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Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach

T0 review · 2 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A wind-electrolyzer plant's bid curve should reflect the hydrogen it forgoes, and market bidding lowers power costs and curtailment.

desk verdict Clean bid-curve derivation for electrolyzer-wind plants, but the fixed-consumption benchmark is biased and the perfect-flexibility assumption is untested—send to review with conditions. read the letter →

arxiv 2501.16844 v3 pith:XJ3MIZOL submitted 2025-01-28 math.OC

Andrea Gloppen Johnsen , Lesia Mitridati , Jalal Kazempour , Line Roald This is my paper · ORCID
classification math.OC
keywords renewableelectrolyzerplantopportunitycostbiddingelectricitymarkethydrogenproductioncurvedemand-sideflexibilitycurtailmentDCoptimalpowerflow
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out to turn a co-located wind-and-electrolyzer plant into an active, price-responsive participant in the wholesale electricity market. Its central move is to derive the plant's bid curve from the revenue the electrolyzer forgoes when electricity is sold to the grid instead of being used to make hydrogen; that forgone hydrogen revenue is the plant's opportunity cost of selling power. The resulting piecewise-linear, convex cost curve can be fed directly into a standard market-clearing optimization without binary variables. Using a year of data on a three-region reliability test system, the paper argues that a market-bidding electrolyzer lowers the average cost of electricity and renewable curtailment compared with a fixed-consumption electrolyzer, while having little effect on total emissions. The practical stakes are that electrolyzer flexibility can be represented truthfully in existing market designs, rather than treated as an exogenous price-taking load.

What carries the argument

The load-bearing object is the opportunity-cost bid curve of the renewable electrolyzer plant. It is built from the electrolyzer's empirical hydrogen production curve $h(p_h)$, which is non-convex but is approximated as concave piecewise linear; multiplying each segment's slope by the fixed hydrogen price $\lambda_h$ gives the REP's marginal opportunity cost of selling power, and the intercepts are fixed by the available renewable output $P^{RES}$. When renewable output exceeds electrolyzer capacity, the excess is offered at zero marginal cost; when renewable output is below capacity, the curve extends to negative exports, which the market reads as a willingness-to-pay for imports. This curve is the entire argument: it packages the electrolyzer's efficiency, the hydrogen price, and the renewable availability into a bid that a DC optimal power flow can clear directly, without binary variables or exogenous price forecasts.

What would settle it

Re-run the year-long case study with an electrolyzer model that includes a minimum load, a one-hour start-up delay from stand-by, and a 2.5% stand-by consumption cost; if a market-bidding REP then no longer reduces average electricity cost or renewable curtailment relative to fixed consumption, the central claim is falsified.

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Extended reading notes

Core claim

The paper's central claim is that the marginal cost of exporting electricity from a renewable electrolyzer plant is the opportunity cost of giving up hydrogen production, not the zero marginal cost usually assigned to renewable generation. Formally, with hydrogen revenue $r_h(p)=h(p)\lambda_h$ and available renewable output $P^{RES}$, the cost of selling a power quantity $p^{DA}$ is $c_{el}(p^{DA})=r_h(P^{RES})-r_h(P^{RES}-p^{DA})$, and the marginal cost is the derivative of this curve. Because the hydrogen production curve $h(p)$ is non-convex and has no closed form, the paper approximates it as a concave piecewise-linear function, which makes the derived cost curve convex and piecewise linear, suitable for submission to the market. In the case study, a co-located wind farm with a market-bidding electrolyzer produces lower average electricity cost and lower renewable curtailment than the same wind farm with a fixed electrolyzer load, while the emissions difference between the two operating strategies stays small.

Load-bearing premise

The bid curve assumes the electrolyzer can change its electricity consumption freely within the one-hour market interval, with no minimum load, no start-up delay, and no stand-by consumption cost, and the paper itself notes that alkaline electrolyzers may take more than an hour to start from a full shutdown.

