A Process-Aware Demand Response Evaluation Framework for Hydrogen-Integrated Zero-Carbon Steel Plants Coupled with Methanol Production

Jianzhong Wu; Kaidi Huang; Lin Cheng; Ming Cheng; Ning Qi; Qiang Ji; Yue Zhou

arxiv: 2604.04486 · v2 · submitted 2026-04-06 · 📡 eess.SY · cs.SY

A Process-Aware Demand Response Evaluation Framework for Hydrogen-Integrated Zero-Carbon Steel Plants Coupled with Methanol Production

Qiang Ji , Lin Cheng , Yue Zhou , Ning Qi , Kaidi Huang , Jianzhong Wu , Ming Cheng This is my paper

Pith reviewed 2026-05-10 20:10 UTC · model grok-4.3

classification 📡 eess.SY cs.SY

keywords demand responsehydrogen direct reduced ironelectric arc furnacemethanol productionrenewable energy integrationprocess constraintszero-carbon steelflexibility evaluation

0 comments

The pith

A process-aware framework for hydrogen steel plants integrated with methanol production delivers 178 MW of effective demand response capacity while improving renewable energy matching.

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

The paper develops a framework to evaluate demand response potential in zero-carbon steel production systems that use hydrogen and couple with methanol synthesis. It models the electric arc furnace's operational limits through a feasible region validated against real plant data, then applies a nonlinear asymmetric penalty and rolling optimization to schedule load adjustments without risking process integrity. This approach allows steel plants to act as flexible loads for power grids with high renewables, as shown in case studies where it boosts delivered capacity, doubles renewable-load alignment, and lowers overall costs compared to standard methods.

Core claim

The authors introduce an H2-DRI-EAF-MeOH system architecture and formulate an operating feasible region model for the EAF with 4.1% validation error. They then build a process-aware DR evaluation model using nonlinear asymmetric penalties and adaptive rolling to account for operator preferences, establishing dual metrics for grid-side DR capacity and risks. Case studies demonstrate an average effective delivered DR capacity of 178.3 MW, RES-load matching improvement from 0.257 to 0.587, 15.68% cost reduction, and mitigation of tail risks via the exponential penalty.

What carries the argument

The operating feasible region model for the electric arc furnace, which captures operational constraints tractably and is validated with field data, combined with the nonlinear asymmetric penalty function in the demand response optimization to reflect aversion to process deviations.

If this is right

The integrated H2-DRI-EAF-MeOH architecture eliminates residual emissions by coupling steel production with methanol synthesis.
Dual-side evaluation metrics make load-side regulation behaviors observable to the grid operator.
The adaptive rolling mechanism prevents myopic short-term scheduling decisions.
The exponential asymmetric penalty specifically reduces the probability of extreme process deviation events.
Overall, the framework provides a foundation for using industrial synergies to address power system flexibility shortages.

Where Pith is reading between the lines

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

Similar feasible region modeling could be applied to other energy-intensive industries like aluminum smelting or chemical plants to unlock additional demand response.
The cost reductions suggest that operators might adopt this for economic benefits if paired with appropriate market incentives for flexibility.
Improved RES matching implies reduced need for backup generation or storage in renewable-heavy grids.

Load-bearing premise

The framework assumes the developed operating feasible region model for the EAF accurately captures real operational constraints, as indicated by the 4.1% average relative error from field data validation, and that the asymmetric penalty correctly represents operator risk aversion.

What would settle it

Running the model on a different steel plant's operational data and observing that the predicted delivered DR capacity differs substantially from actual achievable values, or that process deviations occur more frequently than the penalty predicts.

Figures

Figures reproduced from arXiv: 2604.04486 by Jianzhong Wu, Kaidi Huang, Lin Cheng, Ming Cheng, Ning Qi, Qiang Ji, Yue Zhou.

