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Optimal electric vehicle charging station deployment in Microgrids: strategies and approaches
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DOI：10.1080/15567249.2026.2625082.png)
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Abstract
En 中文
Thanks to the paradigm changes that happened in the automobile industry, resulting in reshaping and updating it, Electric vehicles (EVs) have started to replace their petroleum counterparts gradually. However, to cope with the EVs' fast deployment, a well-planned and efficient expansion of electric vehicle charging stations (EVCSs) becomes inevitable in improving the efficacy of their deployment in distribution systems. Owing to EV integration, significant energy losses and voltage deviations in radial distribution systems (RDS) have become key concerns for power engineers and energy providers. EVCSs are critical components such that they are used as e-mobility units where both Grid to Vehicle (G2V) and Vehicle to Grid (V2G) modes of operation are in practice as and when needed. The proposed work mainly aims at optimally organizing and designing EVCSs and Distributed Generator (DG) units employing Hybrid Teaching Learning-based Particle Swarm Optimization (TLB-PSO) approaches while taking energy losses and voltage sensitivity, as major parameters into consideration. The objective function is formulated aiming to reduce real and reactive power losses, average voltage deviation index, and loss sensitivity factors, and validated for IEEE 33 & 69 RDS in MATLAB. Results imply minimal system losses & improved stability, due to coordinated deployment of EVCS & DG units. The proposed research work yields reduced real and reactive power loss of 59.55% and 50.41% for IEEE 33 bus RDS and 51.74% and 42.03% for IEEE 69 RDS, respectively. In addition, the proposed methodology also improves the voltage profile from 0.9131 p.u. to 0.9607 p.u for IEEE 33 RDS and from 0.9565 p.u. to 0.9699 p.u. for IEEE 69 RDS by optimal placement of DG units and EVCS.
Keywords:
EVCS deployment
electric vehicles
electric vehicle charging stations
distributed generator units
optimal sizing
hybridTLB-PSO algorithm
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