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

Energy optimization in smart grid has gradually shifted to agent-based machine learning method represented by the state of art deep learning and deep reinforcement learning. Especially deep neural network based reinforcement learning methods are emerging and gain popularity to for smart grid application. In this work, we have applied the applied two deep reinforcement learning algorithms designed for both discrete and continuous action space. These algorithms were well embedded in a rigorous physical model using Simscape Power Systems
^TM
(Matlab/Simulink
^TM
Environment) for smart grid optimization. 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 reward.