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.

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随着可再生能源，电力电子设备和柔性负载的日益集成，现代电力系统变得越来越复杂，面临着更大的不确定性。传统的基于模型的方法由于其复杂性和不确定性而不能完全满足现代电力系统的分析和控制要求。同时，随着智能电表和高级传感器的部署，电力系统始终会产生前所未有的数据量。生成的数据具有重要的价值，可以弥补传统的基于物理模型的方法的不足。在数据的驱动下，人工智能可以直接从数据中学习，而无需对物理模型进行简化和/或假设。近年来，在人工智能领域取得了巨大的成功，带来将最新的机器学习技术应用于电力系统的新机会。本节专门介绍一些新兴技术，以解决人工智能在现代电力系统中的应用所面临的挑战和解决方案。该特殊部分中包含的十三篇文章摘要如下：在题为“强化学习及其在现代电力和能源系统中的应用：综述”的论文中，作者对强化学习在现代电力和能源系统中的应用进行了全面综述。 。首先分别说明了强化学习，深度强化学习和多主体深度强化学习算法的基本思想和各种类型的方法。然后详细讨论了它们在优化智能电力和能源分配网格，需求侧管理，电力市场和运营控制中的应用。最后，介绍了现代电力和能源系统中强化学习的挑战和前景。