ENHANCING THE DEVELOPMENT OF AN INTELLIGENT IOT INTEGRATED CONTROL SCHEME FOR OPTIMIZING MICROGRID PERFORMANCE AND ENERGY STORAGE MANAGEMENT

The increasing demand for reliable, efficient, and sustainable energy solutions has accelerated the adoption of microgrids integrated with renewable energy sources and advanced energy storage systems. However, the variability of renewable generation and fluctuating load demands present significant challenges in ensuring optimal microgrid performance and energy management. This study focuses on enhancing the development of an intelligent Internet of Things (IoT)-integrated control scheme aimed at optimizing microgrid performance and improving energy storage management. The proposed system leverages intelligent algorithms such as artificial neural networks (ANN), fuzzy logic, and machine learning techniques in combination with IoT-enabled communication infrastructure to enable real-time monitoring, adaptive control, and efficient energy distribution. Through seamless coordination between distributed energy resources, storage systems, and control units, the intelligent scheme enhances the stability, responsiveness, and efficiency of the microgrid. The integration of IoT facilitates data acquisition and remote control, enabling predictive and autonomous energy management. This research contributes to the advancement of smart grid technologies by providing a scalable, intelligent framework for next-generation microgrid operations, ensuring improved energy utilization, reduced operational costs, and enhanced system resilience. The conventional, inefficient energy storage utilization, which causes poor development of microgrid performance and energy storage management, was 25%. Meanwhile, when an intelligent IOT integrated control scheme was integrated in the system, it decisively reduced it to 21.5%. Finally, with these results obtained, the percentage enhancement in the development of micro grid performance and energy storage management when an intelligent IOT integrated control scheme was imbued in the system was 3.5%

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-15
DOI
https://doi.org/10.5281/zenodo.17122012
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

ENHANCING THE DEVELOPMENT OF AN INTELLIGENT IOT INTEGRATED CONTROL SCHEME FOR OPTIMIZING MICROGRID PERFORMANCE AND ENERGY STORAGE MANAGEMENT

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

ENHANCING THE DEVELOPMENT OF AN INTELLIGENT IOT INTEGRATED CONTROL SCHEME FOR OPTIMIZING MICROGRID PERFORMANCE AND ENERGY STORAGE MANAGEMENT

article en

Abstract

The increasing demand for reliable, efficient, and sustainable energy solutions has accelerated the adoption of microgrids integrated with renewable energy sources and advanced energy storage systems. However, the variability of renewable generation and fluctuating load demands present significant challenges in ensuring optimal microgrid performance and energy management. This study focuses on enhancing the development of an intelligent Internet of Things (IoT)-integrated control scheme aimed at optimizing microgrid performance and improving energy storage management. The proposed system leverages intelligent algorithms such as artificial neural networks (ANN), fuzzy logic, and machine learning techniques in combination with IoT-enabled communication infrastructure to enable real-time monitoring, adaptive control, and efficient energy distribution. Through seamless coordination between distributed energy resources, storage systems, and control units, the intelligent scheme enhances the stability, responsiveness, and efficiency of the microgrid. The integration of IoT facilitates data acquisition and remote control, enabling predictive and autonomous energy management. This research contributes to the advancement of smart grid technologies by providing a scalable, intelligent framework for next-generation microgrid operations, ensuring improved energy utilization, reduced operational costs, and enhanced system resilience. The conventional, inefficient energy storage utilization, which causes poor development of microgrid performance and energy storage management, was 25%. Meanwhile, when an intelligent IOT integrated control scheme was integrated in the system, it decisively reduced it to 21.5%. Finally, with these results obtained, the percentage enhancement in the development of micro grid performance and energy storage management when an intelligent IOT integrated control scheme was imbued in the system was 3.5%

Zenodo (CERN European Organization for Nuclear Research)
Enugu State University of Science and Technology (NG), Delta State University (US)
Openalex Percentile: Top 41%
Geochemistry and Geologic Mapping
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ENHANCING THE DEVELOPMENT OF AN INTELLIGENT IOT INTEGRATED CONTROL SCHEME FOR OPTIMIZING MICROGRID PERFORMANCE AND ENERGY STORAGE MANAGEMENT · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS