Integration of Electric Vehicles in Smart Grids for Efficient Power Management using Improved Dwarf Mongoose Optimization

Power management efficiency grows more effective when Electric Vehicles (EVs) merge with Smart Grids (SGs) because this allows enhanced distribution of loads and utilization of energy resources. However, the fluctuating charging demands of EVs can introduce grid stability challenges, necessitating e...

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Veröffentlicht in:2025 7th International Conference on Inventive Material Science and Applications (ICIMA) S. 511 - 516
Hauptverfasser: Al-Shaikh, Ala'a, Mathumitha, R., Singh, Navdeep, Sivaramkrishnan, M., Dhivya, S., Kalpana, P. Elizabeth
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 28.05.2025
Schlagworte:
and Wind Turbine
Battery Storage System
Electric Vehicle
Improved Dwarf Mongoose Optimization
Optimization
Photovoltaic
Power system stability
Real-time systems
Regulation
Reliability
Smart Grid
Smart grids
Stability analysis
Total harmonic distortion
Voltage control
Wind turbines
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Zusammenfassung:Power management efficiency grows more effective when Electric Vehicles (EVs) merge with Smart Grids (SGs) because this allows enhanced distribution of loads and utilization of energy resources. However, the fluctuating charging demands of EVs can introduce grid stability challenges, necessitating effective coordination strategies. This paper proposes an Improved Dwarf Mongoose Optimization (IDMO) method which optimizes the integration of EV technology in SGs. The main goal of this proposed approach aims to reduce Total Harmonic Distortion (THD) alongside improving voltage stability and enhancing the entire grid operational performance. An IDMO algorithm efficiently manages EV charging operations as well as discharging requirements to fulfill power flow regulations and handle fluctuating energy needs. The proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Binary Particle Swarm Optimization (BPSO), Fire Hawk Optimizer (FHO), and Archimedes Optimization Algorithm (AOA). The experimental results show IDMO generates power with a THD of 1.92% thus proving it can optimize EV integration along with power management systems. IDMO stands out from other methods in lowering THD values while boosting energy efficiency and grid reliability which establishes it as a promising solution to integrate EVs effectively into SGs.
DOI:10.1109/ICIMA64861.2025.11074112