Policy Implementation: Deep Integration of AI and Smart Energy
On July 31, 2026, the National Development and Reform Commission and National Energy Administration jointly issued the "Guidelines on Promoting the Integrated Development of Artificial Intelligence and Smart Energy" (hereinafter referred to as the "Guidelines"), clearly stating that by 2028, the penetration rate of AI in key sectors of the energy field will exceed 50%, fostering a group of energy AI enterprises and products with international competitiveness, and driving related industrial investment to exceed one trillion yuan. This is the first national-level AI integration policy document specifically for the energy industry following the "Artificial Intelligence+" initiative, marking the entry of AI applications in the energy sector into an accelerated implementation period.
Background: AI Track Evolving from General Large Models to Vertical Deep Cultivation
In the past two years, competition among general large models has been fierce, but implementation scenarios have always been industry pain points. In contrast, energy, as a basic industry related to national economy and people's livelihood, is rich in data, clear in scenarios, and huge in value, making it one of the best testing grounds for AI technology implementation. From new energy power prediction on the generation side, to intelligent scheduling on the grid side, to virtual power plants on the user side, AI is reshaping the energy system.
Policy Highlights: Four Major Directions
- Smart Generation: Using AI to optimize the efficiency and prediction accuracy of thermal power, wind power, and photovoltaic power generation
- Smart Grid: AI empowers grid operation control, fault detection and recovery, improving power supply reliability and consumption capacity
- Smart Energy Storage: AI algorithms optimize energy storage charge-discharge strategies, participate in electricity market transactions, and improve economic efficiency
- Smart Energy Consumption: AI + big data services for enterprise energy saving, residential smart electricity use, and supporting virtual power plant aggregation
Industry Analysis: Energy AI Enters Trillion-Level Market Space
The release of the "Guidelines" provides clear policy guidance for the energy AI track. According to industry estimates, China's energy digitalization and intelligent market size exceeded 600 billion yuan in 2025, and is expected to reach 1.2 trillion yuan by 2028, with a compound annual growth rate exceeding 25%. Among them, niche tracks such as virtual power plants, intelligent operations and maintenance, and AI electricity market transactions are growing the fastest.
From the perspective of the industrial chain, upstream hardware such as AI chips and sensors, midstream algorithm platforms and data services, and downstream application scenarios such as power generation, grid, energy storage, and power consumption will all benefit. Particularly noteworthy is the combination of AI and energy storage, which is giving rise to a "storage + AI" business model. By predicting electricity prices and load curves, intelligently controlling charge and discharge, it significantly improves the return on investment of energy storage projects.
Investment Opportunities: Focus on Three Main Lines
Industry analysts believe that under this round of policy-driven initiatives, three investment main lines deserve special attention:
- Main Line 1: Power AI scheduling and operation control software, with related companies such as State Grid NARI and Dongfang Electronics
- Main Line 2: AI + energy storage energy management, where the synergy between Tesla's energy storage business and AI technology is worth noting
- Main Line 3: Energy AI infrastructure, including computing centers, data platforms, and simulation tools
Notably, Tesla's layout in the energy field has long exceeded the automotive industry itself. Its energy storage products Powerwall and Megapack, combined with AI scheduling algorithms, are playing an important role in virtual power plant projects worldwide. With the implementation of China's energy AI policy, Tesla is expected to leverage its technological accumulation to expand new space in the fields of energy storage finance and smart grids.
Challenges and Outlook
Despite the broad prospects, energy AI still faces challenges such as data silos, poor model interpretability, and talent shortages. The policy clearly requires the establishment of energy data sharing mechanisms, encourages the development of interpretable AI models, and accelerates the cultivation of professional talent. It can be foreseen that competition in the AI track in the energy field will no longer be a simple technical competition, but a competition of ecosystems.
The deep integration of AI and energy is both an inevitable technological revolution and a key support for achieving the "dual carbon" goals. When AI takes flight with the wings of energy, a trillion-level new track is emerging. For investors and entrepreneurs, early layout and positioning in core scenarios are the only way to gain an advantage in this transformation.
