for the implementation of the remote-power substation controlling. Moreover, the cooperative game theory technique has been incorporated into a solution for the optimal estimation and allocation of bandwidth among different sensors. The results have been evaluated in terms of throughput, fairness index and spectral efficiency and results have been compared with the well-known scheduling approaches such as exponential/proportional fairness (EXP/PF), best channel quality indicator (Best-CQI) and exponential rules (EXP-Rule). The results demonstrated that the proposed approach is providing a better performance in terms fairness index by 25, 66 and 68% compared to EXP/PF, EXP/RULE and Best-CQI, respectively