electra-supply.comAI technology is increasingly playing a crucial role in the energy transition, with widespread applications emerging in the sector. Recently, the State Power Investment Corporation launched its self-developed large-scale renewable energy model, “Kongming.” This model is designed to enhance the efficiency of renewable energy generation and address challenges posed by extreme weather conditions. Collaboratively, companies like China Huadian, JiuTian Weather, and Huawei have introduced an AI-powered meteorological power forecasting solution, which leverages advanced AI modeling to improve prediction accuracy and reduce operational costs for power plants. Industry experts believe that as AI technology continues to evolve, its potential for accelerating energy transformation will become increasingly apparent.
**Enhancing Management Efficiency**
Zhangjiakou, located at the junction of the North China Plain and the Inner Mongolia Plateau, is one of the most resource-rich areas in Northern China for wind and solar energy. The Datang Sola Wind Power Station is situated here and utilizes a smart control system for management, allowing operators to monitor the entire wind farm, including substations and their corresponding equipment, from an integrated platform. This system offers comprehensive insights into equipment status, personnel behavior, and security environments.
Renewable energy stations, particularly in wind and solar projects, often operate in remote and dispersed locations with diverse equipment types, complicating management. Traditionally, maintenance personnel had to conduct on-site equipment control and inspections, consuming significant human, material, and financial resources, which posed a barrier to the growth of renewable energy enterprises. The integration of AI technology can reduce management costs and significantly improve overall efficiency.
“AI helps achieve intelligent control of renewable energy generation equipment, intelligent image recognition, and precise load forecasting, drastically cutting labor costs,” noted a leader from Datang International’s Renewable Energy Department. AI technology can implement smart control over wind turbines and solar inverters while leveraging self-learning algorithms to optimize electricity generation from natural resources like wind and sunlight. Moreover, AI is transforming traditional manual inspection methods by utilizing cameras equipped with AI recognition algorithms for high-precision automated inspections of photovoltaic panels and wind turbine blades.
AI technology not only enables real-time monitoring of the energy supply process but also analyzes real-time data to detect potential issues and make optimizations. “In addition to applications at the station level, AI can also optimize generation and maintenance strategies at the centralized control level,” the Datang International representative explained. AI large models can analyze vast datasets of equipment operation and historical fault data, providing early warnings for potential risks. “For instance, the fault model developed by our company can identify signs of equipment degradation and optimize maintenance strategies based on the equipment’s lifespan patterns, significantly extending device longevity and reducing the risk of outages. This results in a comprehensive optimization of maintenance management.”
AI technology is progressively infiltrating and permeating every level and detail of management within renewable energy enterprises. According to Song Jianjun, head of the Artificial Intelligence Application Research Institute at Goldwind Technology, AI is already being incorporated into various functions such as unmanned station construction, material allocation, and translation services. The company’s industry translation model helps achieve high-precision translations of technical terms, supporting the internationalization of the wind power sector. “In the future, we will continue to invest in areas such as multimodal large models, visual analysis, and operations research, enabling AI technology to fully support innovation and operational management in the renewable energy sector.”
“AI technology is transformative for the renewable energy field, enhancing overall operational efficiency,” said Chen Zhanming, a professor at Renmin University of China’s School of Applied Economics. He believes that AI not only enables companies to optimize management decisions but also helps reduce costs and increase efficiency, making it an increasingly attractive choice for renewable energy organizations.
**Optimizing Energy Dispatch**
AI technology not only enhances corporate management efficiency but also optimizes energy dispatch between companies and consumers. This summer, under the automatic control of the “Source Grid Load Storage Charge” smart regulation platform operated by the State Grid’s Dali Power Supply Bureau in Yunnan, the Tianshan Wind Farm was able to quickly and efficiently deliver renewable energy to various locations across Yunnan and the Guangdong-Hong Kong-Macao Greater Bay Area, providing comfort to thousands of households.
According to related personnel, the AI smart control model efficiently assists operators in decision-making and transfer methods, continuously performing dynamic control of current flow interfaces, automatically identifying risks, and enabling rapid responses to various anomalies in the power system. This capability significantly minimizes the risks of frequent operator errors, thereby effectively supporting the safe and stable operation of the power grid in regions where renewable energy accounts for over 60% of the annual electricity consumption.
