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Lookup NU author(s): Fulong YaoORCiD, Dr Wanqing ZhaoORCiD, Dr Matthew ForshawORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Predictive control approaches based on deep reinforcement learning (DRL) have gained significant attention in microgrid energy optimization. However, existing research often overlooks the issue of uncertainty stemming from imperfect prediction models, which can lead to suboptimal control strategies. This paper presents a new error temporal difference (ETD) algorithm for DRL to address the uncertainty in predictions, aiming to improve the performance of microgrid operations. First, a microgrid system integrated with renewable energy sources (RES) and energy storage systems (ESS), along with its Markov decision process (MDP), is modelled. Second, a predictive control approach based on adeep Q network (DQN) is presented, in which a weighted average algorithm and a new ETD algorithm are designed to quantify and address the prediction uncertainty, respectively. Finally, simulations on a realworld US dataset suggest that the developed ETD effectively improves the performance of DRL in optimizing microgrid operations.
Author(s): Yao F, Zhao W, Forshaw M
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2024 9th International Conference on Renewable Energy and Conservation (ICREC 2024)
Year of Conference: 2024
Online publication date: 22/11/2024
Acceptance date: 13/08/2024
Date deposited: 11/12/2024
ISSN: 1865-3537
Publisher: Springer
URL: https://www.icrec.org/2024.html
ePrints DOI: 10.57711/yz04-5338
Series Title: Green Energy and Technology
