Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Osman Akbulut (), Muhammed Cavus (), Mehmet Cengiz, Adib Allahham, Damian Giaouris and Matthew Forshaw
Additional contact information
Osman Akbulut: School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
Muhammed Cavus: School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Mehmet Cengiz: School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
Adib Allahham: Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Damian Giaouris: School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Matthew Forshaw: School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK

Energies, 2024, vol. 17, issue 10, 1-23

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 optimizing 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 (GRUs), basic recurrent neural networks (RNNs), 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 optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter 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 R 2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R 2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

Keywords: deep learning; energy management system; optimization; microgrid; resilience; rule-based control; stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/10/2260/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/10/2260/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:10:p:2260-:d:1390363

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().