Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

Abstract

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8 kW or lower for Sweden and at 2.1 kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation.

Deep learning time pattern attention mechanism-based short-term load forecasting method

“…However, the extensive integration of renewable energy sources (Yang et al, 2021), the widespread adoption of electric vehicles (Hartvigsson et al, 2021;Yang et al, 2022), the largescale deployment of energy storage systems (Zhang et al, 2021), and emerging cyber threats (Ruan et al, 2023a) have introduced greater uncertainty and disturbances in short-term load forecasting (Wang et al, 2019b). To address the limitations of existing models in capturing these dynamic changes, this paper proposes a deep learning-based approach that incorporates a time pattern attention (TPA) mechanism to construct a highly accurate load forecasting model.…”