In the morning light of the Western Sichuan plateau, the first phase of the Yalong River Lianghekou Hydropower Station's water-solar complementary project, the Kela Solar Power Station, has rows of photovoltaic panels generating power at full capacity under the sun. At 9 a.m. the previous day, a thick cloud slowly drifted toward the station with the mountain wind—in the past, this cloud would cause a sharp drop in photovoltaic output for several hours, forcing the dispatch room to urgently coordinate hydropower to fill the gap, while the marketing team had to simultaneously adjust the next day's trading prices, often in a flurry of activity that might still miss the fleeting electricity price window. However, with the Yalong River Water-Wind-Solar Integrated Intelligent Operation Large Model, by the time the cloud had not yet covered the photovoltaic panels, the model had already accurately "locked in" its trajectory, thickness, and shading duration. Within milliseconds, two instructions were issued simultaneously: the dispatch intelligent agent quickly calculated the photovoltaic output deficit and issued an order to the Lianghekou Hydropower Station to increase generation to compensate; the marketing intelligent agent, combined with real-time electricity price trends, matched the adjusted generation plan and simultaneously optimized the next day's pricing strategy. By the afternoon, when the cloud dispersed and sunlight intensified, hydropower would preemptively reduce its load to free up absorption capacity for photovoltaic generation. "From 'a cloud drifting in the morning' to 'selling each kilowatt-hour of electricity at the best price,' what used to require cross-departmental communication and multi-layered transmission can now be fully closed-loop processed in the background by the Yalong River Large Model," said Zhang Peng, General Manager of Yalong River Basin Hydropower Development Co., Ltd. This scenario is a vivid example of the first nationwide intelligent operation large model for a water-wind-solar integrated clean energy base, breaking down business silos and achieving cross-link collaboration.

This is not simply "machines replacing human labor," but a complete restructuring of operational logic. In traditional energy operations, weather forecasting is under the meteorology department, generation dispatch is under the production department, and electricity trading is under the marketing department. For a cloud's information to be converted into final generation revenue, it requires multiple rounds of coordination and step-by-step transmission, which is not only inefficient but often misses market windows. The solution from the Yalong River Large Model involves breaking down the process into multiple specialized intelligent agents for meteorology, dispatch, marketing, and operations, all built on a unified data foundation for automatic interaction and parallel decision-making. What was once a link for human information transmission and conflict resolution has become millisecond-level coordination between intelligent agents, avoiding the lag and bias of human judgment while freeing business personnel from tedious coordination tasks, allowing them to focus on more core strategic analysis.

The ease of cross-business collaboration stems from the large model's powerful predictive forecasting foundation. As the core support of the entire system, the Yalong River Company, in collaboration with NARI Group Corporation, has developed a new forecasting system based on the Huawei Pangu Large Model. For the first time, it integrates weather, runoff, and wind and solar power predictions, creating a new paradigm of "artificial intelligence + energy + meteorology." The most direct breakthrough is the extension of medium-to-long-term runoff forecasting from the traditional 45 days to 60 days. This 15-day increase is significant for basin power stations with a cascade of reservoirs, as a longer foresight period provides more flexibility in water storage and release decisions, effectively equipping the basin dispatch with a "telescope." This ability to "predict in advance" ultimately translates into tangible financial gains. For example, in late June this year, the Yalong River's inflow increased, but spot electricity prices were low. Following the conventional approach of "generating as much as the water inflow allows" would only lead to a "more generation, more loss" dilemma. However, with the application of the Yalong River Large Model, it became possible to pre-judge weather, inflow, and electricity price trends. The operations team used this insight to adjust trading strategies and generation plans in advance—locking in higher electricity prices for late June through medium-to-long-term contracts to cover base generation, while proactively storing surplus water for increased generation when spot prices rose in July, thus securing higher returns through the time difference.

"The large model aggregates information on weather, water levels, units, and markets, predicts inflow, electricity prices, and revenue, and mobilizes multiple intelligent agents for collaborative analysis and decision support, enabling a shift from human experience-based decision-making to AI-assisted decisions," said Zhang Peng. A 1% improvement in resource utilization might sound small, but for the Yalong River integrated base, which is expected to have an installed capacity of 78 million kilowatts, it represents very substantial benefits. Beyond calculating "generation accounts" and "revenue accounts," the large model has also tackled the "tough problem" of operations and maintenance on the plateau. Addressing the challenges of remote and scattered high-altitude stations in Western Sichuan, as well as the difficulty of maintenance, the Yalong River Large Model integrates monitoring data from tens of thousands of devices across the entire basin. It transforms decades of expert experience in hydropower operations and maintenance into a knowledge graph, shifting fault handling from "post-event repair" to "pre-event warning." The diagnostic accuracy for photovoltaic module faults is expected to exceed 96%, and fault location efficiency could increase by 80%, significantly reducing maintenance costs and safety risks in high-altitude regions.

A representative from Yalong River Basin Hydropower Development Co., Ltd. stated that the company will continue to deepen industry-university-research collaborative innovation, relying on the cavern-based intelligent computing center and the domestically developed large model foundation, to continue tackling cutting-edge energy artificial intelligence technologies. Using digital, domestic, and intelligent innovation achievements, they will safeguard the high-quality construction of China's new power system. From tracking the trajectory of a cloud to uncovering the optimal value of a kilowatt-hour of electricity, when AI deeply integrates with clean energy, it solves the common industry challenge of water-wind-solar complementarity, enhances new energy absorption capacity and power station operational efficiency, and injects a continuous stream of intelligent momentum into China's high-quality energy development and the achievement of the "dual carbon" goals.