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satisfied, which is considered the marginal generation. Applying this approach and adopting assumptions stated in Kintner-Meyer et al. (2007), the marginal generation is provided by two power plant types: 1) coal steam power plants and 2) natural gas combined-cycle power plants. The marginal generation by power plant type is input into the GREET model (Wang 2001). The model output provides the improvement in energy and emissions from a well-to-wheel perspective. The analysis is performed for the 12 modified North American Reliability Corporation (NERC) sub-regions to reflect the varying electric generation mix for charging the batteries. The results in Table F.1 reflect the improvements expressed as a ratio relating to energy requirements and emissions of electric to conventional gasoline vehicles. The reference (conventional gasoline) vehicle represents the fuel efficiency of today's average mid-size car (Wang 2001). The results indicate that if you replace a conventional vehicle with an EV, then the energy improvement per VMT improves by 30% (the energy ratio is 0.70) as a weighted national average. Similarly, the CO2 emission improves by 27%. F.3.4 Unmanaged Versus “Smart” Charging The estimates of vehicles that can be supported by the regional grid are a technical potential that requires “perfect valley-filling” load-management strategies such that the nocturnal valley is filled out without setting a new system peak load. To achieve “perfect valley-filling” requires smart grid technologies that carefully sequence the charging of PHEVs and EVs. The question is what the maximum percentage of the LDV fleet in the absence of load management would be if, for instance, owners of PHEVs would plug in their vehicles at home or at work at the time they arrive. We analyzed the 2001 National Household Travel Survey data to determine the arrival and departure times for travel to and from home and work, and the miles traveled between those destinations (NHTS 2001). We assumed that the PHEV will be plugged into a power supply and charge until either the PHEV is used for the next trip or when the battery is fully charged. Assuming a 120V/12A charger, with battery sizes varying corresponding to the vehicle class composition with an all-electric range of 33 miles, we estimated the charging profiles and the new resulting system load for the technical potential of the regional PHEV fleet. Figure F.1 below shows the results of both the “perfect valley-filling” of the entire regional fleet, as well as the new system load for the regional PHEV fleet if plugged in at home immediately after arriving, based on the NHTS statistics when people arrive home. Unlike in the “perfect valley-filling” solution, which avoided a new system peak load, permitting unmanaged PHEV charging may set some new system peak loads in several regions. Constraining the system load such that it never exceeds the peak load demand on peak load days reduced the technical potential of the number of vehicles that could be supported by the grid from 73% to 64% of the entire U.S. LDV fleet (Kintner-Meyer et al. 2008). The reductions in electricity and CO2 emissions calculated in Attachment 2 are based on this and additional information from the literature review, and the author’s experience. It is estimated that managed charging provides for a direct reduction of 3% in energy consumption by enabling 9% (18 M) more EVs to be entered into the fleet with a 30% reduction in energy consumed per VMT. No indirect reductions in electricity or capacity are expected. F.6PDF Image | The Smart Grid: An Estimation of the Energy and CO2 Benefits
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