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Electrical Engineering and Systems Science > Systems and Control

arXiv:2103.04152 (eess)
[Submitted on 6 Mar 2021]

Title:Correlated Deep Q-learning based Microgrid Energy Management

Authors:Hao Zhou, Melike Erol-Kantarci
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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.
Comments: Accepted by 2020 IEEE 25th International Workshop on CAMAD, 978-1-7281-6339-0/20/$31.00 ©2020 IEEE
Subjects: Systems and Control (eess.SY); Machine Learning (cs.LG)
Cite as: arXiv:2103.04152 [eess.SY]
  (or arXiv:2103.04152v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2103.04152
Related DOI: https://doi.org/10.1109/CAMAD50429.2020.9209254

Submission history

From: Hao Zhou Mr [view email]
[v1] Sat, 6 Mar 2021 16:43:18 UTC (1,401 KB)
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