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Deep Reinforcement Learning-Based Trading Strategy for Load Aggregators on Price-Responsive Demand.

Guang Yang¹, Songhuai Du¹, Qingling Duan¹

¹College of Information and Electrical Engineering, China Agricultural University, Beijing, China.

Computational Intelligence and Neuroscience

|September 22, 2022

Summary

This study introduces a deep reinforcement learning strategy for electricity trading, enhancing load aggregator profits. The method uses real-time market data to optimize purchasing and selling decisions, leading to improved revenue.

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Area of Science:

Artificial Intelligence
Smart Grids
Computational Economics

Background:

Modern electricity markets integrate demand response with spot markets via price-responsive loads.
Load aggregators' trading strategies are vital for maximizing profits in these dynamic markets.

Purpose of the Study:

To develop a deep reinforcement learning-based strategy for electricity trading to maximize load aggregator revenue.
To leverage real-time electricity prices and demand data for optimized trading decisions.

Main Methods:

Application of the deep deterministic policy gradient (DDPG) algorithm.
Integration of a bidirectional long- and short-term memory (BiLSTM) network within the actor-critic framework.
Utilizing BiLSTM to extract market state features and infer hidden states in partially observable Markov states.

Main Results:

The proposed strategy demonstrated increased revenue generation in electricity markets.
Validation using datasets from the New England and Australian electricity markets confirmed the method's effectiveness.
Comparative experiments showed superior yield results compared to existing approaches.

Conclusions:

The deep reinforcement learning strategy effectively optimizes electricity trading for load aggregators.
The integration of BiLSTM enhances the DDPG algorithm's ability to handle complex market dynamics.
This approach offers a promising solution for maximizing revenue in smart grid-enabled electricity markets.