English:

Against the dual backdrop of new-type power system construction, industrial low-carbon transition, and the deep upgrading of digital grids, industrial loads—with their massive scale, strong regulation potential, and broad response dimensions—have become core high-quality resources for flexible grid regulation, source-grid-load-storage coordination, and electricity-carbon synergy. With the rapid iteration of artificial intelligence, digital simulation, and computing-power-enabling technologies, electricity-computing synergy has emerged as a brand-new core frontier in energy digital transformation, providing a novel paradigm for the refined perception, intelligent regulation, and efficient grid-load interaction of industrial loads.
This special session focuses on the deep integration of AI technologies, electricity-computing synergy mechanisms, and industrial power systems. Centering on the fundamental theories and key technologies of precise industrial load modeling, state perception, flexibility mining, and intelligent optimized control, it highlights frontier directions such as computing-powered multidimensional collaborative optimization of industrial loads, coupled scheduling of electricity-computing resources, bidirectional grid-load interaction, virtual power plant aggregation and operation, and electricity-carbon synergy for emission reduction.
It aims to resolve the pain points of traditional industrial energy-use control—insufficient accuracy, fragmented computing-power and electricity resources, and lagging grid-load interaction—and to drive industrial loads to transform from passively rigid energy consumers into AI-driven, electricity-computing-synergized, actively responsive, and efficiently interactive flexible regulation entities, providing theoretical support and technical references for industrial energy digitalization, efficient and safe grid operation, and the realization of dual-carbon goals.

中文:

在新型电力系统建设、工业低碳转型与数字电网深度升级的双重背景下，工业负荷凭借体量规模大、调节潜力强、响应维度广的特点，已成为电网灵活调节、源网荷储协同、电碳协同增效的核心优质资源。随着人工智能、数字仿真、算力赋能技术的快速迭代，电力-算力协同（电算协同）成为能源数字化转型的全新核心赛道，为工业负荷精细化感知、智能化调控、网荷高效互动提供了全新范式。本专题聚焦AI技术、电算协同机制与工业电力系统的深度融合，围绕工业负荷精准建模、状态感知、灵活性挖掘、智能优化调控等基础理论与关键技术，重点探索算力赋能下工业负荷多维度协同优化、电算资源耦合调度、网荷双向互动、虚拟电厂聚合运行、电碳协同降碳等前沿方向。旨在破解传统工业用能调控精度不足、算力电力资源割裂、网荷互动滞后等痛点，推动工业负荷从被动刚性用能，向AI驱动、电算协同、主动响应、高效互动的新型灵活调节主体转变，为工业能源数字化、电网高效安全运行与双碳目标落地提供理论支撑与技术参考。