Editorial extensions

If this is right

  • A renewable electrolyzer plant can be cleared in standard market software through a convex piecewise-linear bid curve, so no binary variables or exogenous price forecasts are needed to represent its flexibility.
  • At low hydrogen prices, active market bidding lowers the system's average generation cost per load and renewable curtailment relative to a fixed-consumption electrolyzer.
  • At high hydrogen prices, the electrolyzer's marginal cost moves up the merit order and it behaves almost like a fixed load, which erodes the system-level benefits of market bidding.
  • Relaxed transmission models (copper-plate or zonal) overestimate the electrolyzer's consumption and profit because they miss the intra-regional congestion that a nodal model captures.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same opportunity-cost construction should extend to any co-located flexible load with a concave benefit curve, such as desalination or data centers, a step the paper does not take.
  • Because the bid curve is proportional to the hydrogen price, allowing the hydrogen price to vary hour by hour would shift the bid curve; coupling it to natural-gas prices is a natural test of the framework's sensitivity.
  • The analysis is deterministic in renewable output; under forecast uncertainty the opportunity cost should be replaced by its expectation over wind scenarios, which is an open extension.
  • The results imply that hydrogen subsidies that raise the effective price may reduce electrolyzer flexibility and thereby counteract the grid benefits of market bidding, a policy consequence the paper mentions but does not develop.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper proposes a method to construct a piecewise-linear convex bid curve for a renewable electrolyzer plant (REP), defined as a co-located renewable generator and an electrolyzer sharing a grid connection. The bid curve is derived from the opportunity cost of selling electricity to the grid rather than using it for hydrogen production, given a fixed hydrogen price and an empirical hydrogen production curve. The derived curve is integrated into a DC OPF market-clearing model, and a case study on the RTS-GMLC system compares a market-bidding REP against a fixed-consumption REP in terms of generation cost, renewable curtailment, and emissions. The paper also compares nodal, zonal, and copper-plate network representations and analyzes the effect of electrolyzer capacity and hydrogen price on the results.

Significance. The derivation of a convex, piecewise-linear bid curve from the opportunity cost of a non-convex production process is a clean and useful contribution: it connects the techno-economic detail of electrolyzer operation with a standard market-clearing formulation, and the accompanying case study is implemented with publicly available code on a standard test system. The analysis of transmission-network modeling choices and the finding that market bidding does not significantly change system emissions relative to fixed consumption are appropriately nuanced and of interest to the power-systems community. However, the central policy-relevant comparison against a fixed-consumption benchmark is undermined by the benchmark's construction, and the key flexibility assumption is not stress-tested. If these issues are addressed, the paper would be a valuable contribution to the literature on electrolyzer market participation and renewable hydrogen production.

major comments (2)
  1. [IV-C1, Fig. 7, Table II] The fixed-consumption benchmark is not a fair counterfactual. The fixed load is defined as the average consumption of the market-bidding REP (an endogenous outcome of the flexible case), and in hours where this load is infeasible the authors first model it as a bid with a $10,000/MWh marginal cost and then fix the load to the reduced value. This asymmetric treatment forces the fixed case to pay an arbitrary, extremely high price for any required curtailment, while the market-bidding case reduces consumption at its true opportunity cost. The resulting differences in cost and curtailment (Figs. 7 and 8) are therefore at least in part an artifact of the benchmark design. Moreover, the actual fixed-case consumption is lower than the market-bidding case (e.g., 617.38 vs 629.77 MW for the 1000 MW, $1.5/kg case in Table II), so the two cases do not even compare equal total electrolyzer energy. Please redesign the fixed benchmark with an exogenous, prespecified consumption level (e.g., a fixed utilization factor) and handle infeasibilities in both cases in a symmetric way, such as allowing voluntary load shedding in both cases at the same penalty price.
  2. [III-A and III-D] The entire analysis relies on the assumption that the electrolyzer can freely change its set-point within the hourly market interval, has no start-up time or cost, no minimum load, and no cost for stand-by consumption. The text explicitly acknowledges that alkaline electrolyzers may have start-up times exceeding one hour and that stand-by consumption (around 2.5% of nominal capacity) is neglected, but the case study never assesses how often the optimal market-bidding dispatch enters the problematic low-load or shutdown region. Because the headline benefit of market bidding is precisely the value of this flexibility, the manuscript should report the distribution of hourly electrolyzer loads for the market-bidding cases (e.g., the number of hours at or near zero load) and should include a robustness test that imposes a minimum load and/or a start-up cost to show that the main conclusions regarding cost and curtailment are not driven by an overly optimistic flexibility assumption. Without such a check, the quantitative claims about system-level benefits are not fully supported.
minor comments (6)
  1. [Eq. (11)] The range is printed as 'p_DA in [P_RES - P_i, P_RES - P_i]' which is confusing because the upper end appears to be the same as the lower end in the typeset version; it should presumably be [P_RES - \bar{P}_i, P_RES - \underline{P}_i]. Please correct the notation so the interval is clearly stated.
  2. [IV-C1] The sentence 'we first model the fixed consumption as a bid with a high marginal cost (of $10,000 per MWh), thus approximating the highest electrolyzer consumption possible while maintaining a feasible program' is unclear; the penalty-bid method is really a load-shedding approximation, not an approximation of the highest feasible consumption. Please rewrite to explain that the fixed load is curtailed in infeasible hours.
  3. [III-A] The paper states that the approximation error of the piecewise linear hydrogen production curve is negligible, but it does not provide a quantitative error measure; showing the error (e.g., in % of hydrogen output or in % of revenue) would strengthen this claim.
  4. [Tables I and II] The 'Delta' symbol is used in Table I to denote percent change; please define it in the table caption. Also, in Table II the column headers are abbreviated without explanation; a short note in the caption would help.
  5. [III-C, last paragraph] The text says 'the approximated marginal cost therefore becomes an increasing, step-wise function'; strictly, it is non-decreasing, since the first piece can be zero in the excess-RES case. Please adjust the wording to 'non-decreasing'.
  6. [V] The conclusion section contains several consecutive sentences beginning with 'Therefore'; minor editorial condensation would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the bid curve is derived from exogenous hydrogen price and an empirical electrolyzer curve, and self-citations supply inputs rather than conclusions.

