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arxiv: 2304.07019 · v1 · submitted 2023-04-14 · 💰 econ.GN · q-fin.EC

Low-carbon Lithium Extraction Makes Deep Geothermal Plants Cost-competitive in Energy Systems

Jann Michael Weinand , Ganga Vandenberg , Stanley Risch , Johannes Behrens , Noah Pflugradt , Jochen Lin{\ss}en , Detlef Stolten This is my paper

Pith reviewed 2026-05-24 09:35 UTC · model grok-4.3

classification 💰 econ.GN q-fin.EC
keywords lithium extractiondeep geothermalenergy system optimizationcost competitivenesselectric vehicle batteriesUpper Rhine Grabenrenewable displacement
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The pith

Direct lithium extraction from deep geothermal plants makes them cost-competitive in energy systems even under unfavorable conditions and lets them displace photovoltaics, wind, and storage.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that pairing direct lithium extraction with deep geothermal plants turns the plants into economically viable components of regional energy systems. A sympathetic reader cares because the approach supplies a critical battery material through a low-carbon route while generating electricity. If the models hold, geothermal projects become attractive enough to scale in lithium-bearing regions and reduce the share of other renewables needed to meet demand. The analysis quantifies this for the Upper Rhine Graben, where 10 percent municipal adoption could yield lithium for 1.2 million electric-vehicle battery packs annually.

Core claim

Regional energy-system optimization shows that geothermal plants become cost-competitive when direct lithium extraction is included, even under unfavorable techno-economic assumptions, and that the plants partially displace photovoltaics, wind power, and storage. In the Upper Rhine Graben, 10 percent of municipalities building such plants could supply lithium for roughly 1.2 million electric-vehicle battery packs per year, equal to 70 percent of current annual EU electric-vehicle registrations. The same technology has high mass-application potential in the United States, United Kingdom, France, and Italy.

What carries the argument

Regional energy-system optimization models that add revenue from direct lithium extraction to deep geothermal plants, thereby lowering the net cost of electricity generation.

If this is right

  • Geothermal plants with lithium extraction partially replace photovoltaics, wind, and storage in optimized energy mixes.
  • 10 percent adoption in the Upper Rhine Graben supplies lithium for 1.2 million EV battery packs per year.
  • The approach delivers environmental benefits by avoiding conventional lithium mining impacts.
  • The technology shows high potential for mass application in the United States, United Kingdom, France, and Italy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Revenue from lithium could accelerate permitting and drilling for geothermal wells in other lithium-bearing basins.
  • If lithium prices remain elevated, the same plants might also extract other dissolved minerals to further improve economics.
  • National energy planners could treat geothermal-lithium sites as dual-purpose infrastructure when setting renewable targets.

Load-bearing premise

The capital costs, lithium recovery rates, market prices, and regional resource potentials taken from external literature and fed into the optimization models are accurate and remain stable.

What would settle it

Re-running the same regional optimization with capital costs for geothermal-plus-lithium plants raised by 30 percent or lithium prices lowered by 40 percent would show whether the cost-competitiveness and displacement results disappear.

read the original abstract

Lithium is a critical material for the energy transition, but conventional procurement methods have significant environmental impacts. In this study, we utilize regional energy system optimizations to investigate the techno-economic potential of the low-carbon alternative of direct lithium extraction in deep geothermal plants. We show that geothermal plants will become cost-competitive in conjunction with lithium extraction, even under unfavorable conditions and partially displace photovoltaics, wind power, and storage from energy systems. Our analysis indicates that if 10% of municipalities in the Upper Rhine Graben area in Germany constructed deep geothermal plants, they could provide enough lithium to produce about 1.2 million electric vehicle battery packs per year, equivalent to 70% of today`s annual electric vehicle registrations in the European Union. This approach could offer significant environmental benefits and has high potential for mass application also in other countries, such as the United States, United Kingdom, France, and Italy, highlighting the importance of further research and development of this technology.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper uses regional energy-system optimization models to show that coupling direct lithium extraction (DLE) from geothermal brines with deep geothermal power plants renders the plants cost-competitive even under unfavorable conditions, allowing partial displacement of PV, wind, and storage; it further quantifies that 10% deployment in the Upper Rhine Graben could supply lithium for ~1.2 million EV battery packs annually (70% of current EU registrations) with similar potential in the US, UK, France, and Italy.