In addition to preventing mismanagement and incorrect dispatches, AI can help formulate more optimal dispatch plans. The intermittent and volatile nature of renewable energy generation, such as during “windless heat waves” or “cloudless evenings,” poses challenges to both generation quality and grid management. AI can help integrate multidimensional data, predict fluctuations in renewable power output, and enhance supply-demand alignment. As Song Jianjun elaborated, Goldwind Technology is using AI large model technology to upgrade its existing load forecasting systems by analyzing heterogeneous data from historical generation, real-time weather, and grid loads to create more optimized dispatch plans, thereby reducing energy waste.
Extreme weather presents further challenges for renewable power generation, directly affecting output stability. AI algorithms can capture real-time atmospheric conditions and accurately predict weather events to improve renewable energy output efficiency. For instance, the “Kongming” large model developed by Guoneng Rixin has excelled in predicting extreme weather events like strong winds and typhoons, thus enhancing the accuracy of energy dispatch planning and ensuring a balance between power supply and demand.
The ability of AI to recognize patterns and optimize processes from vast data sets makes it a critical technology in advancing emerging energy dispatch models. At the 2024 International Digital Energy Exhibition, Shenzhen announced the launch of the 3.0 version of its virtual power plant management cloud platform, which has connected 55,000 adjustable load resources, including EV charging stations and building HVAC systems, totaling over 3.1 million kilowatts in capacity. “Thanks to advanced communication and data collection technologies, the platform’s data processing capacity has surged from hundreds of thousands to millions, achieving millisecond-level perceptual timeliness,” a staff member reported. This emerging energy dispatch model aggregates and optimizes power load resources from end users and is currently being explored for implementation domestically. The application of AI will accelerate the deployment and utilization of distributed renewable equipment, further supporting the energy transition.
**Fostering Innovation**
Looking ahead, the diversification of application scenarios in renewable energy will intensify the industry’s demand for AI, fostering more AI-driven innovative solutions. For instance, AI can assist in developing high-performance, low-cost materials for renewable energy generation and storage, facilitating the overall energy transition. Moreover, automation, intelligent machinery, and AI technologies can be utilized in the construction of offshore renewable energy stations, providing green, clean energy.
As renewable energy companies transition towards management models centered around “AI algorithms,” AI can catalyze further innovation in new products and models. Industry insiders suggest that while AI can facilitate more efficient energy storage and management, yielding innovations like intelligent wind turbines, unmanned power stations, and renewable energy big data platforms, it can also aid in designing more optimized resource layouts and configurations, giving rise to new models such as virtual power plants, carbon trading, and spare parts platforms.
Currently, AI applications are primarily concentrated within enterprises. However, its potential will further unfold, offering broader benefits to consumer groups. “In the energy consumption domain, AI can analyze historical usage data to characterize consumer behavior, helping users optimize switches between various power supply methods for better micro-level energy management,” Chen Zhanming noted. Beyond delivering personalized energy consumption suggestions, he believes there is potential for AI in the sales of renewable energy products and services. “By analyzing consumption patterns, we can create accurate consumer profiles, reducing energy use while enhancing consumer participation and satisfaction.”
Despite the promising outlook, there are still challenges and difficulties in leveraging AI for the renewable energy sector’s development. According to Lin Boqiang, a professor at Xiamen University’s China Energy Policy Research Institute, issues such as the uncertainty of AI performance, data silos and the lack of high-quality data, misuse and social risks, inadequate cutting-edge computing power, and the potential energy consumption of AI itself pose challenges to its application in the sector. Addressing these challenges requires a multifaceted approach, including promoting data standardization and sharing, refining relevant laws and regulations on AI research and applications, ensuring data security and ethical norms, increasing investment in technological research and development, optimizing algorithms to reduce computational requirements, and exploring operational models for using renewable energy to support AI infrastructure.
Chen Zhanming also emphasized that while AI based on large language models primarily focuses on processing and predicting information within existing knowledge frameworks, more innovative AI technologies are poised to have an even greater impact on the energy transition.
