Digital Twins为智能电网提供了有前途的解决方案，这些解决方案涉及能源资产的优化运营、管理和控制，以及安全可靠的能源分配。由于在电网边缘大规模采用分布式可再生资源，这些挑战比以往任何时候都更加紧迫。数字双胞胎利用物联网、大数据分析、机器学习和云计算等技术，分析来自不同能源传感器的数据，查看和验证物理能源资产的状态，并提取有用的信息来预测和优化资产性能。在本文中，我们将提供一个概述数字孪生应用领域的智能电网，同时分析现有的最先进的文献。我们专注于以下应用领域：能源资产建模，故障和安全诊断，运营优化和商业模型。所发现的大多数相关文献方法都是在最近三年内发表的，表明数字孪生在智能电网中的应用领域是热门的，并正在逐步发展。无论如何，对于数字孪生系统的实施以及与能源管理流程的集成还没有统一的看法，因此，仍然需要做很多工作来理解和自动化智能电网管理。
英文摘要：
The Digital Twins offer promising solutions for smart grid challenges related to the optimal operation, management, and control of energy assets, for safe and reliable distribution of energy. These challenges are more pressing nowadays than ever due to the large-scale adoption of distributed renewable resources at the edge of the grid. The digital twins are leveraging technologies such as the Internet of Things, big data analytics, machine learning, and cloud computing, to analyze data from different energy sensors, view and verify the status of physical energy assets and extract useful information to predict and optimize the assets performance. In this paper, we will provide an overview of the Digital Twins application domains in the smart grid while analyzing existing the state of the art literature. We have focused on the following application domains: energy asset modeling, fault and security diagnosis, operational optimization, and business models. Most of the relevant literature approaches found are published in the last three years showing that the domain of Digital Twins application in smart grid is hot and gradually developing. Anyway, there is no unified view on the Digital Twins implementation and integration with energy management processes, thus, much work still needs to be done to understand and automatize the smart grid management.
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
Department of Mechanical Engineering, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校机械工程系）；Computer Science & Information Systems Department, Bradley University（布雷纳德大学计算机科学与信息系统系）
Large Language Models for Explainable Decisions in Dynamic Digital Twins
Southern University of Science and Technology（南方科技大学）；University of Birmingham（伯明翰大学）；University of Thessaly（塞萨利大学）；Research Institute of Trustworthy Autonomous Systems (RITAS)（可信自主系统研究院（RITAS））
Heating in human-HVAC interaction for smart homes: An interdisciplinary overview
University of Siegen（锡根大学）；Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所（FIT））；Eindhoven University of Technology（埃因霍温理工大学）；University of Strathclyde（思克莱德大学）；University of Toronto（多伦多大学）
