Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

Abstract

Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. This model gives priority to consumer preferences while planning the operation of appliances. A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes. Furthermore, the allotment mechanism has been designed to manage the grid appliances through the edge node. The proposed model helps to improve the latency in the grid appliances scheduling process.