Plug-in electric vehicles (PEVs) represent a substantial opportunity for governments to reduce emissions of both air pollutants and greenhouse gases. The Government of India has set a goal of deploying 6-7 million hybrid and PEVs on Indian roads by the year 2020. The uptake of PEVs will depend on, among other factors like high cost, how effectively range anxiety is mitigated through the deployment of adequate electric vehicle charging stations (EVCS) throughout a region. The Indian Government therefore views EVCS deployment as a central part of their electric mobility mission. The plug-in electric vehicle infrastructure (PEVI) model - an agent-based simulation modeling platform - was used to explore the cost-effective siting of EVCS throughout the National Capital Territory (NCT) of Delhi, India. At 1% penetration in the passenger car fleet, or ∼10 000 battery electric vehicles (BEVs), charging services can be provided to drivers for an investment of $4.4 M (or $440/BEV) by siting 2764 chargers throughout the NCT of Delhi with an emphasis on the more densely populated and frequented regions of the city. The majority of chargers sited by this analysis were low power, Level 1 chargers, which have the added benefit of being simpler to deploy than higher power alternatives. The amount of public infrastructure needed depends on the access that drivers have to EVCS at home, with 83% more charging capacity required to provide the same level of service to a population of drivers without home chargers compared to a scenario with home chargers. Results also depend on the battery capacity of the BEVs adopted, with approximately 60% more charging capacity needed to achieve the same level of service when vehicles are assumed to have 57 km versus 96 km of range.

In India, there is growing interest among policymakers, planners, and regulators for aggressive electrification of passenger vehicles. For example, Piyush Goyal, the Minister of State for India’s Ministry of Coal, Power, New and Renewable Energy, announced an aspirational goal of converting all vehicle sales in India to battery electric vehicles (BEVs) by 2030 (Economic Times, 2016). In 2012, India has already announced the National Mission on Electric Mobility (NMEM) sets a countrywide goal of deploying 6 to 7 million hybrid and electric vehicles (EVs) by 2020 (DHI, 2012). A major policy motivation for transport electrification is to reduce India’s oil import dependency. The objective of this paper is to assess the effect of full electrification of vehicle sales in India by 2030 on the key stakeholders such as BEV owners, electric utilities, and the government. Specifically, we attempt to answer the following questions: (a) How does the total vehicle ownership cost of BEVs compare with the conventional vehicles? (b) What is the additional load due BEV charging? (c) What is the impact on the power sector investments, costs, and utility revenue? (d) How can smart BEV charging help renewable energy grid integration? (e) What is the impact on the crude oil imports? (f) What is the impact on the greenhouse gas (GHG) emissions?

Widespread adoption of plug-in electric vehicles (PEVs) and renewable energy (RE) can help to jointly decarbonize the transportation and electricity sectors. Previous studies indicate strategies to manage PEV charging facilitate integration of RE into electricity grids, but the value of such strategies at scale is unclear because electricity markets and PEV charging have been inadequately represented together. This analysis focuses on the state of California in 2025, and improves on prior work by linking high-resolution mobility and grid dispatch models to quantify the value of managed charging under a 50% RE grid and PEV adoption scenarios up to California's 5 million vehicle target. Even after accounting for practical charging and grid constraints, 0.95 to 5 million “smart” charging PEVs avoid $120 to $690 million in California grid operating costs annually (up to 10% of total costs) and reduce RE curtailment up to 40% relative to unmanaged PEVs. Overnight time-of-use (TOU) charging provides similar cost savings but increases curtailment. Both of these managed strategies defer system infrastructure expansion at the 5 million PEV deployment. The results suggest residential smart charging complemented by TOU tariffs with added daytime periods are policies with most potential to advance California's dual PEV and RE goals.

As the transportation sector undergoes three major transformations—electrification, shared/on-demand mobility, and automation—there are new challenges to analyzing the impacts of these trends on both the transportation system and the power sector. Most models that analyze the requirements of fleets of shared autonomous electric vehicles (SAEVs) operate at the scale of an urban region, or smaller. A quadratically constrained, quadratic programming problem is formulated, designed to model the requirements of SAEVs at a national scale. The size of the SAEV fleet, the necessary charging infrastructure, the fleet charging schedule, and the dispatch required to serve demand for trips in a region are treated as decision variables. By minimizing both the amortized cost of the fleet and chargers as well as the operational costs of charging, it is possible to explore the coupled interactions between system design and operation. To apply the model at a national scale, key complications about fleet operations are simplified; but a detailed agent-based regional simulation model to parameterize those simplifications is leveraged. Preliminary results are presented, finding that all mobility in the United States (U.S.) currently served by 276 million personally owned vehicles could be served by 12.5 million SAEVs at a cost of $ 0.27/vehicle-mile or $ 0.18/passenger-mile. The energy requirements for this fleet would be 1142 GWh/day (8.5% of 2017 U.S. electricity demand) and the peak charging load 76.7 GW (11% of U.S. power peak). Several model sensitivities are explored, and it is found that sharing is a key factor in the analysis.