机器学习赋能的优化算法及其在新型电力系统中的应用与展望

Machine Learning Empowered Optimization Algorithms and Their Applications and Prospects in New Type Power System

近年来,随着可再生能源的快速发展和新型电力系统建设的加速推进,电力系统的不确定性日益突出,给建模和优化调度带来巨大挑战。机器学习技术可有效利用海量历史数据,为优化稳定快速求解提供了新的理论依据。该文详细分析该新兴交叉领域的研究进展。首先,针对一般化的优化问题,基于机器学习与优化计算的交互方式,将其基本算法框架概括为机器学习端到端优化求解、机器学习增强的优化求解算法以及机器学习和电力系统优化联合驱动求解3类,分别阐述其基本原理和适用问题形式。其次,针对相关技术在电力系统优化中的应用研究进展进行梳理,总结其基本方法和应用效果。最后,对基于学习的优化方法的发展趋势及其在新型电力系统中的应用前景进行展望,以期为该新兴领域的后续研究工作提供参考和启发。

In recent years,with the rapid development of renewable energy and the accelerated promotion of new power system construction,the uncertainty of power systems has become more prominent,posing huge challenges for modeling and optimization scheduling.Machine learning techniques can effectively utilize vast historical data to provide new theoretical basis for optimizing stable and fast solutions.This paper provides a detailed analysis of the progress in this emerging interdisciplinary field.First,for general optimization problems,based on the interaction between machine learning and optimization computing,the basic algorithm framework is summarized into three categories:machine learning end-to-end optimization solving,machine learning enhanced optimization solving algorithms,and machine learning and power system optimization joint driving solving.Their basic principles and applicable problem forms are explained respectively.Then,the progress of related technology applications in power system optimization is reviewed and the basic methods and application effects are summarized.Finally,the development trends of learning-based optimization methods and their application prospects in new power systems are explored,with the aim of providing references and inspirations for future research work in this emerging field.