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Strategy for EV Charging Station Placement Using a Bootstrapping-Based Probabilistic Power Flow
Estrategia para la ubicación de estaciones de carga de EVs utilizando un flujo de carga probabilístico basado en bootstrapping
DOI:
https://doi.org/10.15446/ing.investig.116496Keywords:
Bootstrap method, charging stations, electric vehicles, decision-making, energy efficiency, probabilistic power flow, probability density function, uncertainty (en)método bootstrap, estaciones de carga, vehículos eléctricos, toma de decisiones, eficiencia energética, flujo de carga probabilístico, función de densidad de probabilidad, incertidumbre (es)
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This work proposes a technique for placing electric vehicle charging stations using a bootstrapping-based probabilistic power flow. The methodology employs maximum likelihood estimation to model uncertainties in EV charging demand and establish robust confidence intervals for key system metrics. This approach was implemented in the Matpower simulation software within the IEEE-14 bus system, modeling the probabilistic load profile of 4500 EVs while considering 4000 realizations to obtain a wide spectrum of operation scenarios. The main results identified bus 9 as the optimal location for EV charging infrastructure, obtaining minimal active power losses (26.5 +/- 0.5 MW) and a maximum efficiency of 92.87 +/- 0.08 %. The strategic placement of charging stations is closely linked to the lowest active power losses, offering optimal efficiency. However, beyond an optimal placement, this paper aims to increase the robustness of modern grids, overcoming drawbacks related to the integration of electromobility infrastructure. The selection of the most representative features, combined with uncertainty analysis, contributes to an improved decision-making, emphasizing the need for supporting sustainable mobility.
Este trabajo propone una técnica para ubicar estaciones de carga de vehículos eléctricos mediante un flujo de potencia probabilístico basado en \textit{bootstrapping}. La metodología emplea la estimación de máxima verosimilitud para modelar las incertidumbres en la demanda de carga de vehículos eléctricos y establecer intervalos de confianza robustos para los principales indicadores del sistema. Este enfoque se implementó en el \textit{software} de simulación Matpower dentro del sistema de 14 nodos del IEEE, modelando el perfil de carga probabilístico de 4500 vehículos eléctricos y considerando 4000 realizaciones para obtener un amplio espectro de escenarios de operación. Los principales resultados identificaron el bus 9 como la ubicación óptima para la infraestructura de carga de vehículos eléctricos, obteniendo pérdidas activas mínimas (26.5 +/- 0.5 MW) y una eficiencia máxima de 92.87 +/- 0.08 %. La ubicación estratégica de las estaciones de carga está estrechamente vinculada con las menores pérdidas de potencia activa, ofreciendo una eficiencia óptima. No obstante, más allá de una ubicación óptima, este artículo buscó incrementar la robustez de las redes modernas, superando las limitaciones relacionadas con la integración de infraestructura de electromovilidad. La selección de las características más representativas, combinada con el análisis de incertidumbre, contribuye a una mejor toma de decisiones, subrayando la necesidad de apoyar la movilidad sostenible.
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Copyright (c) 2025 Harrynson Ramírez Murillo, Ricardo Rincon, Fabián Salazar, Martha Patricia Camargo, Natalia Rojas-Medina, Camilo Leal-Rincón
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