Developing an Agent-based Integrated Framework for Investigating the Potential Expansion and Impact of the Electric Vehicle Market: Test Cases in Two Chinese Cities
Initiatives to electrify urban transport promote the purchase and usage of Electric Vehicles (EVs) and have great potential to mitigate the pressing challenges of climate change, energy scarcity and local air quality. Transportation electrification is a huge innovation and could directly and indirectly impact and/or be impacted by several urban sub-systems. This project develops an agent-based integrated framework for investigating how the EV market expands in the context of urban evolution at the micro scale, and assessing the potential impacts of the market expansion on the environment, power grid system and transport facilities, considering the interactions and dynamics found there. The framework may be useful for stakeholders, such as governments, as an aid to decision making. The integrated framework, SelfSim-EV, is updated from a Land Use and Transport (L-T) model, SelfSim, by incorporating several EV-related modules, including an EV market model, an activity-based travel demand model, a transport facility development model and a social network model. In order to somewhat present the behavioural rules of some key agents in SelfSim-EV, two questionnaire surveys on individual EV travel and purchase behaviours were delivered to members of the general public in Beijing, and semi-structured interviews with EV stakeholders were also carried out. The collected data was analysed using discrete choice models and Geographic Information System (GIS). SelfSim-EV was fully tested within two test cases in China, Baoding (a medium-sized city) and Beijing (the capital of China): first, parameter Sensitivity Analyses (SAs) were carried out to test SelfSim-EV within the test case of Baoding from both global and local perspectives, investigating the relationships between the 127 model parameters and 78 outputs of interest; Then SelfSim-EV was further tested within the case study of Beijing, involving in model initialisation, calibration, validation and prediction. Specifically, the SA results were used to calibrate SelfSim-EV in Beijing from 2011 to 2014 by matching various observed and simulated data types at both city- and district-levels, and the calibrated SelfSim-EV model was further validated against historical data in 2015. Then the future of EVs in Beijing was explored within a Reference Scenario (RefSc) from 2016 to 2020. Due to the model uncertainty in future events, several “what-if” scenarios were set up with the SelfSim-EV Beijing model to explore how three typical types of driving factors, namely policy, technology and infrastructure, may influence the EV market expansion at both aggregate and disaggregate levels. The results indicate that policies tend to be more influential than technologies and infrastructures in terms of EV penetration rates. RefSc eventually shows some improvement in total emissions, however, boosting sales of EVs (particularly PHEVs) in the wrong way could have negative impacts. Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system in RefSc, and it does not increase linearly as the EV sales rise. Slow charging posts appear to be necessary, whereas fast charging facilities seem to contribute slightly to the EV market expansion and thus may be not necessary at the current stage.