Method of Integrating Deep Reinforcement Learning for Electricity Price Prediction and Intelligent Trading Strategy Construction in Smart Grids

European Conference on Electrical Engineering and Computer Science  ·  2026

Miao-Yi Xiang; Shu-Hao Gong; Yu-Hao Jing; Lu-Hui Lin; Yang Tan;

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ABSTRACT

Traditional electricity price prediction methods are difficult to fully exploit deep nonlinear features, and traditional reinforcement learning (RL) algorithms have unstable convergence in trading strategy construction. In response to these issues, this article proposes a method for electricity price prediction and intelligent trading strategy construction that integrates deep learning (DL) and deep reinforcement learning (DRL). Firstly, in terms of electricity price prediction, this paper constructs a hybrid prediction model (VMD-MLP-LSTM) based on Variational Mode Decomposition (VMD) combined with Multi Layer Perceptron (MLP) and Long Short Term Memory Network (LSTM). This model utilizes VMD to adaptively decompose the original non-stationary electricity price sequence to reduce the difficulty of prediction; Furthermore, the local variation features of each modal component are extracted through MLP, and their temporal dependencies are captured using LSTM, ultimately achieving accurate sliding prediction of electricity prices. Secondly, at the level of trading strategy, this article constructs an intelligent trading strategy model based on Deep Deterministic Policy Gradient (DDPG) on the basis of the prediction model, providing optimal trading strategies for both electricity generation and consumption parties in the market. Simulation experiments show that the method can accurately predict electricity prices and improve the efficiency of strategy formulation, which is of great significance for the stable operation of smart grids and the optimization of revenue for power generation enterprises.

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