Energy Trading in Smart Grid: A Deep Reinforcement Learning-based Approach

Abstract

To achieve the efficient operation of the smart grid, appropriate energy trading strategy plays an important role in reducing multi-agent costs in the trading process as well as alleviating grid pressure. However, with the increase of the number of participants in smart grid, energy trading has been greatly challenged in terms of stable and effective operation. In this paper, we propose a deep reinforcement learning-based energy double auction trading strategy. Through the deep reinforcement learning algorithm, buyers and sellers can gradually learn the environment by treating the three elements: total supply, total demand and their own supply and demand as states, in addition, regarding both bidding price and quantity as bidding strategy. Results from simulation indicate that as the learning continues and reaches the convergence, both the cost which buyers pay in the auction has decreased significantly, and the profit which sellers earn in the auction will increase.