Journal
SUSTAINABLE ENERGY GRIDS & NETWORKS
Volume 25, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.segan.2020.100413
Keywords
Artificial intelligence; Deep reinforcement learning; Demand Response; Dynamic pricing; Energy management system; Microgrid; Neural networks; Price-responsive loads; Smart grid; Thermostatically controlled loads
Funding
- Jenny and Antti Wihuri Foundation, Finland
Ask authors/readers for more resources
This study investigates the performance of various deep reinforcement learning algorithms in enhancing the energy management system of a microgrid. A novel microgrid model is proposed and seven algorithms are implemented and compared. The results show significant differences in convergence to optimal policies among the algorithms, with the modified asynchronous advantage actor-critic algorithm achieving the highest model performance and convergence to near-optimal policies.
In this paper, we study the performance of various deep reinforcement learning algorithms to enhance the energy management system of a microgrid. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a set of thermostatically controlled loads, a set of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate among the different flexible sources by defining the priority resources, direct demand control signals, and electricity prices. Seven deep reinforcement learning algorithms were implemented and are empirically compared in this paper. The numerical results show that the deep reinforcement learning algorithms differ widely in their ability to converge to optimal policies. By adding an experience replay and a semi-deterministic training phase to the well-known asynchronous advantage actor-critic algorithm, we achieved the highest model performance as well as convergence to near-optimal policies. (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available