A novel QoS-enabled load scheduling algorithm based on reinforcement learning in software-defined energy internet

Recently, smart grid and Energy Internet (EI) are proposed to solve energy crisis and global warming, where improved communication mechanisms are important. Software-defined networking (SON) has been used in smart grid for real-time monitoring and communicating, which requires steady web environment with no packet loss and less time delay. With the explosion of network scales, the idea of multiple controllers has been proposed, where the problem of load scheduling needs to be solved. However, some traditional load scheduling algorithms have inferior robustness under the complicated environments in smart grid, and inferior time efficiency without pre-strategy, which are hard to meet the requirement of smart grid. Therefore, we present a novel controller mind (CM) framework to implement automatic management among multiple controllers. Specially, in order to solve the problem of complexity and pre-strategy in the system, we propose a novel Quality of Service (QoS) enabled load scheduling algorithm based on reinforcement learning in this paper. Simulation results show the effectiveness of our proposed scheme in the aspects of load variation and time efficiency. (C) 2018 Elsevier B.V. All rights reserved.