Smart Grid Optimization by Deep Reinforcement Learning over Discrete and Continuous Action Space

Abstract

In this work, we have applied two deep reinforcement learning (DRL) algorithms designed for both discrete and continuous action space. These algorithms were well embedded in a rigorous physical model using Simscape Power SystemsTM (Matlab/SimulinkTM Environment) for smart grid optimization. Bechmark test were conducted by comparing the results from the MILP (Mixed-integer linear programming) and the DRL. The results showed that the agent successfully captured the energy demand and supply feature in the training data and learnt to choose behavior leading to maximize its profit.