Modeling of Electric Vehicle Energy Demand: A Big Data Approach to Energy Planning

Iván Sánchez-Loor and Manuel Ayala-Chauvin ()
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Iván Sánchez-Loor: Maestría en Big Data y Ciencia de Datos, Centro de Investigación en Mecatrónica y Sistemas Interactivos (MIST), Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador
Manuel Ayala-Chauvin: Centro de Investigación en Ciencias Humanas y de la Educación (CICHE), Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador

Energies, 2025, vol. 18, issue 20, 1-24

Abstract: The rapid expansion of electric vehicles in high-altitude Andean cities, such as the Metropolitan District of Quito, Ecuador’s capital, presents unique challenges for electrical infrastructure planning, necessitating advanced methodologies that capture behavioral heterogeneity and mass synchronization effects in high-penetration scenarios. This study introduces a hybrid approach that combines agent-based modelling with Monte Carlo simulation and a TimescaleDB architecture project charging demand with quarter-hour resolution through 2040. The model calibration deployed real-world data from 764 charging points collected over 30 months, which generated 2.1 million charging sessions. A dynamic coincidence factor ( F C = 0.222 + 0.036 ∗ e ( − 0.0003 n ) ) was incorporated, resulting in a 52% reduction in demand overestimation compared to traditional models. The results for the 2040 project show a peak demand of 255 MW (95% CI: 240–270 MW) and an annual consumption of 800 GWh. These findings reveal that non-optimized time-of-use tariffs can generate a critical “cliff effect,” increasing peak demand by 32%, whereas smart charging management with randomization reduces it by 18 ± 2.5%. Model validation yields a MAPE of 4.2 ± 0.8% and an RMSE of 12.3 MW. The TimescaleDB architecture demonstrated processing speeds of 2398.7 records/second and achieved 91% data compression. This methodology offers robust tools for urban energy planning and demand-side management policy optimization in high-altitude contexts, with the source code available to ensure reproducibility.

Keywords: electric vehicles; Big Data; ABM; Monte Carlo; TimescaleDB; demand response; dynamic coincidence factor; energy planning; reproducibility; Quito (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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