Graph Neural Networks and Bi-Level Optimization for Equitable Electric Vehicle Charging Infrastructure Planning.
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| Title: | Graph Neural Networks and Bi-Level Optimization for Equitable Electric Vehicle Charging Infrastructure Planning. |
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| Authors: | Silva, Javier Alexander Guerrero1 (AUTHOR), Gelvez, Jorge Ivan Romero1,2 (AUTHOR) jorgei.romerog@utadeo.edu.co, Zapata, Sebastian1,2 (AUTHOR) |
| Source: | Energies (19961073). Apr2026, Vol. 19 Issue 8, p1981. 28p. |
| Subject Terms: | *Graph neural networks, *Bilevel programming, *Electric vehicle charging stations, *Demand forecasting, *Spatiotemporal processes, *Urban planning, *Infrastructure (Economics) |
| Geographic Terms: | Colombia |
| Abstract: | Equity-aware electric vehicle (EV) charging planning remains difficult in data-constrained cities. In this work, an integrated framework was developed by combining spatiotemporal graph neural networks (ST-GNNs), EVI-Pro Lite demand estimation, and lexicographic bi-level optimization, and was applied to Bogotá, Colombia (8.3 million inhabitants). Household travel survey data (12,500 households across 142 zones) were used to estimate zone-level priority scores and venue-specific temporal weights. EVI-Pro Lite simulations projected a 2025 requirement of 10,870 charging ports (7352 residential, 2739 workplace, and 779 public). In the allocation stage, Level 1 preserved priority-proportional targets, while Level 2 minimized inter-zonal inequality in Hansen accessibility subject to near-optimal Level-1 compliance. The final allocation retained strong priority alignment in installed ports (Spearman ρ = 0.799 , p < 10 − 31 ), while the priority–accessibility association was lower (Spearman ρ = 0.320 , p = 1.04 × 10 − 4 ), consistent with second-stage equity redistribution. Equity outcomes also improved (Hansen Gini = 0.433; bottom-50% Lorenz share = 0.204). The mean Hansen accessibility reached 296.630 (standard deviation 248.099; minimum 1.126). These findings indicate that reproducible, equity-oriented EV infrastructure plans can be produced in cities where revealed charging microdata are limited. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Equity-aware electric vehicle (EV) charging planning remains difficult in data-constrained cities. In this work, an integrated framework was developed by combining spatiotemporal graph neural networks (ST-GNNs), EVI-Pro Lite demand estimation, and lexicographic bi-level optimization, and was applied to Bogotá, Colombia (8.3 million inhabitants). Household travel survey data (12,500 households across 142 zones) were used to estimate zone-level priority scores and venue-specific temporal weights. EVI-Pro Lite simulations projected a 2025 requirement of 10,870 charging ports (7352 residential, 2739 workplace, and 779 public). In the allocation stage, Level 1 preserved priority-proportional targets, while Level 2 minimized inter-zonal inequality in Hansen accessibility subject to near-optimal Level-1 compliance. The final allocation retained strong priority alignment in installed ports (Spearman ρ = 0.799 , p < 10 − 31 ), while the priority–accessibility association was lower (Spearman ρ = 0.320 , p = 1.04 × 10 − 4 ), consistent with second-stage equity redistribution. Equity outcomes also improved (Hansen Gini = 0.433; bottom-50% Lorenz share = 0.204). The mean Hansen accessibility reached 296.630 (standard deviation 248.099; minimum 1.126). These findings indicate that reproducible, equity-oriented EV infrastructure plans can be produced in cities where revealed charging microdata are limited. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19081981 |