full rationale

The derivation chain is self-contained. The REP bid curve is constructed from an exogenous hydrogen price (Section II: "we assume that the hydrogen price is fixed and exogenously determined"), an empirical electrolyzer production curve h(ph) (Section III-A, grounded in the external empirical works [32] and [33] and the production curve of [10]), and the hour-specific renewable availability P_RES. Equations (5), (7), (9)-(12) are algebraic consequences of defining opportunity cost as lost hydrogen revenue; no parameter is fitted to the system-level outcomes (cost, curtailment, emissions) reported in the case study. The self-citations to [10] and [36] provide modeling inputs and a modular-operations assumption, but the cited curve rests on independent empirical sources, and the assumptions are stated explicitly rather than imported as a uniqueness theorem or hidden ansatz. The fixed-consumption benchmark is defined after the fact as the average of the market-bidding REP's consumption (Section IV-C1: "we define the fixed consumption power as the average consumption of the market-bidding REP"), which makes the flexible-versus-fixed comparison fair but does not enter the bid-curve derivation as a fitted parameter. The paper's acknowledged limitations, such as one-hour set-point freedom and neglect of start-up time, minimum load, and stand-by consumption (Section III-A), are modeling risks rather than circular steps. No step of the claimed derivation reduces to its own input or to a self-citation chain, so no specific circular step is identified.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The derivation relies on standard market assumptions and an empirical production curve; the main free parameters are scenario choices. No new physical entities are introduced. The most fragile input is the perfect flexibility assumption for the electrolyzer.

free parameters (3)
  • Hydrogen price λh = $1.5/kg and $6/kg
    Selected from reports of hydrogen production costs as scenarios, not fitted to outcomes. It directly shifts the bid curve.
  • Electrolyzer capacity in case study = 100, 500, 1000 MW
    Three scenario levels chosen to test capacity effects on system and REP results.
  • Number of linearization pieces for hydrogen production curve = 4 in Fig. 3; not specified for case study
    Arbitrary choice; no sensitivity analysis is provided to show whether the number of pieces affects scheduling results.
assumptions (6)
  • domain assumption Electricity market clears under perfect competition and participants bid true marginal cost
    Stated in Section II; justifies using the derived cost curve as the actual bid curve.
  • standard math Submitted bid curves must be piecewise linear and convex
    Standard market requirement in many U.S. and E.U. markets; motivates the convex piecewise-linear approximation.
  • domain assumption The empirical hydrogen production curve from [10] is representative of the electrolyzer
    Used as the basis for the revenue curve; taken from prior work without re-validation in this paper.
  • ad hoc to paper Electrolyzers can freely change set-point within one hour, have no startup costs or minimum load, and stand-by consumption is negligible
    Assumed in Section III-A; not supported by data for all technologies. Could overstate flexibility and thus the benefits of market bidding.
  • domain assumption Hydrogen price is fixed and exogenous
    Simplifies the derivation; the paper notes it can be time-varying but does not model that in the case study.
  • standard math DC OPF with nodal pricing represents the market clearing outcome
    Standard model for transmission-constrained electricity markets.

