Sensitivity Analysis of Reinforcement Learning to Schedule the battery in Grid-tied microgrid

  • Khawaja Haider Ali
  • Hasnain Hyder
  • Muhammad Asif Khan
Keywords: Reinforcement learning, offline RL, microgrid, battery operation, sensitivity analysis

Abstract

This research paper explores the application of offline reinforcement learning (RL) in controlling battery operation in a grid-connected microgrid. The study investigates the impact of different parameters on the performance of the RL algorithm, such as the number of discretization levels, gamma, and alpha values. The results show that the convergence time and optimality of the RL algorithm are affected by the choice of these parameters. The research concludes that carefully selecting the discretization levels of state-action spaces and RL hyperparameters is crucial for optimal RL algorithm performance. The benchmark offline sensitivity analysis can be compared in the future with other RL approaches, such as function approximation or DRL methods.

Published
2023-05-04
How to Cite
Khawaja Haider Ali, Hasnain Hyder, & Muhammad Asif Khan. (2023). Sensitivity Analysis of Reinforcement Learning to Schedule the battery in Grid-tied microgrid. University of Sindh Journal of Information and Communication Technology , 6(3), 115-123. Retrieved from https://sujo.usindh.edu.pk/index.php/USJICT/article/view/6218

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