Significance. If the central techno-economic result is robust, the work identifies a low-carbon co-production pathway that simultaneously supplies critical lithium and dispatchable power, with quantified supply potential at municipal scale and cross-country applicability; the modeling approach directly links resource extraction parameters to system-level technology choice.

major comments (3)
  1. [Abstract] Abstract and modeling description: the claim that geothermal+DLE plants are selected by the optimizer 'even under unfavorable conditions' and displace PV/wind/storage rests entirely on externally sourced values for DLE capital/O&M costs, lithium recovery fraction, and realized market price; no sensitivity ranges, downside scenarios, or Monte-Carlo propagation of these three free parameters are reported, so the displacement result is not insulated from plausible upward bias in early-stage DLE estimates.
  2. [Methods] Methods/optimization setup: the energy-system model outputs inherit the accuracy of the input techno-economic database without reported validation against historical geothermal or lithium-extraction data, cross-model comparison, or out-of-sample testing; this directly affects the load-bearing claim that net LCOE becomes competitive enough to alter capacity mix.
  3. [Results] Results on lithium supply: the 1.2 million EV-battery figure for 10% municipal deployment in the Upper Rhine Graben is a direct scaling of the modeled lithium yield; without an accompanying table or equation showing how recovery rate, brine concentration, and plant capacity factor combine to produce this number, it is impossible to assess whether the 70% EU-registration equivalence survives parameter variation.
minor comments (1)
  1. [Abstract] Notation for 'unfavorable conditions' is used in the abstract but never defined with explicit numerical bounds in the text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below and will revise the manuscript to improve transparency and robustness where the concerns are valid.

read point-by-point responses

  1. Referee: [Abstract] Abstract and modeling description: the claim that geothermal+DLE plants are selected by the optimizer 'even under unfavorable conditions' and displace PV/wind/storage rests entirely on externally sourced values for DLE capital/O&M costs, lithium recovery fraction, and realized market price; no sensitivity ranges, downside scenarios, or Monte-Carlo propagation of these three free parameters are reported, so the displacement result is not insulated from plausible upward bias in early-stage DLE estimates.

    Authors: We agree that explicit sensitivity analysis on the DLE parameters is needed to substantiate the 'unfavorable conditions' claim. The manuscript used conservative point estimates drawn from the literature, but did not propagate uncertainty. In revision we will add a sensitivity analysis section that varies DLE capex, opex, recovery fraction, and lithium price over literature-derived ranges (including downside scenarios) and report the resulting changes in optimal capacity mix and displacement of PV/wind/storage. revision: yes

  2. Referee: [Methods] Methods/optimization setup: the energy-system model outputs inherit the accuracy of the input techno-economic database without reported validation against historical geothermal or lithium-extraction data, cross-model comparison, or out-of-sample testing; this directly affects the load-bearing claim that net LCOE becomes competitive enough to alter capacity mix.

    Authors: The techno-economic inputs are taken from peer-reviewed studies and industry reports on geothermal and emerging DLE processes. Comprehensive historical validation is limited because commercial-scale DLE from geothermal brines remains at pilot stage. We will expand the methods section to document all parameter sources, cite any available pilot data for cross-checks, and explicitly discuss model limitations and the absence of out-of-sample testing for this nascent technology. revision: partial

  3. Referee: [Results] Results on lithium supply: the 1.2 million EV-battery figure for 10% municipal deployment in the Upper Rhine Graben is a direct scaling of the modeled lithium yield; without an accompanying table or equation showing how recovery rate, brine concentration, and plant capacity factor combine to produce this number, it is impossible to assess whether the 70% EU-registration equivalence survives parameter variation.

    Authors: We agree that the lithium-yield calculation must be fully transparent. The 1.2 million figure is obtained by scaling the per-plant lithium output (itself a function of brine concentration, recovery rate, and capacity factor) across the assumed 10 % municipal deployment. In the revised manuscript we will insert an explicit equation together with a supplementary table that lists all inputs and the arithmetic steps, enabling readers to test sensitivity of the EU-equivalence result to parameter changes. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper reports results from regional energy system optimization models whose outputs depend on externally sourced techno-economic parameters (capex, lithium recovery rates, prices) drawn from independent literature. No equations, self-citations, or derivations are quoted that reduce the central claim to a self-definition, a fitted input renamed as prediction, or a load-bearing self-citation chain. The modeling approach is standard and remains falsifiable against external benchmarks; the derivation chain does not collapse by construction.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 0 invented entities

The central claim rests on techno-economic parameters and modeling assumptions typical for energy system studies; no new physical entities are postulated.

free parameters (3)
  • direct lithium extraction costs and recovery rates
    Capital and operating costs plus lithium yield per unit of geothermal fluid are required inputs that determine whether revenue offsets plant costs.
  • deep geothermal capital and O&M costs
    Plant construction and maintenance costs are key fitted or assumed values that affect the cost-competitiveness result.
  • lithium market price
    Future lithium price assumptions directly influence the revenue side of the optimization.
axioms (2)
  • domain assumption Energy system models assume perfect foresight, cost minimization, and perfect substitutability between generation and storage technologies.
    Standard assumption in linear programming energy system models used for long-term capacity expansion.
  • domain assumption Regional lithium resource potentials and extraction feasibility are accurately represented by the input data.
    The 10% municipality adoption scenario and resulting 1.2 million battery packs depend on these external estimates.

pith-pipeline@v0.9.0 · 5722 in / 1504 out tokens · 26249 ms · 2026-05-24T09:35:25.396065+00:00 · methodology

discussion (0)

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