Sustainable Transportation Energy Pathways (STEPS)
Available online at https://trid.trb.org/view.aspx?id=1242397
Sohnen, Julia, Yueyue Fan, Joan M. Ogden, Christopher Yang (2013) Accommodating Electrical Vehicle Charging in California's Power Sector: Regional Impacts on Greenhouse Gas Emissions. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-13-57
This paper explores the implications of the increased adoption of plug-in electric vehicles (PEVs) in California due to the state’s greenhouse gas and energy policies. The well-to-wheels emissions associated with driving an electric vehicle depend on the resource mix of the electricity grid used to charge the battery. The authors establish a systems approach that can be used to evaluate the impact of growing electric vehicle demand on existing power grid infrastructure system and energy resources. They present a new least-cost dispatch model for the California electricity grid consisting of interconnected sub-regions that represent the five largest state utilities. This model considers spatiality and temporal dynamics of energy demand and supply when determining the regional impacts of additional charging profiles on the current electricity network. Model simulation runs for one year were benchmarked against historical data and found to match actual generation within 5%. Sensitivity analyses show seasonal variation in response to uncertainty of demand, a result that has implications for future electric vehicle charging strategies. In minimizing total system cost, the model will choose to dispatch the lowest-cost resource to meet additional vehicle demand, regardless of location, as long as transmission capacity constraints are met. PEV charging location has an effect on the utility region from which additional generation originates. However, scenario testing confirms that location of additional PEVs does not have an effect of overall emissions and cost impacts to the grid, provided there is available transmission capacity for the lowest-cost resource to be utilized.