EV batteries alone could support the grid in the short term as the world transitions to renewables, according to new research published yesterday. Contact online >>
EV batteries alone could support the grid in the short term as the world transitions to renewables, according to new research published yesterday.
The study, titled "Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030," was published in Nature Communications.
Researchers "quantify the global EV battery capacity available for grid storage using an integrated model incorporating future EV battery deployment, battery degradation, and market participation." They look at the main EV battery markets of China, the European Union, and the US, and what it calls the "Rest of the World region."
They write that EV batteries can be used in both vehicle-to-grid (V2G) capacity and after the end of vehicle life, when they are removed and used in stationary storage.
The researchers, which state that their estimates are conservative, assert that low participation rates of just 12-43% are needed to provide short-term grid storage demand globally, and that demand could be met as early as 2030 across most regions. And beyond 2030:
We estimate a total technical capacity of 32-62 TWh by 2050. This is significantly higher than the 3.4–19.2 TWh required by 2050 in IRENA and Storage lab scenarios.
This is an extremely interesting study, and initially it''s kind of exciting. But ultimately I tend to read it as theoretical because I don''t know if people would buy into this. There are many factors that need to be taken into account. What about vehicle depreciation? Do people want to share their EV power with the grid? What would incentivize people to do so?
Also, the EV industry hasn''t truly launched V2G (although to be fair, Proterra has launched it in its school buses, as per above, and V2G in school buses and other commercial fleets could be great).
I live in Vermont, and I am signed up for Green Mountain Power''s Tesla Powerwall program. I am going to let GMP install two Powerwalls in my home, and they can use them to balance the grid. If bad weather is anticipated, then they won''t draw from the Powerwalls so I have backup power. And I''m going to get solar and hook that up to the Powerwalls as well. At $55 a month, it''s a lot cheaper to get backup power than actually buying Powerwalls.
I made this argument to an EV skeptic not long ago. That overall capacity is not the problem, it''s peak demand. And if EVs are all equipped with V2G you end up having an enormous distributed grid scale battery to help even out that dreaded peak of the demand curve. We really need Tesla to get on board with V2G for it to be successful though.
But if GMP wanted to use my EVs to stabilize its grid, it would have to provide really good incentives. I like to be in control of my car, and know how much charge it has in it, and I think other drivers will feel the same, and that plays a role in why V2G hasn''t well and truly taken off.
What do you think about this study? Let us know in the comments below.Image: Courtesy of Proterra
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Michelle Lewis is a writer and editor on Electrek and an editor on DroneDJ, 9to5Mac, and 9to5Google. She lives in White River Junction, Vermont. She has previously worked for Fast Company, the Guardian, News Deeply, Time, and others. Message Michelle on Twitter or at michelle@9to5mac . Check out her personal blog.
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Received 2022 Apr 7; Accepted 2022 Nov 30; Collection date 2023.
See legend for use of colours. Square, white boxes indicate model outputs. Please see details for the model framework in the methods section. USDOE US Department of Energy, FASTSim Future Automotive Systems Technology Simulator, NREL National Renewable Energy Laboratory, IEA International Energy Agency, SoH State of Health.
a STEP-NCX scenario. b SD-NCX scenario. c STEP-LFP scenario. d SD-LFP scenario (see details in Supplementary Table 1). IRENA = International Renewable Energy Agency.
a Technical vehicle-to-grid capacity. Hatched bars indicate the capacity limits due to key factors and blue bars the technical vehicle-to-grid capacity. b Real-world vehicle-to-grid capacity as a function of participation rates. Results are shown for the STEP-NCX and the SD-NCX scenarios with a comparison to the range of storage demand computed by IRENA and Storage Lab models in 2050 (orange shading). Please see Supplementary Fig. 16 for global real-world vehicle-to-grid capacity under STEP-LFP and the SD-LFP scenarios and Supplementary Figs. 17–20 for regional real-world vehicle-to-grid capacity.
a Average annual additions and cumulative technical capacity of second-use batteries in 2050. Here capacity refers to the technically available capacity considering battery degradation but without considering battery second-use utilisation rate. b Impacts of second-use utilisation rate on cumulative actual second-use capacity and a comparison to storage demand in 2050 (orange shading). See Supplementary Figs. 22–25 for regional actual second-use capacity.
Blue, white, and red colors depict minimum, average, and maximum values. See Supplementary Figs. 26–28 for other scenarios.
where q is the relative battery degradation, qLoss, Calendar is the relative calendar life degradation, qLoss, Cycling is the relative cycling life degradation, T is temperature, t is time (unit: days), EFC is equivalent full cycles. Note R is the universal gas constant (8.3144598 J mol-1 K-1), Tref is the reference temperature (298.15 K), F is Faraday constant (96485 C mol-1), kCal (unit: days0.5), Ea (unit: J mol-1 K-1), and α (no unit) are fitting parameters for calendar life degradation, and kCyc (unit: EFC-1). A, B, C, D, G, and H (no units) are fitting parameters for cycling life degradation. The value of the anode-to-reference potential, Ua (unit: V), is calculated from the storage SoC using the Eqs. (4) and (5)72.
The degradation change ∆q during any given timestep Δt is then calculated by the following equation:
Description of Additional Supplementary Files
C.X. designed and conducted the research with valuable inputs from B.S., P.B., A.T., M. H., as well as P. G. and K. S. C.X. wrote the manuscript with the help of P.B., A.T., B.S., P. G., and other authors. P.B., A.T., and B.S. contribute significantly to the structure of research results and scientific writing of this research. P. G. and K. S. developed the battery degradation model, and further provided technical inputs on how to integrate the degradation model into the analysis of the results.
The datasets, including EV fleet size by country, EV sales share by cities, and battery chemistry share, are all deposited in an Excel file (10.6084/m9 gshare.21542472.v1). These raw data are used for the dynamic battery stock model for quantifying future battery flows. Please see the dynamic battery stock model from this link (10.6084/m9 gshare.13042001.v4). City ambient temperature and its effects on battery degradation are also deposited in the Excel file, while the code for estimating battery degradation, which is under privacy and license, is available upon reasonable request.
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