Consumer reality check: Satisfaction among current EV owners is high, but charging convenience and policy uncertainty can deter many would-be buyers. Our team (with Matteo at the helm) outlines what evidence says could move the needle.
Read more here: rdcu.be/eKc4C
Read more here: rdcu.be/eKc4C
🚨New paper out in Nature Reviews Clean Technology🚨
EVs are now 22% of global car sales, 10% in the U.S., and rising. Charging, grid integration, and supply chains are catching up fast.
EVs are now 22% of global car sales, 10% in the U.S., and rising. Charging, grid integration, and supply chains are catching up fast.
The full article is worth a read: www.sciencedirect.com/science/arti...
Factors influencing recent trends in retail electricity prices in the United States
This study analyzes the primary drivers of recent state-level trends in U.S. retail electricity prices. We summarize pricing trends, explore descripti…
www.sciencedirect.com
Narrative violation: Load growth has tended to depress retail electricity prices in recent years, according to a new LBNL study.
A key caveat is that this relationship need not always exist. Higher growth can increase prices if new supply/delivery infrastructure is constrained and costly.
A key caveat is that this relationship need not always exist. Higher growth can increase prices if new supply/delivery infrastructure is constrained and costly.
by John Bistline — Reposted by Stephen D. Murphy
But the analysis also suggests that the 25% of recent solar and wind growth that contributed to incremental state RPS often did increase prices in addition to NEM solar.
Check out the excellent paper in The Electricity Journal: www.sciencedirect.com/science/arti...
Check out the excellent paper in The Electricity Journal: www.sciencedirect.com/science/arti...
New LBNL analysis finds that market-driven wind and solar (roughly 75% of additions) have not contributed to retail price increases.
GHG Protocol revisions (especially Scope 2) are where this bites. With Asa Watten (@asawatten.bsky.social), I've got new work on how these dynamics could change rooftop PV's emissions impact. More soon.
I mostly agree. If the SRMER is the right answer to your question, then you're probably asking the wrong question.
Persistence is an additional important dimension here, probably more so than the size of the load. Persistent interventions would likely impact the build margin.
Yes, I should have clarified that by "ops" I had in mind operational decisions for individual end-use loads at a small scale like a household deciding when to charge their EV, not aggregator-level dispatch.
What should dashboards default to? LRMER for planning; SRMER for ops. Change my mind 👇 #EnergyTwitter #EVs #LCA #ModelingMonday @emildimanchev.bsky.social @jessedjenkins.com 12/
If you model: label your metric (AER, SRMER, LRMER), your counterfactual, and what's endogenous. Your readers will thank you. 11/
For grid nerds: beware marginal vs. super-marginal conflation, short-run vs. long-run, and the period over which change occurs. These drive wildly different answers. We wrote about it a couple years back: www.pnas.org/doi/abs/10.1... 10/
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
www.pnas.org
Favorite reads: the ERL paper, plus new work showing EV adoption induces clean capacity builds (www.pnas.org/doi/10.1073/...), and that V2G can even push net emissions negative in some cases (pubs.acs.org/doi/full/10....). 9/
tl;dr for comms: When a chart claims "EV charging emits X kgCO1/kWh," ask: Attributional or consequential? Short-run or long-run? Are new builds endogenous? If not, treat with caution. 8/
tl;dr for practitioners:
• Use consequential with long-run capacity expansion to assess policy/scale-up.
• Use attributional if you only need today's footprint.
• Be explicit about boundaries, time horizon, and what's endogenous. 7/
• Use consequential with long-run capacity expansion to assess policy/scale-up.
• Use attributional if you only need today's footprint.
• Be explicit about boundaries, time horizon, and what's endogenous. 7/
Flexible charging (and V2G) matters: uncontrolled evening charging can lean on gas/coal; controlled/V2G boosts the business case for new wind/solar/storage, so total system emissions can fall even as EV load rises. 6/
It even finds that using short-run marginal emissions as a control signal can backfire, worse than price-based signals, because it ignores how charging affects investment: iopscience.iop.org/article/10.1... 5/
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by John Bistline — Reposted by Christian Odendahl
The excellent new ERL paper by Bhandarkar, Luo, Dimanchev, & Jenkins shows that when you let the grid build (wind/solar/storage/gas) in response to EVs, simple averages or short-run marginals can mislead, often understating EV climate benefits. 4/
Average Emission Rates (AER) ≠ Marginal Emission Rates. And short-run marginal (dispatch only) ≠ long-run marginal (dispatch + new builds). Don't extrapolate tiny perturbations for larger adoption. 3/
Two LCAs, two questions:
• Attributional: "What's the average footprint to charge on today's grid?"
• Consequential: "How do emissions change because EV adoption changes the grid?"
For power systems, the consequential question often matters more for planning. 2/
• Attributional: "What's the average footprint to charge on today's grid?"
• Consequential: "How do emissions change because EV adoption changes the grid?"
For power systems, the consequential question often matters more for planning. 2/
by John Bistline — Reposted by Dominik Wiedenhofer
Modeling Monday: How clean are EVs, really?
Short answer: it depends on the question. Attributional vs. consequential LCA, average vs. marginal, short- vs. long-run... the metric can flip the answer. 1/🧵
Short answer: it depends on the question. Attributional vs. consequential LCA, average vs. marginal, short- vs. long-run... the metric can flip the answer. 1/🧵
Big move in long-duration energy storage: SRP is deploying a 5 MW / 50 MWh iron flow battery with ESS. A test case for iron + salt + water storage.
Integrated resource planner, LCOE spreadsheet guy, vibe-coding capacity expansion modeler
Context: forecasts were collected in mid-2022 with outcomes assessed by mid-2025. Here's the report: forecastingresearch.org/near-term-xp...
It'd be good to see details on how "resolved" values were set. H2 costs vary with local renewables, utilization, subsidy eligibility, etc.
It'd be good to see details on how "resolved" values were set. H2 costs vary with local renewables, utilization, subsidy eligibility, etc.
Assessing Near-Term Accuracy in the Existential Risk Persuasion Tournament — Forecasting Research Institute
forecastingresearch.org
Climate-tech forecasting check:
🟢 Green H2 in 2024 -- forecasts: $4.50/kg ("superforecasters"), $3.50/kg (experts). Actual: ~$7.50/kg.
🔵 DAC in 2024 -- forecasts: 0.32-0.60 MtCO2/yr. Actual: ~0.01.
Prediction is hard, especially about the future.
🟢 Green H2 in 2024 -- forecasts: $4.50/kg ("superforecasters"), $3.50/kg (experts). Actual: ~$7.50/kg.
🔵 DAC in 2024 -- forecasts: 0.32-0.60 MtCO2/yr. Actual: ~0.01.
Prediction is hard, especially about the future.
One Battle After Another in 70mm. The only PTA that’s ever matched it for me was The Master premiere at The Castro, where Maya Rudolph was casually working the poster table. Is there a more iconic duo?
I'm pleased to share comments on the draft DOE Climate Working Group report that I coauthored with colleagues: www.epri.com/research/pro...
The energy use modeling detail is impressive, though the magnitude of impact is relatively small ($25 average with some locations decreasing and others increasing).
This is a terrific synthesis of the literature and brings together many moving parts. It's great to see transparent assumptions and range of values that reflect uncertainty in the literature. Check out the full paper here: www.brookings.edu/articles/who...
Who bears the burden of climate inaction? | Brookings
www.brookings.edu