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Cite this review

Pith. "Pith review of Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach." pith.science (2026). https://pith.science/paper/XJ3MIZOL

@misc{pith2026250116844,
  author       = {Pith},
  title        = {Pith review of: Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XJ3MIZOL}},
  note         = {Machine review of arXiv:2501.16844}
}
read the original abstract

Hydrogen produced through electrolysis with renewable power is considered key to decarbonize several hard-to-electrify sectors. This work proposes a novel approach to model the active electricity market participation of co-located renewable energy and electrolyzer plants, based on opportunity-cost bidding. While a renewable energy plant typically has zero marginal cost, selling power to the grid carries a potential opportunity-cost of not producing hydrogen when it is co-located with a hydrogen electrolyzer. We first consider only the electrolyzer, and derive its revenue of consuming electricity based on the non-convex hydrogen production curve. We then consider the available renewable energy production and form a piece-wise linear cost curve representing the opportunity cost of selling (or revenue from consuming) various levels of electricity. This cost curve can be used to model a stand-alone electrolyzer or a co-located hydrogen and renewable energy plant participating in an electricity market. Our case study analyzes the effects of market-bidding electrolyzers on electricity markets and grid operations. We compare two strategies for a co-located electrolyzer-wind plant; one based on the proposed bid curve and one with a more conventional fixed electrolyzer consumption. The results show that electrolyzers that actively participate in the electricity market lower the average cost of electricity and the amount of curtailed renewable energy in the system compared with a fixed consumption case. However, the difference in total system emissions between the two strategies is insignificant. The specific impacts vary based on electrolyzer capacity and hydrogen price, which determines the location of the co-located plant in the electricity market merit order.

Figures

Figures reproduced from arXiv: 2501.16844 by the authors.

Figure 1
Figure 1. The renewable electrolyzer plant, REP, consisting of co￾located RES and an electrolyzer with a common connection point to the grid. The REP generates electricity from the RES and produces hydrogen via the electrolyzer. The electricity produced can be used internally for hydrogen production or exported to the grid. When RES production is low, the REP may import electricity to maintain hydrogen production. The hydroge… view at source ↗
Figure 2
Figure 2. Derivation of the REP (marginal) cost curve from the empirical hydrogen production curve. Illustrated for P¯h = P RES = 500 MW. For an electrolyzer within a REP where the renewable generation capacity is larger than the electrolyzer capacity, i.e., P RES > P¯h , the available renewable power may at times exceed the electrolyzer capacity. In these cases, the excess power cannot be used to further increase hydrogen pr… view at source ↗
Figure 3
Figure 3. Illustrations of the REP’s approximated cost curves (first row) and approximated marginal cost curves (second row). We show three cases of renewable power production relative to the electrolyzer capacity, set to 500 MW in this example, with four arbitrarily selected pieces. When there is excess renewable power, meaning production exceeds the electrolyzer capacity, this excess power is offered at zero marginal cost, … view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Schematic of an REP actively participating in an electricity market (here, a day-ahead market). An exogenous, fixed hydrogen price λ h , combined with known local renewable power production P RES t at hour t and hydrogen production curve h(p h ), defines the REP’s bid …
Figure 5
Figure 5. Figure 5: Generation capacities and demand range per region of the RTS-GMLC. While region 3 has the most capacity, it also has a slightly higher demand load, approximately 10% higher than the other two regions across the year. Further, the increase in generation capacity in regi…
Figure 6
Figure 6. Figure 6: Merit order curve of the total installed generation capacities for a low (left) and high (right) hydrogen price. The REP illustrated consists of a 847 MW wind farm and a 500 MW electrolyzer capacity. The adjusted net demand, i.e., the demand subtracted by the renewable…
Figure 7
Figure 7. Figure 7: Total cost of generation and cost of generation per load for a fixed consumption and market-bidding electrolyzer under the hydrogen prices of $1.5 and $6 per kg. Base Bidder Fixed Bidder Fixed 0 200 400 600 RES curtailment [GWh] λ h = $1.5 per kg λ h = $6 per kg 100 MW…
Figure 8
Figure 8. Figure 8: Total curtailment of renewable power generation. power curtailment over the year in the entire system, for the different electrolyzer capacities, REP representations, and hydrogen prices explored previously. In absence of congestion, increasing system load should yield…
Figure 6
Figure 6. Figure 6: For a higher hydrogen price, the REP lies high in the [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

