Positioning of Charger Stations for a fullyelectrified Swedish Transport System - Consequence on the Power System

Abstract

Abstract In pursuing a fully electrified transportation system, with the promotion of electric vehicles, existing traffic prediction models are showing weakness. The inadequate layout of charging facilities fails to meet demands, and the growing energy consumption has severely impacted the power system. This study proposes an integrated energy optimization and charging infrastructure framework (EOIET), addressing the challenges during the electrification process. In terms of energy consumption prediction, the study established urban traffic flow nergy consumption estimation models UCEM and UTEM based on macro traffic data, and constructed a long-distance traffic flow energy consumption prediction model through the LSTM-GRU model with Rayleigh distribution assumption. For the optimization of charger station layout, the study comprehensively considered the decentralized nature of the urban layout and applied a simulated annealing model and topology optimization techniques to determine the optimal location of longdistance charging stations. The power system impact assessment uses Monte Carlo simulation combined with deep learning algorithms to optimize the power transmission strategy and evaluate the grid stability and load balance. The study verified the model based on Sweden’s historical transportation energy consumption and relevant EU indicators, which confirmed the reliability of the model. It provided a reference for infrastructure development and grid optimization in the context of electrified transportation.