AI与能源的融合，已不再停留于行业构想，而是正式被纳入国家战略体系。 近期，国家发改委、能源局印发《关于推进"人工智能+"能源高质量发展的实施意见》（下称《实施意 见》），首次将"AI+储能"纳入国家能源战略，并提出到2027年，目标建成5个以上能源行业专业大模 型、10个以上可复制示范项目、探索100个典型场景赋能路径；到2030年，能源AI技术总体达到世界领 先水平，算力与电力协同机制全面完善。 具体到新能源储能环节，AI对储能系统的赋能体现在哪些环节？现在业内企业的应用落地进度如何？ 还有哪些风险或挑战亟待解决等话题值得探讨。 从安全运维到收益增值，AI如何重塑储能产业 目前产业内的共识是，AI技术可以提升储能系统的运行效率、安全性和经济性。基于此，相关能源企 业正从安全保障、运维提效、收益增值等角度切入，加大其在AI领域的投入。 记者注意到，基于AI的资产运营理念正在头部公司形成共识。例如，10月15日，海博思创 （688411.SH）在投资者平台公开表示，未来3~5年，公司将加大社会化资本投资的独立储能项目拓展 与合作，基于公司现有的AI+大数据分析能力，规划储能资产的后端运营布局。 据其2025年 ...

Core Insights - AI technology can enhance the operational efficiency, safety, and economic viability of energy storage systems, and its integration into national energy strategies has been formalized [1][3][9] Group 1: National Strategy and Goals - The National Development and Reform Commission and the Energy Administration have issued implementation opinions that include "AI + Energy" as part of the national energy strategy, aiming to establish over five specialized large models and ten replicable demonstration projects by 2027 [1] - By 2030, the overall AI technology in energy is expected to reach a world-leading level, with a fully developed synergy between computing power and electricity [1] Group 2: Industry Applications and Investments - Energy companies are increasing investments in AI from perspectives of safety assurance, operational efficiency, and revenue enhancement [3] - HaiBoSiChuang plans to expand independent energy storage projects over the next 3-5 years, leveraging its existing AI and big data capabilities [3] - A strategic partnership between NengHui Technology and Ant Group aims to develop "Energy AI Intelligent Agents" to reconstruct management paradigms in renewable energy projects [3] Group 3: Operational Efficiency and Safety - AI can significantly improve operational efficiency in energy storage, transitioning from reactive maintenance to proactive monitoring [5][6] - AI technologies can predict battery health and lifespan, reducing failure rates and enhancing safety through precise diagnostics [4][5] - The integration of AI in operational processes allows for real-time monitoring and predictive maintenance, optimizing energy management and maximizing returns [6][7] Group 4: Market Potential and Economic Impact - The overall service market for energy storage is projected to reach between 40 billion to 50 billion yuan by 2030 [6] - AI-driven algorithms can enhance trading operations by providing accurate price forecasts and optimizing charging and discharging strategies [6][8] Group 5: Challenges and Bottlenecks - Despite the potential of AI, challenges such as data security, privacy protection, and the need for robust computational power remain [9][10] - The development of AI in energy storage is constrained by the need for advanced data centers (AIDC) and the associated high energy consumption [10][11] - The synergy between AI and energy storage must overcome commercial viability challenges due to the uncertain returns of storage projects [10][11]