Influencing factors for light duty electric vehicle adoption and anticipated impacts on the Electric Reliability Council of Texas
Electric vehicles (EVs) are becoming a more prominent portion of Texas’s light duty vehicle (LDV) fleet as they become more attractive to consumers and as relevant governing bodies work to incentivize further adoption rates in an effort to reduce emissions within the transportation industry. Meanwhile, the Electric Reliability Council of Texas (ERCOT), the electric grid that services about 90% of the state’s residents, has been seeing increases in power demand. This has been due to factors such as a growing population, increased air conditioning use, and pushes for electrification across other industries, all factors that are expected to continue contributing to power demand increases within the foreseeable future. As more vehicles in the state are electrified, they will add further power demand increases on top of the existing contributing factors. This work focuses on evaluating different EV adoption, charging management, and policy scenarios in order to then evaluate how they may be expected to impact ERCOT, particularly regarding peak demand increase within two time horizons: one into 2030 and one into 2050. Peak demand is an important consideration because as it increases, it presents challenges for maintaining the electrical grid’s reliability when it exceeds generation capacity. After constructing and refining models which predict EV presence at the county level based on socio-economic and infrastructure related feature variables, the anticipated impacts of a growing EV fleet are quantified using historical data from ERCOT, planned installations and reserve margins, EV charging patterns, and travel patterns. Additionally, this work includes results from a collaboration which culminated in applying relevant EV fleet growth predictions to a DC-OPF simulation for Austin Energy, serving as a case study for the future of EV fleet impacts on relatively small-scale utilities. The results of this study showcase the fact that it is possible to accurately predict EV presence at the county level with 6 publicly available feature variables. In examining adoption pathways, it importantly finds that current incentives will very likely be insufficient for the achievement of the Biden Administration’s 50% market share goal by 2030. Should this market share goal come to fruition, however, it is expected to be manageable at the state level and within the Austin Energy case study regarding electricity supply in 2030. Sustained growth from this scenario, however, will necessitate ambitious charging management strategies in order to limit the potentially heavy impact of a growing EV fleet on peak demand looking forward into and beyond 2050.