Cloud-Edge-End Intelligence for Fault-Tolerant Renewable Energy Accommodation in Smart Grid
Xueqing Yang, Xin Guan, Ning Wang, Yongnan Liu, WU Hua-yang, Yan Zhang
Abstract
Smart grid integrates the distributed energy resources such as renewable energy with massive information to facilitate the flow of energy in the industries. The renewable energy accommodation is one of the key issues to achieve the energy efficiency in smart grid, which is difficult to obtain dynamic optimal policies due to the intermittency of renewables. To capture statuses of renewable energy for decision-making, large amounts of information in heterogeneous forms are collected by massive end devices deployed in smart grid. Such information not only provides fruitful features for existing learning based algorithms but also incurs high computation complexity. Besides, such heterogeneous data may also contain missing values, which may result in wrong policies by existing algorithms. In this article, a novel cloud-edge-end orchestrated computing scheme is proposed to efficiently repair missing values and obtain optimal policies in two separate layers. In the first layer, deep learning based algorithms deployed can perceive the characteristics and repair the missing values. In the second layer, deep reinforcement learning based algorithms are employed to obtain optimal policies. Simulations on the real power grid dataset illustrate the effectiveness of proposed fault-tolerant renewable energy accommodation algorithm.