**Figure 2.** Figure 2: EAF operating feasible region with electricity [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: RES output and electricity-price profiles. PhSlli Wind output Solar output [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 7.** Figure 7: Comparative analysis of order fulfillment and energy [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 5.** Figure 5: Operating trajectories of key process units under base 600150 M W (t) [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Price-responsive grid-interactive operation under BI [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 10.** Figure 10: Distributional comparison of the normalized deviation [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗

read the original abstract

High penetration of renewables (RES) and the retirement of thermal units aggravate flexibility scarcity in power systems. Hydrogen-based low-carbon steel production systems possess substantial demand response (DR) potential. This paper proposes a process-aware DR evaluation framework for hydrogen-integrated zero-carbon steel plants coupled with methanol production (H2-DRI-EAF-MeOH). First, a novel H2-DRI-EAF-MeOH architecture is introduced to eliminate residual emissions via methanol synthesis. Integrated energy-material flows are formulated to reflect coupling interactions governing DR potential. Second, to capture electric arc furnace (EAF) operational constraints while preserving tractability, an operating feasible region model is developed and validated using field data from a pure hydrogen direct reduced iron and EAF plant, yielding a 4.1% average relative error. Third, a process-aware DR potential evaluation model is formulated, incorporating a nonlinear asymmetric penalty and an adaptive rolling mechanism to reflect operators' aversion to process deviations and avoid myopic scheduling. Finally, dual-side evaluation metrics are established to quantify grid-side delivered DR capacity and ramping risks, making load-side unit-level regulation behaviors observable. Case studies show the proposed framework achieves an average effective delivered DR capacity of 178.3 MW, improves RES-load matching from 0.257 to 0.587, and reduces costs by 15.68% compared to the baseline. Furthermore, the exponential asymmetric penalty mitigates extreme tail risks of process deviations. Ultimately, this work provides a theoretical foundation for leveraging RES-steel-chemical synergies to mitigate flexibility scarcity.

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.

Desk Editor's Note private letter to a colleague

The paper models a coupled steel-methanol system for grid demand response with some promising case study numbers, but the EAF feasible region validation doesn't cover the methanol coupling.

read the letter

The paper introduces a framework for assessing demand response in hydrogen-integrated steel plants that are coupled with methanol production to reach zero carbon. It reports meaningful improvements in delivered flexibility and costs from case studies, but the validation of the key model doesn't extend to the full proposed system. What is new here is the H2-DRI-EAF-MeOH architecture designed to close the carbon loop through methanol synthesis, plus the integrated modeling of energy and material flows. They develop an operating feasible region for the EAF that they validate with field data from a pure hydrogen DRI-EAF plant at 4.1% average relative error. The demand response evaluation then adds a nonlinear asymmetric penalty for process deviations and an adaptive rolling mechanism to better match how operators actually behave. In the studies, this setup delivers 178.3 MW average effective DR capacity, lifts RES-load matching from 0.257 to 0.587, and cuts costs by 15.68% against the baseline. The asymmetric penalty also helps control tail risks. The feasible region modeling and its validation stand out as practical steps that keep things computable while grounding in data. The penalty and rolling features address real operational concerns that simpler models often ignore. The main soft spot is the validation gap. The field data is from a pure-hydrogen setup without methanol production. Adding the MeOH part changes the material and energy balances, which could alter the feasible region boundaries and ramping capabilities used for DR scheduling. The reported numbers on capacity and savings rest on that model, so they may not hold up once the coupling is fully accounted for. There are also no error bars, sensitivity runs on the penalty coefficients, or out-of-sample checks described, which makes the circularity in the metrics more of a concern. This is for people working on power system flexibility, industrial decarbonization, or sector coupling between energy and chemicals. A reader focused on demand response modeling would find the process-aware elements useful to build on or critique. The paper shows clear thinking on the architecture and metrics, so it deserves a serious referee who can push on the validation and parameter choices. I would send it to peer review.

Referee Report

2 major / 1 minor

Summary. The paper proposes a process-aware demand response (DR) evaluation framework for a novel hydrogen-integrated zero-carbon steel plant architecture coupled with methanol production (H2-DRI-EAF-MeOH). It introduces the integrated energy-material flow architecture, develops and validates an operating feasible region model for the electric arc furnace (EAF) against field data from a pure-H2 DRI-EAF plant (4.1% average relative error), formulates a DR potential model incorporating a nonlinear asymmetric penalty and adaptive rolling mechanism, and establishes dual-side metrics for grid-side DR capacity and load-side behaviors. Case studies report an average effective delivered DR capacity of 178.3 MW, RES-load matching improvement from 0.257 to 0.587, and 15.68% cost reduction versus baseline, with the penalty mitigating tail risks of process deviations.

