With the deepening of global electricity marketization reform, electricity trading is accelerating its transformation from traditional planned scheduling mode to market-oriented bidding mode. In this transformation process, artificial intelligence technology is becoming a key tool for optimizing the trading efficiency of the electricity market and improving the accuracy of trading decisions.
The core challenge of electricity market trading lies in its high complexity and uncertainty. Electricity prices are influenced by various factors - weather changes affecting new energy output, load fluctuations affecting supply and demand balance, transmission line constraints affecting regional price differences, and even carbon emission prices indirectly transmitting to electricity costs. Traditional statistical based prediction models are difficult to simultaneously capture these nonlinear coupling relationships.
The application of AI technology in electricity market trading is mainly reflected in three aspects:
Firstly, accurate price prediction. Deep learning models, especially LSTM (Long Short Term Memory Network) and Transformer architectures, can effectively handle temporal dependencies in electricity price sequences. By jointly modeling multidimensional information such as historical electricity prices, load data, new energy output data, and weather data, AI models can improve the prediction accuracy of daily market electricity prices by 15% -25%. A study by the European Electricity Exchange (EPEX SPOT) shows that a hybrid model based on gradient boosting trees and deep learning has reduced the average absolute percentage error (MAPE) to below 8% in the daily price prediction of the German electricity market.
Secondly, intelligent pricing strategy. In the electricity spot market, power generation companies need to develop pricing strategies based on their own cost curves and predictions of competitor behavior. Deep reinforcement learning (DRL) provides a new paradigm for solving this sequential decision-making problem. Researchers have constructed a multi-agent simulation environment to allow the crew to repeatedly learn the optimal pricing strategy through game theory in a virtual market. The simulation experiment of State Grid Energy Research Institute shows that the pricing strategy based on Deep Q-Network (DQN) can increase power generation revenue by about 5% -8% compared to the traditional marginal cost pricing method in the same market environment.
Thirdly, risk management and asset optimization. AI algorithms can evaluate the risk exposure of power generation portfolios in real time and provide purchasing strategy recommendations for electricity retailers. In power systems with a high proportion of renewable energy, the fluctuation risk caused by weather uncertainty is particularly prominent. The combination of generative AI and Monte Carlo simulation can generate thousands of possible output scenarios, helping traders optimize asset allocation between intraday and intraday markets.
At present, multiple provincial power trading centers in China have begun exploring AI assisted quotation and intelligent trading decision-making systems. The southern regional electricity market has taken the lead in piloting AI settlement review and abnormal transaction monitoring, effectively reducing the workload of manual review. The PJM market in the United States and the Nord Pool market in Europe are also promoting the commercial implementation of AI assisted trading decision-making tools.
It is worth noting that the application of AI in electricity market trading still faces challenges such as data quality, model interpretability, and regulatory compliance. Electricity trading involves system operation safety and market fairness, and the "black box" characteristics of AI models need to be balanced with regulatory transparency requirements. In the future, the advancement of explainable AI (XAI) technology is expected to play a bridging role in this field.
【 Reference sources 】 Research on the Application of Artificial Intelligence in the Electricity Market by State Grid Energy Research Institute, official technical report of EPEX SPOT, and brief report on the pilot work of the Southern Regional Electricity Market.