A Simple Energy Management System for a Micro Grid System Using Rule-Based Algorithm

Basit, Nur Aziidah Izzati Abdul; Rosmin, Norzanah; Hatib, Aede; Hussin, Siti Maherah; Said, Dalila Mat

doi:10.1007/978-981-19-3923-5_24

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Rulebased control operates MG components, including loads, ESSs, and renewable energy generators, using predefined logic and decision-making rules. Although rule-based plans are straightforward to understand and implement, they may not efficiently use the resources at hand and are frequently not flexible enough to react to changing operating situations [42].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Akbulut,

Cavus,

Cengiz

et al. 2024

Energies

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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 R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

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Section: Literature Reviewmentioning

confidence: 99%

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

Akbulut,

Cavus,

Cengiz

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Rule-based control operates MG components, including loads, ESSs, and renewable energy generators, using predefined logic and decision-making rules. Although rule-based plans are straightforward to understand and implement, they may not 3 of 23 efficiently use the resources at hand and are frequently not flexible enough to react to changing operating situations [34].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Akbulut,

Cavus,

Cengiz

et al. 2024

Preprint

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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.

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Energy management controllers: strategies, coordination, and applications

Bakare,

Abdulkarim,

Shuaibu

et al. 2024

Energy Inform

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Energy management controllers (EMCs) are pivotal for optimizing energy consumption and ensuring operational efficiency across diverse systems. This review paper delves into the various control strategies utilized by energy management controllers and explores their coordination mechanisms. Additionally, it examines the architectures of energy management controllers and their real-world implementations. The paper surveys a spectrum of EMCs, including conventional-based, rule-based, optimization-based, hybrid methods, and commercial software-based approaches, highlighting their respective advantages and drawbacks. It investigates how these controllers are coordinated within complex energy systems to achieve optimal performance and adaptability. Furthermore, the review outlines different architectures of energy management controllers, ranging from centralized to decentralized designs, discussing their suitability for various applications and their impact on system performance. Real-world applications of energy management controllers in sectors such as smart grids, buildings, industrial processes, and transportation systems are examined. Case studies and examples demonstrate the efficacy of different control strategies and architectures in addressing specific energy management challenges and achieving desired outcomes. Overall, this review provides valuable insights into the current landscape of energy management controller design and implementation, offering direction for future research and development in the pursuit of energy optimization and sustainability.

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A Simple Energy Management System for a Micro Grid System Using Rule-Based Algorithm

Cited by 3 publications

References 7 publications

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

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

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

Energy management controllers: strategies, coordination, and applications

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