Self-Dispatch of Wind-Storage Integrated System: A Deep Reinforcement Learning Approach

The uncertainty of wind power and electricityprice restrict the profitability of wind-storage integrated system (WSS) participating in real-time market (RTM). This paper presents a self-dispatch model for WSS based on deep reinforcement learning (DRL). The designed model is able to learn the integrated bidding and charging policy of WSS from the historical data. Besides, the maximum entropy and distributed prioritized experience replay frame, known as Ape-X, is used in this model. The Ape-X decouples the acting and learning in training by a central shared replay memory to enhance the efficiency and performance of the DRL procedures. Besides, the maximum entropy framework enables the designed agent to explore various optimal possibilities, thus the learned policy is more stable considering the uncertainty of wind power and electricity price. Compared with traditional methods, this model brings more benefits to wind farms while ensuring robustness.

Automated summary

This paper presents a self-dispatch model based on deep reinforcement learning for wind-storage integrated system (WSS) in real-time market (RTM). The model is able to learn the bidding and charging policy of WSS from historical data and uses the Ape-X framework to improve efficiency and performance. With the maximum entropy framework, the model can explore optimal possibilities considering the uncertainty of wind power and electricity price, bringing more benefits to wind farms.