A Cloud Fog Intelligent Approach Based on Modified Algorithm in Application of Reinforced Smart Microgrid Management

Abstract Smart Microgrid (SMG) has been proposed to increase the stability, efficiency, reliability, and cost-effectiveness of power services. Along with these advantages, there exists the main security challenge in SMG operation which is due to the wide use of smart equipment, e.g., smart metering devices. Also, there is a significant challenge in the processing of the huge amount of data retrieved from these metering types of equipment. To overcome these challenges, in this paper, a new cloud-fog computing (CFC) framework has been developed for SMG energy management and operation to provide the necessary functions for the real-time operation of the SMG. Indeed, the proposed framework can take advantage of the cloud computing technique, e.g., cost-effectiveness, energy efficiency, flexibility, high computational and convergence speed, and scalability. Moreover, in this paper, various load balancing techniques have been applied to increase the efficiency of cloud computing. Also, a newly evolutionary algorithm, known as the Grey Wolf optimization algorithm (GWOA) has been utilized to balance the load among SMG, user demands, and service providers. Finally, the proposed GWOA has been compared with well-known techniques to show the effectiveness and excellence of the proposed framework and technique.