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<p>The proposed method is used in this study to demonstrate a small hydropower microgrid in a smart distribution with a grid-isolated electric car charging station. The method proposed here is a consolidation of Komodo Milpir Algorithm (KMA) and Spiking Deep Residual Networks (SDRN) algorithm, so it is known as KMA-SDRN. The reduction of fuel, operating, and maintenance costs, and the reduction of environmental costs, and power loss reduction are all objectives of this article. Uncertainty prediction sets are used to model the variability of PV power and Electric Vehicles (EVs) state of charge (SoC). The electric car is shown as both a source and a load. This proposed approach would minimize operational expenses while raising the amount of renewable energy that was absorbed. It would also systematically direct the discharging and charging of EVs to reduce the highest demand and fill up valleys. The proposed strategy is then applied in the MATLAB platform, the proposed total power loss is 1.7&#xa0;kW, the cost is 200$ and the accuracy is 92%.The outcomes indicate that the proposed KMA-SDRN method offers a higher performance than existing methods.</p>

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Small-hydropower microgrid in smart distribution network with grid-isolated electric vehicle charging system using KMA-SDRN algorithms

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • T. Sathesh Kumar,
  • S. Kirubadevi,
  • P. Shanmugapriya,
  • Balasubbareddy Mallala

摘要

The proposed method is used in this study to demonstrate a small hydropower microgrid in a smart distribution with a grid-isolated electric car charging station. The method proposed here is a consolidation of Komodo Milpir Algorithm (KMA) and Spiking Deep Residual Networks (SDRN) algorithm, so it is known as KMA-SDRN. The reduction of fuel, operating, and maintenance costs, and the reduction of environmental costs, and power loss reduction are all objectives of this article. Uncertainty prediction sets are used to model the variability of PV power and Electric Vehicles (EVs) state of charge (SoC). The electric car is shown as both a source and a load. This proposed approach would minimize operational expenses while raising the amount of renewable energy that was absorbed. It would also systematically direct the discharging and charging of EVs to reduce the highest demand and fill up valleys. The proposed strategy is then applied in the MATLAB platform, the proposed total power loss is 1.7 kW, the cost is 200$ and the accuracy is 92%.The outcomes indicate that the proposed KMA-SDRN method offers a higher performance than existing methods.