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Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages

  1. [1]

    Global Hydrogen Review 2023

    International Energy Agency (IEA), “Global Hydrogen Review 2023.”

    work page 2023

  2. [2]

    DOE national clean hy- drogen strategy and roadmap,

    U.S. Department of Energy, “DOE national clean hy- drogen strategy and roadmap,” 2023. https://www. hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/ us-national-clean-hydrogen-strategy-roadmap.pdf?sfvrsn=c425b44f 5

    work page 2023

  3. [3]

    A hydrogen strategy for a climate-neutral Eu- rope,

    European Comission, “A hydrogen strategy for a climate-neutral Eu- rope,” 2020. https://energy.ec.europa.eu/system/files/2020-07/hydrogen strategy 0.pdf

    work page 2020

  4. [4]

    Electric power system flexi- bility: Challenges and opportunities

    Electric Power Research Institute (EPRI), “Electric power system flexi- bility: Challenges and opportunities.” Palo Alto, CA. https://www.epri. com/research/programs/109396/results/3002020700

    work page arXiv

  5. [5]

    The Goverment’s Strategy for Power-to-X,

    Danish Ministry of Climate, Energy and Utilities, “The Goverment’s Strategy for Power-to-X,” 2021. https://ens.dk/sites/ens.dk/files/ptx/ strategy ptx.pdf

    work page 2021

  6. [6]

    Demand-side view of electricity markets,

    D. Kirschen, “Demand-side view of electricity markets,” IEEE Trans- actions on Power Systems , vol. 18, no. 2, pp. 520–527, 2003

    work page 2003

  7. [7]

    Assessing the value and risk of renewable ppas,

    J. Pombo-Romero, O. R ´uas-Barrosa, and C. V ´azquez, “Assessing the value and risk of renewable ppas,” Energy Economics , vol. 139, p. 107861, 11 2024

    work page 2024

  8. [8]

    https://energy.ec.europa.eu/ delegated-regulation-union-methodology-rfnbos en

    “EU Commission Delegated Regulation (EU) 2023/1184 of 10 Febru- ary 2023 Supplementing Directive (EU) 2018/2001 of the European Parliament and of the Council by Establishing a Union Methodol- ogy Setting Out Detailed Rules for the Production of Renewable Fuels of Non-Biological Origin,” 2023. https://energy.ec.europa.eu/ delegated-regulation-union-methodo...

    work page 2023

Show all 41 references
  1. [9]

    Section 45V Credit for Production of Clean Hydrogen; Section 48(a)(15) Election To Treat Clean Hydrogen Production Facilities as Energy Property,

    Internal Revenue Service (IRS), Treasury, “Section 45V Credit for Production of Clean Hydrogen; Section 48(a)(15) Election To Treat Clean Hydrogen Production Facilities as Energy Property,” 2023. https: //www.govinfo.gov/content/pkg/FR-2023-12-26/pdf/2023-28359.pdf

    2023

  2. [10]

    Optimization of hybrid power plants: When is a detailed electrolyzer model necessary?,

    M. T. Baumhof, E. Raheli, A. Gloppen Johnsen, and J. Kazempour, “Optimization of hybrid power plants: When is a detailed electrolyzer model necessary?,” in 2023 IEEE Belgrade PowerTech, pp. 1–10, 2023

    2023

  3. [11]

    A conic model for electrolyzer scheduling,

    E. Raheli, Y . Werner, and J. Kazempour, “A conic model for electrolyzer scheduling,” Computers & Chemical Enginnering , vol. 179, 2023

    2023

  4. [12]

    Optimal day-ahead dispatch of an alkaline electrolyser system concerning thermal–electric properties and state-transitional dynamics,

    Y . Zheng, S. You, H. W. Bindner, and M. M ¨unster, “Optimal day-ahead dispatch of an alkaline electrolyser system concerning thermal–electric properties and state-transitional dynamics,” Applied Energy , vol. 307, p. 118091, 2022

    2022

  5. [13]

    Modeling alkaline water electrolysis for power-to-x applications: A scheduling approach,

