Application of Deep Learning in Virtual Power Plants—A Review

Hongjie Zhu, Zezhen Zhang, Yang Gao, Xiao Hu, Ying Cai, Yunqi Wang, Qin Wang, Qian Ai

ABSTRACT The advancement of deep learning (DL) has substantially improved the power grid’s ability to model and optimise diverse and highly complex tasks. Although intelligent transformation is an inevitable trend, traditional approaches increasingly reveal limitations in computational efficiency, nonlinear nonconvex optimisation, and multi‐layer control mechanism design. To overcome these bottlenecks, effectively integrating advanced technologies—particularly DL—into Virtual Power Plants (VPPs) has become essential. As a result, recent research has shifted toward leveraging digital twins, communication network topology modelling, Large Language Model (LLM)‐based cloud platforms and hierarchical distributed control strategies. These approaches aim to address the core challenges of deploying DL in practical applications, thereby driving VPPs toward a higher level of modernisation. This review provides a systematic assessment of existing research on VPP optimisation. Specifically, it examines key technological paradigms, including digital twins, communication network topology, LLM‐based cloud platform construction and hierarchical distributed control, analysing the principal methods and major achievements through which these technologies shape current practice. With respect to DL–market integration mechanisms, the paper further identifies the limitations and key challenges of existing studies and outlines potential future research directions in methodological development and the expansion of optimisation objectives, thereby offering insights for advancing the field.