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<p>This paper proposes a novel Pelican Optimization Algorithm (POA) for an optimal multi-objective Energy Management System (EMS) in a Microgrid (MG). This study focuses on minimizing total operational costs and emissions while maintaining supply-demand balance. It considers different cost factors such as operation and maintenance, fuel and depreciation costs. The proposed EMS is evaluated using five distinct test cases starting with a grid-connected MG with a passive main grid. Then, the system is upgraded gradually to study the effects of these updates on operational cost. The main grid’s mode of operation transferred to be active in the second case. Then, the RESs, such as Photovoltaics (PVs) and Wind Turbines (WTs), has been penetrated to the system in the third case to study the impact of green energy on the system. The fourth case includes incorporation of Battery Energy Storage System (BESS) considering its degradation cost to increase system reliability and further enhancing the MG’s performance. The fifth case integrates Internet of Things (IoT) technology through the ThingSpeak platform. This enables real-time monitoring and optimization of the MG that further reduces costs by dynamically adjusting power generation based on real-time data such as load demand, electricity pricing, and RES output. The results clarify the significant impact of performed upgrades on the system’s operational cost. Especially, RESs penetration that reduces cost by about <b>45.6%</b> of base case cost. Total cost also decreased to <b>52.5%</b> of basic cost after incorporating all system changes. The results demonstrate that the proposed POA not only significantly reduces operational costs and emissions across all test cases but also provides a scalable and adaptable solution for MGs. POA outperforms recently used algorithms by about <b>9%</b> cost reduction in some cases. By integrating IoT for real-time optimization and accounting for practical concerns such as battery degradation, this approach offers a comprehensive and forward-looking solution to the challenges of modern energy management. The findings suggest that POA, in conjunction with IoT, can enhance the sustainability, reliability, and efficiency of MGs, making it a promising tool for future smart grids and energy systems.</p>

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A New IoT-based Adaptive Optimization for Multi-Objective Energy Management in Microgrids Considering Battery Degradation Cost and Emissions Minimization

  • 期刊论文

  • 发表日期：

    2025年9月13日

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  • Karim M. Hassanin,
  • Diaa-Eldin A. Mansour,
  • Takeyoshi Kato,
  • Tamer F. Megahed

摘要

This paper proposes a novel Pelican Optimization Algorithm (POA) for an optimal multi-objective Energy Management System (EMS) in a Microgrid (MG). This study focuses on minimizing total operational costs and emissions while maintaining supply-demand balance. It considers different cost factors such as operation and maintenance, fuel and depreciation costs. The proposed EMS is evaluated using five distinct test cases starting with a grid-connected MG with a passive main grid. Then, the system is upgraded gradually to study the effects of these updates on operational cost. The main grid’s mode of operation transferred to be active in the second case. Then, the RESs, such as Photovoltaics (PVs) and Wind Turbines (WTs), has been penetrated to the system in the third case to study the impact of green energy on the system. The fourth case includes incorporation of Battery Energy Storage System (BESS) considering its degradation cost to increase system reliability and further enhancing the MG’s performance. The fifth case integrates Internet of Things (IoT) technology through the ThingSpeak platform. This enables real-time monitoring and optimization of the MG that further reduces costs by dynamically adjusting power generation based on real-time data such as load demand, electricity pricing, and RES output. The results clarify the significant impact of performed upgrades on the system’s operational cost. Especially, RESs penetration that reduces cost by about 45.6% of base case cost. Total cost also decreased to 52.5% of basic cost after incorporating all system changes. The results demonstrate that the proposed POA not only significantly reduces operational costs and emissions across all test cases but also provides a scalable and adaptable solution for MGs. POA outperforms recently used algorithms by about 9% cost reduction in some cases. By integrating IoT for real-time optimization and accounting for practical concerns such as battery degradation, this approach offers a comprehensive and forward-looking solution to the challenges of modern energy management. The findings suggest that POA, in conjunction with IoT, can enhance the sustainability, reliability, and efficiency of MGs, making it a promising tool for future smart grids and energy systems.