Core Insights - The AI + Energy Development Conference highlighted the critical role of AI in addressing challenges in both traditional and renewable energy sectors, emphasizing energy security as a primary concern in the current international landscape [1][3]. Traditional Energy - Coal remains a dominant energy source in China, accounting for 53.2% of primary energy consumption and 67% of primary energy production in 2024, with a coal output of 4.76 billion tons [4]. - The disparity between production and consumption percentages is attributed to the need for significant imports of crude oil (approximately 550 million tons) and natural gas (1.3 billion cubic meters), which inflate the consumption statistics [4]. - The coal industry faces challenges such as safety incidents, health risks, and low recovery rates, but AI technology can provide solutions by enabling "transparent geology" for better mining operations and reducing the need for underground workers [4]. Renewable Energy - Renewable energy, particularly wind and solar, is crucial for energy growth, with installed capacity expected to reach 1.6 billion kilowatts in 2024 and 3.6 billion kilowatts by 2035 [5]. - The conversion of installed capacity to actual electricity generation is essential, as merely increasing capacity without effective conversion leads to waste [5]. - AI is identified as a key enabler for optimizing various energy storage technologies and enhancing the flexibility of thermal power, which is vital for maximizing the utilization of renewable energy [5]. Energy Efficiency - Current energy efficiency in China is significantly lower than in the U.S., with 3.4 tons of standard coal required to generate $10,000 GDP compared to 1.7 tons in the U.S., indicating substantial room for improvement [6]. - Achieving U.S. levels of energy efficiency could allow China to meet future energy demands driven by GDP growth, highlighting the importance of energy efficiency in addressing future energy challenges [6].

2026年1月30日，以"智赋未来.能启新篇"为主题的AI+能源发展大会在中关村会议中心举行。在大会交流环节，中国科学院院士韩布兴结合自身学习体会与 研究实践，围绕AI赋能能源领域高质量发展、绿色资源循环利用等核心议题，分享了独到见解。他聚焦二氧化碳、生物质、废弃塑料等可再生可循环资源 的利用，强调AI对提升科研创新能力、推动能源高效综合利用的重要作用，为能源绿色转型提供了兼具专业性与实践性的思路。 韩布兴开篇指出，人工智能正深刻改变人类生产生活，成为推动社会经济发展的重要力量，这一影响在能源领域同样凸显。从能源生产、储运到终端利用， AI技术将在各个环节发挥日益重要的作用，为能源高效综合利用提供有力支撑，助力能源行业实现高质量发展。 结合自身化学化工的研究背景，韩布兴强调，绿色化、高端化、智能化是化学化工领域的三大核心发展方向，其中智能化与能源领域的融合尤为关键。他重 点分享了自身深耕的绿色资源循环利用领域，明确该领域与能源发展密切相关，更是实现"双碳"目标、推动能源绿色转型的重要抓手，并详细介绍了三大核 心资源的循环利用现状与价值。 关于二氧化碳利用，韩布兴介绍，我国每年因化石资源利用产生的二氧化碳约达1 ...

Core Insights - The global energy transition is currently in its third critical phase, shifting from fossil fuels to renewable and sustainable energy, but faces significant challenges as fossil fuels still account for nearly 80% of the global primary energy structure [4] - The international oil price is experiencing an unusual decline despite a tense global situation, primarily due to an oversupply of crude oil, with the latest forecast indicating global oil inventories could exceed 4 million barrels per day by 2026 [4] - The implementation of China's dual carbon control policy, which began on January 1, aims to control carbon emission intensity and total emissions, significantly impacting high-energy-consuming industries [5] Energy Transition Challenges - The transition from fossil fuels to renewable energy is complex and arduous, with traditional energy's dominance unlikely to change in the short term [4] - The International Energy Agency (IEA) initially predicted a global oil inventory of 1.22 million barrels per day for 2026, but recent data suggests it will surpass 4 million barrels per day, affecting oil price trends [4] - The carbon market in China is progressing, with a 30% increase in trading volume last year, although carbon prices decreased, indicating a potential impact on corporate development and economic trends [5] AI Empowerment in Energy Sector - AI technology is expected to significantly enhance efficiency in the oil and gas exploration and development sector, potentially increasing energy supply and facilitating market-oriented operations of oil storage facilities [6] - Companies are increasingly willing to invest in digital energy, with applications in platform construction, data asset management, and energy forecasting becoming crucial for the digital and intelligent transformation of the energy industry [6] - The concept of "near-zero carbon oilfields" is proposed as a more accurate term, as true zero carbon is unattainable in fossil fuel extraction, with over 60% of carbon emissions occurring at the consumption stage [6] International Experience and Future Directions - Nordic countries have made significant progress in near-zero carbon development, with some achieving nearly 100% renewable electricity and initiating numerous CCUS zero-carbon pilot projects [7] - Major international oil companies have adjusted their green development strategies due to poor profitability in green businesses, increasing investments in oil and gas exploration instead [7] - China's energy transition can benefit from international technological experiences, but it possesses unmatched advantages in the full industrial chain of renewable energy sources, making market-driven applications crucial for future development [8]

