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源网荷储一体化虚拟电厂
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源网荷储一体化虚拟电厂在高耗能工业领域的智慧能源应用方案(63页 PPT)
Sou Hu Cai Jing· 2025-08-16 01:10
Group 1 - The core focus of the report is on the application of the NeuSeer industrial internet platform by Qiyun Technology in the high-energy-consuming industrial sector, specifically in the integrated virtual power plant for source-network-load-storage [1] - Qiyun Technology is recognized as a leading domestic industrial internet enterprise, participating in the formulation of multiple national standards, and the NeuSeer platform has evolved from version 1.0 to 3.0 [1][9] - The NeuSeer platform's development stages include: 1.0 focusing on project needs with issues in customization and data processing; 2.0 transitioning to productization with a complete product line; and 3.0 emphasizing industry-specific solutions, algorithm models, and enhanced collaboration [1][11] Group 2 - The integrated virtual power plant supported by a smart "energy brain" coordinates distributed energy clusters internally and participates in electricity trading externally [1] - The industrial internet plays a crucial role in connecting, gaining insights, and optimizing operations through technologies like digital twins, facilitating the digitalization of assets and operations, and contributing to carbon neutrality [1] - Application cases include comprehensive operations for solar-storage-charging-usage services, energy transition strategies for a coal chemical group, and optimization models for wind-solar-storage-hydrogen microgrids [1] Group 3 - In the future, Qiyun Technology will focus on high-end manufacturing and leverage various technological advancements to develop vertical applications and build ecosystems to promote the digital transformation of smart energy [2] - The NeuSeer platform aims to provide industry-specific applications and solutions, enhancing the integration of energy and industrial services [6][25] - The platform supports a wide range of industries, including manufacturing, energy, rail transit, and oil and gas, with applications in predictive maintenance, intelligent management, and real-time production decision-making [26]