摘要
Abstract
As an innovative mode for the efficient management of distributed energy resource(DER),the virtual power plant(VPP)has become an important solution for the large-scale grid connection of DER through extensive interconnection and flexible scheduling of energy.While improving the energy utilization efficiency,the VPP faces serious challenge of privacy data leakage due to its massive,multi-source data and the broad and open"cloud-pipeline-edge-terminal"communication architecture.This paper thoroughly analyzes the privacy risks and preservation requirements of the VPP,systematically reviews the full-process privacy preservation technologies from data collection and sharing,trading,scheduling to revenue settlement,and summarizes the shortcomings of existing privacy preservation schemes in terms of computational cost,dynamic adaptability,and the balance between privacy and data availability.In response to the above shortcomings,a hierarchical and collaborative overall privacy preservation system of"cloud-pipeline-edge-terminal"is proposed,and the feasibility and technical challenges faced by deep learning and generative artificial intelligence in overcoming above deficiencies through behavior feature mining,dynamic game strategies,and semantic perception are prospected,providing new ideas for building a safe and intelligent VPP.关键词
虚拟电厂/分布式能源/隐私保护/网络攻击/安全分析/人工智能Key words
virtual power plant/distributed energy resource(DER)/privacy preservation/cyber attack/security analysis/artificial intelligence
