Preprint

Article

Deep Reinforcement Learning for Energy Management in a Microgrid with Flexible Demand

Altmetrics

Downloads

803

Views

916

Comments

A peer-reviewed article of this preprint also exists.

Taha abd el halim Nakabi^*,

Pekka Toivanen

Taha abd el halim Nakabi^*,

Pekka Toivanen

This version is not peer-reviewed

Submitted:

06 October 2020

Posted:

07 October 2020

You are already at the latest version

Alerts

Abstract

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.

Keywords:

Subject: Engineering - Energy and Fuel Technology

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.