en.Wedoany.com Reported - The "2026 Embodied Intelligence Development Plan" of State Grid Corporation of China has been recently disclosed. According to the plan, in 2026, State Grid plans to centrally procure approximately 8,500 units of various embodied intelligence equipment, with a total investment of about 68 billion yuan. The focus will be on four major scenarios: power patrol, live-line work, emergency rescue, and warehousing logistics, aiming to drive the transformation of the power grid from "manual operation and maintenance" to "autonomous operation and maintenance". Including follow-up procurements by China Southern Power Grid and local energy groups, the total investment in embodied intelligence by China's power industry in 2026 is expected to exceed 100 billion yuan.

The procurement list comprises three major categories: four-legged patrol robot dogs, humanoid live-line work robots, and dual-arm patrol robots, executed via a batch procurement strategy: trial procurement in Q1, large-scale procurement in Q3, and supplementary procurement in Q4. The four-legged patrol robot dogs total 5,000 units with a budget of 1.5 billion yuan, deployed in substations, transmission lines, and mountainous power grids. The humanoid live-line work robots total 500 units with a budget of 2.5 billion yuan, covering distribution network live-line work and ultra-high voltage projects. The dual-arm patrol robots total 3,000 units with a budget of 1.8 billion yuan, focusing on substation equipment operation and fault handling. The total procurement amount for these three categories is 5.8 billion yuan, with the remaining 1 billion yuan invested in technology research and development and talent training.

Robot body companies such as Deep Robotics, Unitree, AGIBOT, UBTECH, and Fourier Intelligence have entered the procurement list. Industrial chain companies deeply involved in the power grid sector for years, including Xiangheng International, Yijiahe, and Shenhao Technology, have also been shortlisted. State Grid estimates that after replacing human labor with embodied intelligence equipment, each unit can save an average of 500,000 to 800,000 yuan in labor costs annually, with an investment payback period of about 2 to 3 years. Patrol efficiency is expected to improve fivefold, average fault handling time to be reduced by 60%, and power supply reliability to increase by 0.5 percentage points. By replacing human workers with intelligent equipment, exposure risks for over 90% of workers in high-risk operations can be reduced, and the accident rate can be lowered by 80%.

The plan document outlines phased targets: the penetration rate of embodied intelligence in key areas is to be raised to 30% by 2026. By 2027, the adoption rate of intelligent agents should surpass 80%, with embodied intelligence equipment covering over 80% of high-risk operation scenarios. By 2030, deep integration of embodied intelligence with the digital twin grid is to be achieved, enabling autonomous operation and maintenance of the power grid. The investment of approximately 6.8 billion yuan in embodied intelligence in 2026 accounts for about 8.75% of State Grid's total annual intelligent investment of roughly 80 billion yuan, marking the transition of energy central state-owned enterprises' procurement of embodied intelligence from "small-scale verification" to formal "large-scale deployment."

All procured equipment must comply with State Grid's "Technical Specifications for Power Embodied Intelligence Equipment". Priority will be given to suppliers capable of deep integration with the "Brilliant Power Large Model" and supporting localized deployment to ensure data security. The Brilliant Power Large Model was released on December 19, 2024. It is the first hundred-billion-level AI large model in China's power industry, covering State Grid's headquarters and 27 provincial power companies, with applications in grid operation, equipment management, operation control, and other fields. This technical barrier gives a first-mover advantage to companies with years of deep involvement in the grid sector and a rich accumulation of data and scenario understanding.

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