    C. Varela, M. Mostafa, and E. Zondervan, “Modeling alkaline water electrolysis for power-to-x applications: A scheduling approach,” Int. Journal of Hydrogen Energy , vol. 46, no. 14, pp. 9303–9313, 2021

    2021

  6. [14]

    Optimal bidding for a large battery-electrolyzer facility in the day-ahead market,

    P. Miljan, Z. Tomsic, and H. Pandzic, “Optimal bidding for a large battery-electrolyzer facility in the day-ahead market,” MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings , pp. 201–206, 2022

    2022

  7. [15]

    Understanding the role and design space of demand sinks in low-carbon power systems,

    S. van der Jagt, N. Patankar, and J. Jenkins, “Understanding the role and design space of demand sinks in low-carbon power systems,”Energy and Climate Change, vol. 5, p. 100132, 2024

    2024

  8. [16]

    Minimizing emissions from grid-based hydrogen production in the United States,

    W. Ricks, Q. Xu, and J. Jenkins, “Minimizing emissions from grid-based hydrogen production in the United States,” Environmental Research Letters, vol. 18, p. 014025, 2024

    2024

  9. [17]

    The influence of additionality and time-matching requirements on the emissions from grid-connected hydrogen production,

    M. Giovanniello, A. Cybulsky, T. Schittekatte, and D. Mallapragada, “The influence of additionality and time-matching requirements on the emissions from grid-connected hydrogen production,” Nature Energy , vol. 9, 2024

    2024

  10. [18]

    Temporal regulation of renew- able supply for electrolytic hydrogen,

    E. Zeyen, I. Riepin, and T. Brown, “Temporal regulation of renew- able supply for electrolytic hydrogen,” Environmental Research Letters, vol. 19, p. 024034, 2024

    2024

  11. [19]

    Interactions and distortions of different support policies for green hydrogen,

    A. Hoogsteyn, J. Meus, K. Bruninx, and E. Delarue, “Interactions and distortions of different support policies for green hydrogen,” Energy Economics, vol. 141, p. 108042, 2025

    2025

  12. [20]

    Carbon impact of transmission constraints

    S. Sofia and Y . Dvorkin, “Carbon impact of transmission constraints.” https://papers.ssrn.com/abstract=4972564, 2024

    2024

  13. [21]

    Optimal response of a thermal unit to an electricity spot market,

    J. Arroyo and A. Conejo, “Optimal response of a thermal unit to an electricity spot market,” IEEE Transactions on Power Systems , vol. 15, no. 3, pp. 1098–1104, 2000

    2000

  14. [22]

    Price-taker bidding strategy under price uncertainty,

    A. J. Conejo, F. J. Nogales, and J. M. Arroyo, “Price-taker bidding strategy under price uncertainty,” IEEE Transactions on Power Systems, vol. 17, pp. 1081–1088, 11 2002

    2002

  15. [23]

    Constructing bidding curves for a price- taking retailer in the norwegian electricity market,

    S. E. Fleten and E. Pettersen, “Constructing bidding curves for a price- taking retailer in the norwegian electricity market,” IEEE Transactions on Power Systems, vol. 20, pp. 701–708, 5 2005

    2005

  16. [24]

    Bidding in sequential electricity markets: The nordic case,

    T. K. Boomsma, N. Juul, and S. E. Fleten, “Bidding in sequential electricity markets: The nordic case,” European Journal of Operational Research, vol. 238, pp. 797–809, 11 2014

    2014

  17. [25]

    Short-term trading for a wind power producer,

    J. M. Morales, A. J. Conejo, and J. P ´erez-Ruiz, “Short-term trading for a wind power producer,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 554–564, 2010

    2010

  18. [26]

    Euphemia public description: Single price coupling algorithm,

    NEMO Committee, “Euphemia public description: Single price coupling algorithm,” 2019. https://www.nordpoolgroup. com/globalassets/download-center/single-day-ahead-coupling/ euphemia-public-description.pdf

    2019

  19. [27]

    PJM Manual 15: Cost Development Guidelines,

    PJM Interconnection, “PJM Manual 15: Cost Development Guidelines,”

  20. [28]

    Why marginal pricing?,

    A. J. Conejo, “Why marginal pricing?,” Journal of Modern Power Systems and Clean Energy , vol. 11, no. 3, pp. 693–697, 2023