——访中国工程院院士、煤炭清洁高效加工利用专家赵跃民 1月11日，煤炭无人化开采数智技术全国重点实验室第一届学术委员会、战略指导委员会第二次会议暨教育部协同 创新中心2025年年会在北京隆重召开。中国工程院院士、煤炭清洁高效加工利用专家赵跃民围绕煤炭智能分选、 区域能源转型、协同创新发展等核心议题，分享了前沿思考与实践成果，为资源型地区高质量发展提供了深刻洞 见。 中国工程院院士、煤炭清洁高效加工利用专家赵跃民 煤炭分选提质赋能"双碳"战略筑牢能源安全 我国煤炭资源量大但原煤品质整体偏低，加工利用程度不足，这一现状既制约了能源利用效率，也对生态环境造 成压力。面对鄂尔多斯（600295）盆地煤层地质条件复杂、开采效率与安全矛盾突出的挑战，赵跃民院士带领团 队长期深耕煤炭清洁高效加工利用领域，核心突破在于实现煤炭的"提质升级"。 "我们的核心工作是将开采出来的煤炭进行分选提质，精准分离灰分、硫分、水分等杂质，以高品质精煤供应终端 用户。"赵跃民院士指出，煤炭作为我国能源安全的"压舱石"，其四大核心应用场景——燃煤发电、炼焦冶金、煤 化工、建材领域，对煤质均有明确要求。通过分选技术的革新，不仅能提升煤炭利用效率、 ...

2026年1月30日，以"智赋未来.能启新篇"为主题的AI+能源发展大会在中关村会议中心举行。在大会圆桌论坛环节，国家能源局原副局长张玉清围绕人工智 能与能源行业融合发展展开分享，深度解读国家发改委、能源局《人工智能+能源高质量发展的实施意见》出台背景，阐释人工智能赋能能源行业的核心价 值，并提出三大实践路径，为推动AI与能源行业深度融合、助力能源产业高质量发展提供了清晰的实践指引。 张玉清指出，《人工智能+能源高质量发展的实施意见》的出台，是深入贯彻党中央、国务院关于发展人工智能的决策部署，落实国务院深入实施"人工智 能+"行动的具体举措。其核心目的，是抓住人工智能发展的重大战略机遇，推动人工智能技术在能源行业的落地应用，通过人工智能与能源行业的深度融 合，激活能源产业发展新动能，进而推动能源行业实现高质量发展。 在张玉清看来，人工智能对能源行业的赋能价值覆盖全产业链，成为推动能源产业升级的重要抓手。从生产、传输到加工、利用，能源行业的各个核心环节 都能借助AI技术实现创新突破，既可以推动传统能源行业的转型升级，也能优化新能源的消纳与利用效率，助力新能源产业高质量发展。而人工智能与能 源行业的深度融合，最终 ...

刘燕华指出当前人工智能正处于爆发式发展阶段，绿色能源领域也在经历深刻变革，二者的融合已成为推动新时代经济发展的重要驱动力。他强调，人工智 能的发展已从理论奠基、技术探索迈向应用突破阶段，正从单一任务处理向多模态融合、自主决策演进，而这场技术竞争的本质，是技术生态、产业资源与 战略定力的综合较量。 2026年1月30日，以"智赋未来.能启新篇"为主题的AI+能源发展大会在中关村会议中心举行。科技部原副部长、党组成员刘燕华发表致辞，围绕人工智能与 绿色能源融合发展的核心议题，深刻剖析两者融合的战略意义、协调效应，明确AI技术的发展阶段与应用方向，为"AI+绿色能源"的创新实践提供了清晰指 引。 刘燕华表示，智慧能源作为新时代经济发展的主要驱动力，正深刻改变着社会与经济发展模式。他期待本次大会能够从多个角度深入探讨人工智能与绿色能 源的融合路径，希望与会嘉宾能够通过交流碰撞收获启发，在未来的发展中实现更大突破，共同推动能源领域的创新发展与低碳转型。 刘燕华引用国际能源署预测数据，凸显了AI与绿色能源融合的巨大价值——到2035年，AI技术的规模化应用可产生几十亿吨每年的二氧化碳减排潜力。他 进一步阐释，二者之间存在 ...

