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Lithium-ion battery price history and forecast. Source: Bloomberg (2018b).

Lithium-ion battery price history and forecast. Source: Bloomberg (2018b).

Contexts in source publication

Context 1
... costs, the single most expensive component in an EV that accounts for most of the cost difference with conventional combustion vehicles, has been falling from $1,000/kWh in 2010 to ~$200/kWh in 2017, and is projected to fall below $100/kWh in 2025 (Bloomberg, 2018b). See Fig. 3. In 2017, Bloomberg forecast that EVs will reach upfront cost parity with combustion vehicles on an unsubsidized basis starting in 2026, and every year since this crossover point forecast has been moving up in time; its latest 2019 analysis suggests that it will now occur in 2022 (Bullard, 2019). Eleven countries including China, ...
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... initial assumptions were based on those used in Coignard et al. (2018), which assumed 100% home charging, an increasing fraction of workplace charging, and no public charging. Fig. 13 shows the specific assumptions made. These assumptions were used for all MTEP DET runs and the initial set of RIIA runs. ...
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... 30 and 31 show the same simulations for June 2038. For the Base EV penetration scenario (Fig. 30), we find more substantial modifications to the base load curve for both the V1G and V2G optimization schemes, but the optimizations are basically still limited to a single day. The other difference of note is that the negative net load excursions are now deeper, e.g., in the no-EV and uncontrolled charging cases, it dips below -7,000 ...
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... other difference of note is that the negative net load excursions are now deeper, e.g., in the no-EV and uncontrolled charging cases, it dips below -7,000 MW in the early morning of June 7, and makes less pronounced excursions below zero during the evenings of June 6 and 7, as well as momentarily on June 4. By contrast, in the Very High scenario (Fig. 31), while the V1G case (green) appears very similar to that of June 2033 (see Fig. 29) except for deeper filling of valley loads, the V2G case (red) displays a net load curve that is flatter across the entire week than in the June 2033 V2G case. Results for LRZ 7 are shown next. Figs. 32 and 33 show the differences between the Base and ...
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... the gross load. In particular, the V2G case (red) is able to greatly reduce the slopes in the load curves, so much so that the time of day at which the deepest part of the valley occurs is shifted earlier by several hours some days (e.g., June 4-7), and the daily peaks are substantially reduced and altered in shape as well. The Very High scenario (Fig. 33) also shows larger amounts of load shaping than in LRZ 1, resulting in a much smoother profile (ignoring spikes that are modeling artifacts) across the week for both the V1G (green) and V2G (red) cases. The V2G case has also become a multiday ...
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... 34 and 35 show the same simulations for June 2038. For the Base EV penetration scenario (Fig. 34), there is not much difference in the V1G case (green) other than a deeper filling in of valley loads, but the V2G result (red) again results in much smoother net loads than in the LRZ 1 simulations, with the optimization becoming multi-day. Finally, in the Very High scenario (Fig. 35), while the V1G result (green) has significantly ...
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... for June 2038. For the Base EV penetration scenario (Fig. 34), there is not much difference in the V1G case (green) other than a deeper filling in of valley loads, but the V2G result (red) again results in much smoother net loads than in the LRZ 1 simulations, with the optimization becoming multi-day. Finally, in the Very High scenario (Fig. 35), while the V1G result (green) has significantly transformed the shape of the load curve, resulting in much lower peaks and shallower valleys, it is still a single-day optimization. By contrast, the V2G result (red) shows very little day-to-day variation, and gently slopes upward over the course of the week, much more so than in Fig. ...
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... scenario (Fig. 35), while the V1G result (green) has significantly transformed the shape of the load curve, resulting in much lower peaks and shallower valleys, it is still a single-day optimization. By contrast, the V2G result (red) shows very little day-to-day variation, and gently slopes upward over the course of the week, much more so than in Fig. ...

Citations

... Current electric vehicle research considers technology adoption projections and the infrastructure needed to support PEV growth [1-3], and a smaller subset of studies have considered the specific impacts on distribution and transmission electric grids [4][5][6]. There are few studies specific to the Midwestern United States [3], but no existing work for Wisconsin regarding PEV load growth and transmission congestion. ...
Article
Full-text available
This work explores the impact of the rapid growth of plug-in electric vehicles on wholesale electricity pricing. Understanding electric vehicle impacts on the grid is important for the mid- and long-range planning of transmission owners, distribution utilities, and regional system operators. Current research in electric vehicles considers technology adoption projections and the infrastructure needed to support electric vehicle growth. This work considers how projected electric vehicle growth in the State of Wisconsin would impact the transmission congestion and wholesale electricity pricing in the year 2030. We find minimal impacts on electricity prices (<2%) even under rapid growth assumptions, in which EVs comprise 5% of all vehicles in 2030. The increases seen in hourly locational marginal prices (LMPs) due to projected electric vehicle growth are, on average, less than those seen in annual changes of historic electricity prices in Wisconsin. We do find moderate, relative increases in congestion prices (+16–32%), which could provide an opportunity to align electric vehicle charging schedules with times of low transmission congestion.