The advancement of Artificial Intelligence (AI) has improved the automation of energy managements. In smart energy management or in a smart grid framework, all the devices and the distributed resources and renewable resources are embedded which leads to reduce cost. A smart energy management system, Transactive management (TM) is a concept to improve the efficiency and reliability of the power system. The aim of this article is to look for the current development of TM methods based on AI and Machine Learning (ML) technology. In AI paradigm, MultiAgent System (MAS) based method is an active research area and are still in evolution. Hence this article describes how MAS based method applied in TM. This paper also finds that MAS based method faces major difficulty to design or set up goal to various agents and describes how ML technique can contribute to that solution. A brief comparison analysis between MAS and ML techniques are also presented. At the end, this article summarizes the most relevant open challenges and issues on the AI based methods for transactive energy management.

中文翻译：

---

开放的挑战和问题：用于交互管理的人工智能

人工智能 (AI) 的进步提高了能源管理的自动化程度。在智能能源管理或智能电网框架中，所有设备和分布式资源和可再生资源都嵌入其中，从而降低了成本。作为智能能源管理系统，Transactive management (TM) 是一个提高电力系统效率和可靠性的概念。本文的目的是寻找基于 AI 和机器学习 (ML) 技术的 TM 方法的当前发展。在人工智能范式中，基于多代理系统（MAS）的方法是一个活跃的研究领域，并且仍在发展中。因此，本文描述了基于 MAS 的方法如何应用于 TM。本文还发现基于 MAS 的方法在为各种代理设计或设置目标方面面临重大困难，并描述了 ML 技术如何为该解决方案做出贡献。还介绍了 MAS 和 ML 技术之间的简要比较分析。最后，本文总结了基于人工智能的交互能源管理方法中最相关的开放挑战和问题。