Guest Editorial: Applications of Artificial Intelligence in Modern Power Systems: Challenges and Solutions

• Main Authors: Weihao Hu, Di Shi, Theo Borst
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: Journal of Modern Power Systems and Clean Energy
• Online Access: https://ieeexplore.ieee.org/document/9282273/

With the increasing integration of renewable energies, power electronic devices and flexible loads, modern power systems are becoming more sophisticated and facing higher uncertainty. Traditional model-based methods cannot fully satisfy the analysis and control requirements of modern power systems duo to its complexity and uncertainty. At the same time, with the deployment of smart meters and advanced sensors, an unprecedented amount of data is generated by the power systems all the time. The generated data have great value and can make up for the deficiency of the traditional physical model based approaches. Driven by data, artificial intelligence can directly learn from data, and needs no simplifications and/or assumptions of the physical model. Great success has been achieved in the fields of artificial intelligence in recent years, bringing new opportunities of applying the state-of-the-art machine learning technologies to power systems. This special section focusses on some of the emerging technologies to solve existing challenges and solutions for the application of artificial intelligence in modern power systems. Thirteen articles included in this special section are summarized as follows: In the paper entitled “Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review”, the authors make a comprehensive review of applications of reinforcement learning in modern power and energy system. The basic ideas and various types of methods of reinforcement learning, deep reinforcement learning and multiagent deep reinforcement learning algorithms are first illustrated, respectively. Then their applications for the optimization of smart power and energy distribution grid, demand side management, electricity market and operational control are discussed in detailed. Finally, the challenges and prospects of reinforcement learning in modern power and energy system are presented.