Review on the research and practice of deep learning and reinforcement learning in smart grids

By Dongxiao Zhang, Xiaoqing Han, Chunyu Deng. CSEE Journal of Power and Energy Systems, 2018

480 citations recorded by the source corpus.

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/36089b937a5f29e5ca8df93508e5cb8b9f42b6a5

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Citations in the corpus

Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations (2020, background): “In [5], a detailed survey is provided summarizing the applications of reinforcement learning (RL) methods in power grid operation and control.”
A Review of Deep Reinforcement Learning for Smart Building Energy Management (2020, background): “[55] Cyber security, demand response, load forecasting, and microgrid Smart grid RL, DQN, DDPG, NAF, A3C No No”
Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios (2019, background): “Improvements in both algorithms and powerful computer hardware allowed AI to enter into a new developmental stage [3].”
Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, background): “The research and practice of deep learning and RL in smart grid were reviewed in [121].”
Adversarial Robustness with Semi-Infinite Constrained Learning (2021, background): “Learning is at the core of many modern information systems, with wide-ranging applications in clinical research [1–4], smart grids [5–7], and robotics [8–10].”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, unknown): “This combination has been used in game playing program “AlphaGo” developed by Google [105].”
A Review of Machine Learning Approaches to Power System Security and Stability (2020, methodology): “Examples of RL algorithms include Q-learning, DQN, SARSA, DDPG, etc. [48].”
Quo vadis artificial intelligence? (2022, background): “Second, colossal amounts of data are available as the fuels for model training [37, 38].”