2026年1月30日，以"智赋未来.能启新篇"为主题的AI+能源发展大会在中关村会议中心举行。中关村科学城管委会副主任、海淀区副区长唐超发表致辞，立 足海淀区作为北京国际科技创新中心核心区的定位，围绕AI与能源产业融合发展，分享了海淀实践、布局了未来方向，传递出以区域创新优势赋能能源转 型的坚定决心。 唐超指出，当前全球科技变革与产业变革加速演进，人工智能作为核心驱动力，正全方位赋能千行百业，其与能源产业的深度融合，更是推动能源系统向智 能化、绿色化、低碳化转型的关键力量，为构建清洁、安全、高效的新型能源体系筑牢根基。而海淀区凭借得天独厚的创新禀赋，成为推动"AI+能源"融合 发展的重要承载地。 唐超表示，海淀区将持续打造一流营商环境和创新生态，诚邀各界创业者、投资人扎根海淀、深耕兴业，携手推动AI与能源产业双向赋能。她强调，2026 年作为国家"十五五"规划开局之年，此次大会汇聚行业共识与智慧，破解发展难题，海淀区将以自身创新优势，将思想火花转化为能源转型的实际力量，为 建设能源强国、推动人类可持续发展贡献海淀力量。 在创新资源与产业基础上，海淀区具备不可替代的优势。唐超介绍，区域内聚集了清华、北大等21所 ...

截至目前，国网庄浪县供电公司组织输电运检班、供电所运维人员开展输电线路单电源设备巡视及红外 测温2轮次，配网设备巡视测温2轮次,发现消除配变设备开关接头发热隐患5处,更换配变低压刀闸3组。 （牛烨） 春节临近，年味渐浓。1月29日晚，国网庄浪县供电公司春节保电设备巡维"小分队"冒着寒风，顶着零 星飘落的雪花,穿梭于田野乡间,开展输配网设备红外测温工作,把脉问诊迎峰度冬电网设备，春节保电工 作如火如荼推进。 "今年紫荆路的亮化灯饰光彩夺目，引人驻足打卡拍照，等这轮设备测温结束，我也领老婆孩子去紫荆 路拍照……。"测完10千伏112通永线隔离开关B相温度的通化供电所员工韩仁和对班长张世光说。 ？"距离春节还有十多天，公司已全面加大春节保电工作力度。我们将严格落实国网甘肃省电力公司、 国网平凉供电公司职代会精神，坚决扛牢保电责任，不折不扣执行国网庄浪县供电公司2025年春节保电 方案。"国网庄浪县供电公司负责人表示，"下一步我们将加强管辖电网设备的巡视维护，紧盯输配网线 路高负荷运行时段，对设备绝缘子、导线、变压器线夹接点及引流线开展红外测温，精准排查发热隐 患，为班组提前发现、消除设备问题提供数据支撑,全力保障庄 ...

Core Viewpoint - The acquisition of 51% stake in Hubei Jidandun Fire Technology Co., Ltd. by Xuzhou Helenzhe Special Vehicle Co., Ltd. marks a strategic integration aimed at enhancing industrial upgrades and achieving significant synergies in the fire safety sector, particularly in the context of rapid developments in energy storage, electric systems, and new energy vehicles [1][4]. Group 1: Acquisition Details - On January 16, 2026, Helenzhe held its first extraordinary shareholders' meeting to approve the acquisition of a controlling stake in Jidandun [1]. - The share transfer agreement was signed on January 19, 2026, and the internal decision-making processes have been completed, with the 51% stake transfer finalized [1]. Group 2: Strategic Rationale - The acquisition is not merely a financial maneuver but is based on long-term strategic collaboration and industrial upgrades [1]. - The fire safety sector is evolving from standalone equipment to integrated system solutions that include early warning, firefighting, and engineering implementation [1]. Group 3: Technological and Product Integration - Jidandun's aerosol fire extinguishing systems and early warning technologies can be deeply integrated with Helenzhe's advanced equipment, such as fire trucks and emergency power vehicles, to create a comprehensive "early warning + response" system solution [3]. - The technological advantages of Jidandun in the energy storage fire safety sector will complement Helenzhe's engineering implementation capabilities in traditional fields like electricity and emergency rescue [3]. Group 4: Market and Business Synergies - The collaboration is expected to enhance the companies' professionalism and stability in high-demand application scenarios, expanding into diverse high-safety applications such as data centers and new energy vehicles [3]. - Jidandun's leading fire technology and strong partnerships with industry leaders will enhance the overall market competitiveness of the combined entity [3]. Group 5: Future Outlook - The synergistic effects of this strategic integration will need to be gradually realized in practice, focusing on the high-demand field of new energy safety [4]. - The companies aim to explore how to integrate professional technology with industrial platforms to promote the application and enhancement of system-level safety standards [4].

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