基于学习优化的智能电网能量管理研究综述

Learning-to-optimize based energy management in smart grid:A survey

受近年来人工智能浪潮的积极影响,基于学习优化的智能电网能量管理研究不断涌现,并被视为一个富有前景的研究方向.现有学习优化方法的设计思路大相径庭,了解这些思路的优势和局限性对掌握该交叉领域动态和推动其长足发展至关重要.鉴于此,总结3种主要的学习优化类型:学习最优解、学习热启动、学习约束,分析不同学习优化类型的设计思路和优点,基于现有工作的缺憾进一步提出5大挑战,并提供一些潜在的解决方案,以期为该交叉领域的研究者提供更全面的信息和新的视角.

With dramatic development of artificial intelligence in recent years,energy management based on“learning to optimize”is regarded as a promising research direction of smart grid.The designs of existing learning-based energy management approaches are quite different and there is no doubt that understanding the advantages and limitations of these ideas is essential to capture the research trends in this field.In this survey,references are carefully selected and categorized into three main groups:Optimal solution learning,warm-start learning,and constraints learning.The design details and advantages of these groups are systematically analyzed.Further,based on the shortcomings of existing studies,we propose five major challenges and provided some potential solutions.We hope this survey can provide more comprehensive information and new perspectives for researchers in this field.