High-resolution simulation and prediction of urban private vehicles energy consumption system: Agent-based modelling

• A high-resolution multi-agent model of private vehicles commuting is established. • The instantaneous energy consumption of various automobile types is calculated. • The difference of travel behavior of various types of vehicles is considered. • The maximum grid load related to EVs in Beijing in the future is predicted. High-precision energy demand forecasting can help cope with the uncertainties of future vehicle electrification. An agent-based model is employed to simulate and forecast the minute-by-minute energy consumption in Beijing. Based on the results, recommendations for power grid enhancements are formulated to address the uncertainties associated with future vehicle electrification. Firstly, an agent-based simulation model is constructed to simulate the behavior of conventional gasoline vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, and pure electric vehicles in Beijing within the same system. Considering the complex spatiotemporal heterogeneity of motor vehicle energy consumption, two scenarios were established: Scenario 1, focusing on spatial heterogeneity, and Scenario 2, emphasizing temporal heterogeneity. In Scenario 1, the total energy consumption and peak instantaneous energy consumption of various vehicle types across Beijing and its 16 districts are analyzed. Then, in Scenario 2, the total and instantaneous energy consumption for Beijing and its 16 districts from 2024 to 2030 is projected. It is projected that by 2030, the number of EVs in Beijing will reach 1,013,900, representing a 1.3-fold increase from the 2024 figure. This growth is expected to lead to a 1.5-fold rise in total automobile electricity consumption. Among Beijing’s 16 districts, Chaoyang has the highest total electricity consumption and the fastest growth rate. Annual electricity consumption is projected to increase from 227.3 million kWh in 2024 to 32.108 billion kWh in 2030. In contrast, Huairou has the lowest energy consumption and the smallest variation range. Additionally, we observed significant imbalances in peak annual growth rates across different regions. Therefore, it is recommended that Haidian and Chaoyang districts should at least double their current capacity to alleviate overall power demand. Furthermore, starting from 2024, apart from Huairou and Pinggu districts, the remaining 14 districts should implement network reinforcement measures, smart grid peak regulation, dynamic charging, and other peak management policies to address the anticipated future peak growth. The findings are anticipated to serve as a valuable reference for the enhancement of urban power grid infrastructure in order to meet the future surge in demand for electric vehicles.