    2023

  21. [29]

    Regulation (EU) No 1227/2011 of the European Parliament and of the Council of 25 October 2011 on wholesale energy market integrity and transparency (REMIT)

    European Parliament and Council, “Regulation (EU) No 1227/2011 of the European Parliament and of the Council of 25 October 2011 on wholesale energy market integrity and transparency (REMIT).” https: //eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32011R1227,

    2011

  22. [30]

    The european hydrogen market landscape

    European Hydrogen Observatory, “The european hydrogen market landscape.” https://observatory.clean-hydrogen.europa.eu/sites/ default/files/2024-11/The%20European%20hydrogen%20market% 20landscape November%202024.pdf

    2024

  23. [31]

    Global Hydro- gen Review 2024

    International Energy Agency (IEA), “Global Hydro- gen Review 2024.” https://iea.blob.core.windows. net/assets/89c1e382-dc59-46ca-aa47-9f7d41531ab5/ GlobalHydrogenReview2024.pdf

    2024

  24. [32]

    Modeling of advanced alkaline electrolyzers: A sys- tem simulation approach,

    O. Ulleberg, “Modeling of advanced alkaline electrolyzers: A sys- tem simulation approach,” International Journal of Hydrogen Energy , vol. 28, no. 1, pp. 21–33, 2003

    2003

  25. [33]

    Semi- empirical model and experimental validation for the performance eval- uation of a 15 kw alkaline water electrolyzer,

    M. S ´anchez, E. Amores, L. Rodr ´ıguez, and C. Clemente-Jul, “Semi- empirical model and experimental validation for the performance eval- uation of a 15 kw alkaline water electrolyzer,” International Journal of Hydrogen Energy, vol. 43, pp. 20332–20345, 11 2018

    2018

  26. [34]

    Comparative experimental study of alkaline and proton exchange membrane water electrolysis for green hydrogen production,

    J. Wang, J. Yang, Y . Feng, J. Hua, Z. Chen, M. Liao, J. Zhang, and J. Qin, “Comparative experimental study of alkaline and proton exchange membrane water electrolysis for green hydrogen production,” Applied Energy, vol. 379, p. 124936, 2 2025

    2025

  27. [35]

    Novel electrolyzer applica- tions: Providing more than just hydrogen,

    J. Eichman, K. Harrison, and M. Peters, “Novel electrolyzer applica- tions: Providing more than just hydrogen,” National Renewable Energy Laboratory (NREL), 2014. http://www.osti.gov/servlets/purl/1159377/

    2014

  28. [36]

    Advanced scheduling of electrolyzer modules for grid flexibility,

    A. Lesniak, A. G. Johnsen, N. Rhodes, and L. Roald, “Advanced scheduling of electrolyzer modules for grid flexibility,” in 2025 IEEE Power and Energy Society General Meeting (PESGM), Austin, TX, USA, 2025, 2024. In press

    2025

  29. [37]

    The IEEE reliability test system: A proposed 2019 update,

    C. Barrows, A. Bloom, A. Ehlen, J. Ik ¨aheimo, J. Jorgenson, D. Krish- namurthy, J. Lau, B. McBennett, M. O’Connell, E. Preston, A. Staid, G. Stephen, and J.-P. Watson, “The IEEE reliability test system: A proposed 2019 update,” IEEE Transactions on Power Systems , vol. 35, no...

    2019

  30. [38]

    Powermodels.jl: An open- source framework for exploring power flow formulations,

    C. Coffrin, R. Bent, K. Sundar, Y . Ng, and M. Lubin, “Powermodels.jl: An open- source framework for exploring power flow formulations,” in 2018 Power Systems Computation Conference (PSCC) , (Dublin, Ireland), pp. 1–8, 2018

    2018

  31. [39]

    Opportunity cost of reneawable electrolyzer plants

    A. Gloppen Johnsen, “Opportunity cost of reneawable electrolyzer plants.” https://github.com/andreagloppen/OC of REPs. APPENDIX A. Derivation of the Approximated Cost Curve Slopes and Intercepts For notational simplicity, we denote x as the general vari- able for power in all ...

  32. [2011]

    Accessed: 2025-06-24

    2025

  33. [2020]

    https://www.pjm.com/-/media/DotCom/documents/manuals/ archive/m15/m15v36-cost-development-guidelines-09-01-2020.pdf

    2020

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.