Version 1
: Received: 8 April 2024 / Approved: 8 April 2024 / Online: 9 April 2024 (08:49:08 CEST)
How to cite:
Akbulut, O.; Cavus, M.; Cengiz, M.; Allahham, A.; Giaouris, D.; Forshaw, M. Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques. Preprints2024, 2024040625. https://doi.org/10.20944/preprints202404.0625.v1
Akbulut, O.; Cavus, M.; Cengiz, M.; Allahham, A.; Giaouris, D.; Forshaw, M. Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques. Preprints 2024, 2024040625. https://doi.org/10.20944/preprints202404.0625.v1
Akbulut, O.; Cavus, M.; Cengiz, M.; Allahham, A.; Giaouris, D.; Forshaw, M. Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques. Preprints2024, 2024040625. https://doi.org/10.20944/preprints202404.0625.v1
APA Style
Akbulut, O., Cavus, M., Cengiz, M., Allahham, A., Giaouris, D., & Forshaw, M. (2024). Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques. Preprints. https://doi.org/10.20944/preprints202404.0625.v1
Chicago/Turabian Style
Akbulut, O., Damian Giaouris and Matthew Forshaw. 2024 "Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques" Preprints. https://doi.org/10.20944/preprints202404.0625.v1
Abstract
Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.
Keywords
deep learning; energy management system; optimization; microgrid; resilience; rule-based control; stability.
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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