National Economic Value Assessment of Plug-in Electric Vehicles: Volume I

Abstract

Funding for this report came from the U.S. Department of Energy's Vehicle Technologies Office. This work has benefited from guidance and input provided through discussions and reviews with an advisory committee established through the U.S. Department of Energy and the Edison Electric Institute (EEI), which emerged as a result of the recent Memorandum of Understanding between the two organizations. Members of the EEI advisory committee include

Electrification Futures Study: End-Use Electric Technology Cost and Performance Projections through 2050

“…The per-vehicle EVSE costs used for this analysis are displayed in Table 10. Costs for Level 1 and Level 2 charging are based on Melaina et al (2016), and costs for DCFC equipment are taken from Francfort et al (2017). Costs are calculated based on a 10-year lifetime for EVSE (DOE 2015c); the actual and future lifetime is uncertain and limited literature is available.…”

“…A recent analysis fromWood et al (2017) explores non-residential charging needs for LDVs. We use estimates fromMelaina et al (2016) for EFS because it includes both residential and non-residential charging requirements. 36 See Appendix A for additional detail on the DCFC ratio calculation.…”

Electrification and Decarbonization: Exploring U.S. Energy Use and Greenhouse Gas Emissions in Scenarios with Widespread Electrification and Power Sector Decarbonization

“…Like the default version of VISION, our version of the model is resolved at the aggregate annual and national levels; however, our power sector analysis using ReEDS requires temporal (seasonal and diurnal) and regional disaggregation. To perform the disaggregation, we rely on methodologies developed in the National Economic Value Assessment (NEVA) of Electric Vehicles study (Melaina et al 2016). The goal of the NEVA study is to assess the potential benefits and costs for the large-scale, nationwide implementation of EVs and infrastructure to support those vehicles out to 2035.…”

Enabling immersive engagement in energy system models with deep learning

“…The outputs from planning model include cumulative capacity of fossil, nuclear, renewable, and storage resource deployments, the transmission expansions, the generator dispatch and fuel needed to satisfy regional demand requirements and to maintain grid system adequacy, total system cost, electricity prices, emissions, and water consumption. Notable studies that have made significant use of ReEDS include the Wind Vision Study [39], a prospective analysis of state Renewable Portfolio Standards impacts [21], and the National Plug-in Electric Vehicles impacts assessment study [24]. In the work presented here, ReEDS model simulations were performed for 18 515 scenarios using NREL's HPC system.…”