With the acceleration of global energy transition and the rapid growth of renewable energy installed capacity, traditional power grids are facing unprecedented challenges. The large-scale integration of intermittent renewable energy sources such as wind and solar power into the grid has made the supply-demand balance of the power system more complex. In this context, artificial intelligence technology is becoming the core driving force for smart grid dispatch, reshaping the operation of the power system from load forecasting, power generation optimization to fault self-healing.
In the field of load forecasting, deep learning models have significantly surpassed traditional time series methods. A prediction model based on LSTM (Long Short Term Memory Network) and Transformer architecture can simultaneously process multidimensional inputs such as meteorological data, historical loads, holiday factors, and dynamic electricity price signals, reducing the error rate of short-term load forecasting from 5% -8% in traditional methods to within 2% -3%. State Grid's pilot projects in some provincial power grids show that the AI driven load forecasting system has improved the accuracy of the day ahead dispatching plan by about 40%.
In terms of power generation optimization, reinforcement learning is changing the way traditional unit commitment problems are solved. The traditional mixed integer programming method takes a long time to calculate when dealing with large-scale renewable energy uncertainty, while the scheduling strategy based on deep reinforcement learning can provide a near optimal unit start stop plan in seconds. Google's DeepMind has used reinforcement learning to increase the predictive value of wind farm power generation by about 20%, and similar technology is being introduced into domestic electricity market trading decisions.
Computer vision technology plays a crucial role in the monitoring and fault diagnosis of transmission lines. The high-definition cameras deployed on transmission towers and drones, combined with edge AI chips, can identify safety hazards such as wire icing, tree barriers, and bird nests in real time. The practical case of Southern Power Grid shows that AI visual inspection has increased the detection rate of line defects from 60% in manual inspection to over 95%, and shortened the single inspection cycle from weeks to hours.
On the distribution network side, AI driven fault self-healing systems are changing the traditional "manual dispatching on-site maintenance" mode. When a fault occurs in the line, the topology analysis system based on graph neural network (GNN) can locate the fault point in milliseconds and achieve fast restoration of non fault areas through automatic control of segment switches. The Shenzhen Power Supply Bureau's AI based self-healing distribution network project has achieved "second level self-healing", reducing the time for fault restoration from minutes to less than 3 seconds.
Looking ahead, the role of AI in smart grids will evolve from assisting decision-making to autonomous scheduling. The combination of digital twin technology and large-scale models will enable power grid dispatchers to simulate various extreme scenarios in a virtual environment and formulate response strategies in advance. With the deepening of the power system reform and the comprehensive promotion of the electricity spot market, AI will play a more core role in power trading decision-making, demand side response, and virtual power plant operation.
[Reference source] This article is a comprehensive compilation of industry information publicly released by State Grid and Southern Power Grid, as well as publicly available research results from DeepMind.