Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced grids
In light of global efforts to mitigate climate change, the rapid proliferation of Battery Electric Vehicles (BEVs) has introduced new operational challenges for power distribution networks, particularly in terms of managing the fluctuating and location-specific nature of charging demand. This paper...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024727 |
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| Summary: | In light of global efforts to mitigate climate change, the rapid proliferation of Battery Electric Vehicles (BEVs) has introduced new operational challenges for power distribution networks, particularly in terms of managing the fluctuating and location-specific nature of charging demand. This paper introduces a robust and uncertainty-conscious optimization framework for the strategic integration of BEV charging infrastructure into unbalanced distribution systems. The proposed approach incorporates key sources of uncertainty, such as variability in base electrical loads, intermittent outputs from renewable energy sources (RESs), and stochastic patterns of BEV charging behavior, using Hong’s Three-Point Estimate Method (H3PEM). Also, a novel Advanced Multi-objective Growth Optimizer (AMOGO) is developed to achieve a balanced optimization across technical and planning objectives, including the minimization of total active losses, the improvement of the Total Voltage Stability Index (TVSI), and the maximization of the network’s BEV hosting capacity. The proposed strategy is validated on the widely recognized IEEE 123-bus unbalanced system under multiple operational scenarios, each differing in the number and capacity of installed charging stations. Benchmark comparisons with established metaheuristic algorithms of Particle Swarm Optimization (PSO), the original GO, and the Enzyme Action Optimizer (EAO) highlight AMOGO’s superior performance. Results show that AMOGO not only exhibits faster and more stable convergence but also achieves significant enhancements in system efficiency and resilience, with a 72.7 % reduction in power losses and a 10.51 % improvement in voltage stability. These findings demonstrate the practical potential of the proposed framework for facilitating high-penetration BEV integration in next-generation smart distribution systems. |
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| ISSN: | 2590-1230 |