Correlated Deep Q-learning based Microgrid Energy Management

TL;DR

This paper introduces a correlated deep Q-learning approach for microgrid energy management, coordinating multiple agents like energy storage and renewable sources to optimize operation and increase profits.

Contribution

It proposes a novel correlated deep Q-learning method with LSTM networks for microgrid management, improving coordination among agents.

Findings

40.9% higher profit for ESS agent

9.62% higher profit for PV agent

Effective coordination among microgrid entities

Abstract

Microgrid (MG) energy management is an important part of MG operation. Various entities are generally involved in the energy management of an MG, e.g., energy storage system (ESS), renewable energy resources (RER) and the load of users, and it is crucial to coordinate these entities. Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management. Each electrical entity is modeled as an agent which has a neural network to predict its own Q-values, after which the correlated Q-equilibrium is used to coordinate the operation among agents. In this paper, the Long Short Term Memory networks (LSTM) based deep Q-learning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents. The simulation result shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent, respectively.