零碳实际上是一种目标牵引，鼓励园区尽可能多地使用清洁能源、利用周边的绿色电力，通过风电、光 伏、储能来实现绿电直供园区。 2026年，国家级零碳园区建设迈入进行时。 一场关乎产业形态重塑、园区功能升级与绿色发展转型的变革全面启动。首批国家级零碳园区建设名单 中，河北沧州沧东经济开发区、辽宁沈阳中德高端装备制造产业园等6家园区需在2027年率先完成建 设。 建设零碳园区，需要园区管理方、企业、专业服务机构等主体的合力协作。当前，一批园区已展开实 践，探索零碳工业体系路径。近日，远景科技集团首席可持续发展官孙捷在接受21世纪经济报道专访时 表示，国家级零碳园区是应对绿色贸易挑战的桥头堡。远景最初在内蒙古鄂尔多斯布局全球首个零碳产 业园，正是为应对海外市场对电池等产品设立的碳壁垒。 孙捷表示，结合远景零碳产业园的实践来看，打造零碳产业园需要依托新型能源系统、零碳数字化管理 体系与绿色工业集群三大体系。将能源转型、数字技术和产业集聚三者融合，不仅有助于减碳，也能提 升产业链整体竞争力。这一模式正在成为工业园区绿色低碳转型的重要路径。 依托三大体系构建零碳园区 《21世纪》：如何理解零碳园区的内核？在国家级零碳园区建设 ...

Core Concept - The concept of "Physical AI" is introduced as a new paradigm that combines AI with physical laws and knowledge graphs, aiming to eliminate the "hallucinations" of traditional language models and enable reliable AI applications in the physical world [2][3]. Group 1: Development of Physical AI - The future development of Physical AI is seen as a significant direction, particularly in the context of energy systems [2]. - The integration of data intelligence with physical laws like energy conservation and aerodynamics is expected to enhance AI's reliability in real-world applications [2]. Group 2: Energy Model and AI Applications - The energy model is considered crucial for reconstructing energy systems, providing a rich application scenario for Physical AI [4]. - AI's ability to process vast amounts of data in milliseconds can help optimize decision-making in complex energy systems, addressing human anxieties related to energy management [4][5]. Group 3: Competitive Landscape - The U.S. is viewed as lacking the necessary industrial scenarios and complex energy systems to support the development of Physical AI and energy models, giving China a potential advantage in this field [3][5]. - Companies that only specialize in single areas like wind or solar energy may struggle to develop comprehensive energy models due to a lack of holistic understanding and data [5]. Group 4: Addressing Industry Challenges - The energy sector is currently facing issues of overcapacity and price wars, particularly in solar and wind energy, which have led to significant financial losses for many companies [7]. - Physical AI and energy models are proposed as solutions to end the cycle of homogeneous competition and shift the focus from material assets to intelligent assets [8]. Group 5: Future Outlook - The development of energy models is expected to evolve into a robust system capable of generating significant value within 1-3 years [11]. - The future energy system is envisioned as an ecosystem of intelligent agents rather than just a collection of devices, aimed at better integrating renewable energy sources and providing energy at lower costs [11].

Core Insights - The core argument presented by Zhang Lei, Chairman of Envision Technology Group, is that artificial intelligence (AI) is evolving from a mere tool to a主体, fundamentally transforming the energy sector into an "intelligent agent" ecosystem rather than just a collection of physical assets [1][2][4] Group 1: AI's Role in Energy Systems - AI is seen as a revolutionary force that can handle the increasing complexity and market uncertainties associated with high proportions of renewable energy in the grid [2][3] - The concept of "Physical AI" is introduced, which integrates AI with physical laws and knowledge graphs, enhancing its reliability in real-world applications [2][3] Group 2: Technological Advancements - Envision has made significant breakthroughs in large models, particularly with the "Tianji" meteorological model, which improves medium to long-term weather forecasting accuracy, crucial for the reliable operation of renewable energy [3] - The "Tianshu" energy model, capable of real-time control through advanced algorithms, is successfully applied to optimize energy trading and asset investment decisions [3] Group 3: Future Competitiveness - The future competitiveness of energy companies will shift from traditional metrics like installed capacity to the scale of "AI assets" [3][4] - The industry is urged to focus on the intelligence of their models and the scale of their AI capabilities, marking a significant transition from physical to intelligent assets [3]

Core Viewpoint - Envision Energy has launched the Galileo AI Wind Turbine, which aims to address major pain points in the wind power industry by providing more flexible and precise power generation strategies and higher reliability, marking a new phase in the application of physical artificial intelligence in the sector [1][3]. Summary by Sections Addressing Industry Pain Points - The Galileo AI Wind Turbine offers a validated solution to three major pain points in the wind power industry: inaccurate forecasting (power/load/consumption/electricity price), poor turbine performance, and high safety and quality risks. The implementation of the "Tianshu" energy model intelligent control platform has led to over a 20% increase in revenue for wind farms equipped with AI compared to those without [3][4]. Enhancing Forecast Accuracy - The "Tianji" meteorological model utilizes advanced computing power and a model with over 10 billion parameters to achieve significant breakthroughs. It integrates multi-modal data from satellites, radar, and ground stations, along with data from over 800 GW of global energy assets, to generate precise forecasts for the next 15-30 days within just three minutes [5][6]. Improving Power Generation Capability - The core of the Galileo AI Wind Turbine is a neural network with over 100 million parameters, functioning as a "super brain" for the turbine. This system, supported by high-performance chips, enables real-time online reasoning to handle complex, non-linear problems that traditional control logic struggles with. The intelligent control platform allows for real-time adjustments and self-healing capabilities, enhancing overall efficiency [7][8]. Increasing Warning Accuracy - The development of a high-fidelity digital twin platform is crucial for improving warning accuracy in the wind power sector. By leveraging AI computing power and extensive operational data, the integration of multi-modal information has significantly enhanced prediction accuracy. For instance, early detection of blade failures through sound and strain monitoring has improved maintenance scheduling, resulting in substantial operational gains [9][10]. Future of AI in Wind Power - The transition from traditional wind turbines to intelligent systems capable of understanding weather changes and market dynamics represents a significant evolution in the industry. The potential for further advancements in artificial intelligence applications within wind power remains vast [11].