Mitigating Urban Grid Stress via Grid-Aware Deployment of PV-BESS Charging Hubs Using a Spatial MCTS Approach Applied to Bogotá

Abstract

This research presents an integrated techno-energetic framework to decouple electric vehicle (EV) fleet growth from urban grid instability, using Bogotá, Colombia, as a case study. As emerging megacities confront rising charging demands, conventional reactive grid reinforcements are becoming technically and economically constrained. To address this, we develop a multi-layered optimization model that transforms urban Voronoi polygons into operational energy catchment units. Utilizing a Monte Carlo Tree Search (MCTS) algorithm, the framework determines infrastructure deployment sequences under two operational thresholds: a target high-resilience EV-to-charger ratio and a conservative scenario. Each localized node is technically dimensioned as a Representative Charging Station (RCS) equipped with a photovoltaic array and a Battery Energy Storage System (BESS). The results reveal spatial heterogeneity; high-density polygons in specific commercial and residential districts exhibit elevated infrastructure utilization alongside a stable solar resource baseline. Furthermore, the model demonstrates that this distributed architecture alleviates transformer thermal stress during the peak nocturnal charging period, mitigating localized overload risks and supporting distribution grid operational reliability. This study provides a scalable decision-making tool that bridges geospatial urban planning with renewable energy engineering to support the transition of constrained electrical networks.