GenCos' Behaviors Modeling Based on Q Learning Improved by Dichotomy
This work addresses inefficiencies in simulating generation companies' behaviors for electricity market participants, but it is incremental as it builds on existing Q-learning methods.
The paper tackled the slow convergence of Q-learning for modeling electricity market behaviors by proposing a dichotomy-improved Q-learning algorithm, which reduced the required iterations and improved computational efficiency in simulations of a repeated Cournot game.
Q learning is widely used to simulate the behaviors of generation companies (GenCos) in an electricity market. However, existing Q learning method usually requires numerous iterations to converge, which is time-consuming and inefficient in practice. To enhance the calculation efficiency, a novel Q learning algorithm improved by dichotomy is proposed in this paper. This method modifies the update process of the Q table by dichotomizing the state space and the action space step by step. Simulation results in a repeated Cournot game show the effectiveness of the proposed algorithm.