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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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024727 |
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| author | Abdullah M. Shaheen Aya R. Ellien Adel A. El-Ela Ali M. El-Rifaie |
| author_facet | Abdullah M. Shaheen Aya R. Ellien Adel A. El-Ela Ali M. El-Rifaie |
| author_sort | Abdullah M. Shaheen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-395d2003dca84e6792c7ba1eb84ecb02 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-395d2003dca84e6792c7ba1eb84ecb022025-08-20T03:39:05ZengElsevierResults in Engineering2590-12302025-09-012710640210.1016/j.rineng.2025.106402Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced gridsAbdullah M. Shaheen0Aya R. Ellien1Adel A. El-Ela2Ali M. El-Rifaie3Department of Electrical Engineering, Faculty of Engineering, Suez University, P.O,Box: 43221, Suez, Egypt; Corresponding authors.South Delta Electricity Distribution Company (SDEDCo), Ministry of Electricity, Tanta, EgyptElectrical Engineering Department, Faculty of Engineering, Menoufiya University, Shebin El-Kom 32511, EgyptCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2590123025024727Battery electric vehiclesOperating modes of DGsHong's three-point estimate methodAdvanced multi-objective growth optimizer algorithm |
| spellingShingle | Abdullah M. Shaheen Aya R. Ellien Adel A. El-Ela Ali M. El-Rifaie Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced grids Results in Engineering Battery electric vehicles Operating modes of DGs Hong's three-point estimate method Advanced multi-objective growth optimizer algorithm |
| title | Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced grids |
| title_full | Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced grids |
| title_fullStr | Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced grids |
| title_full_unstemmed | Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced grids |
| title_short | Uncertainty-aware multi-objective growth optimizer of EV charging stations allocation in unbalanced grids |
| title_sort | uncertainty aware multi objective growth optimizer of ev charging stations allocation in unbalanced grids |
| topic | Battery electric vehicles Operating modes of DGs Hong's three-point estimate method Advanced multi-objective growth optimizer algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025024727 |
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