Significance. If the central claims hold after addressing validation gaps, the work offers a concrete framework for quantifying DR potential in coupled steel-chemical systems, which could inform grid flexibility planning under high renewable penetration. The emphasis on process-aware penalties and observable unit-level behaviors provides a useful bridge between operational constraints and system-level metrics.

major comments (2)

[Abstract and feasible region model section] The operating feasible region model for the EAF is validated solely against field data from a pure-hydrogen DRI-EAF plant (Abstract), yet the proposed H2-DRI-EAF-MeOH architecture adds methanol synthesis that introduces new material and energy coupling constraints. These could shift the feasible region's boundaries, ramp limits, or DR scheduling outcomes; the manuscript must demonstrate that the validated model remains applicable or provide updated validation for the coupled system, as this directly underpins the reported 178.3 MW DR capacity and cost reductions.
[Case studies section] Case-study quantitative claims (178.3 MW delivered DR, 0.257 to 0.587 RES matching, 15.68% cost reduction) rest on the feasible-region model without reported error bars, sensitivity analysis on penalty coefficients, or out-of-sample testing. This makes it hard to evaluate robustness, especially given the reader's note on potential tautology between model-defined metrics and optimization results.

minor comments (1)

[Metrics definition] Clarify the exact definition and units of the dual-side evaluation metrics (grid-side delivered DR capacity and ramping risks) to ensure they are distinguishable from the optimization objective.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the detailed and constructive review. The comments highlight important aspects of model applicability and robustness that we address below. We believe these clarifications and additions will strengthen the manuscript without altering its core contributions.

read point-by-point responses

Referee: [Abstract and feasible region model section] The operating feasible region model for the EAF is validated solely against field data from a pure-hydrogen DRI-EAF plant (Abstract), yet the proposed H2-DRI-EAF-MeOH architecture adds methanol synthesis that introduces new material and energy coupling constraints. These could shift the feasible region's boundaries, ramp limits, or DR scheduling outcomes; the manuscript must demonstrate that the validated model remains applicable or provide updated validation for the coupled system, as this directly underpins the reported 178.3 MW DR capacity and cost reductions.

Authors: We agree that explicit justification is needed. The EAF feasible region is derived from its intrinsic electrical power limits, ramp rates, and thermal balance, which are unaffected by the downstream methanol synthesis unit. The novel couplings (e.g., off-gas routing to MeOH) are instead captured in the integrated energy-material flow constraints that feed into the DR optimization. The pure-H2 validation data therefore remains representative for the EAF sub-model. We will add a dedicated paragraph in the feasible-region section explaining this separation of concerns and showing that MeOH integration does not alter EAF boundaries. We do not possess operational data from a full H2-DRI-EAF-MeOH plant, as the architecture is proposed; hence we cannot supply new field validation. revision: partial
Referee: [Case studies section] Case-study quantitative claims (178.3 MW delivered DR, 0.257 to 0.587 RES matching, 15.68% cost reduction) rest on the feasible-region model without reported error bars, sensitivity analysis on penalty coefficients, or out-of-sample testing. This makes it hard to evaluate robustness, especially given the reader's note on potential tautology between model-defined metrics and optimization results.

Authors: We accept that additional robustness checks are warranted. The reported metrics are computed from the optimization outputs using independently defined formulas (DR capacity as net load shift, RES matching as a normalized correlation index, cost reduction versus a no-DR baseline). To address concerns about robustness and any perceived circularity, we will (i) include sensitivity sweeps over the asymmetric penalty coefficient, (ii) report standard deviations across multiple RES and price scenarios, and (iii) add an out-of-sample test using withheld RES profiles. These results will be inserted into the case-studies section with accompanying figures. revision: yes

standing simulated objections not resolved

We cannot supply new field validation data for the full coupled H2-DRI-EAF-MeOH system because the architecture is novel and no operational plants yet exist.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The derivation proceeds by introducing a novel H2-DRI-EAF-MeOH architecture, formulating integrated energy-material flows, developing an operating feasible region model that is validated against independent field data from a pure-hydrogen DRI-EAF plant (4.1% average relative error), then formulating a process-aware DR model with asymmetric penalty and rolling mechanism, and finally establishing dual-side metrics whose values are computed in case studies. No equation or step reduces a claimed prediction or result to a fitted parameter or self-citation by construction; the reported performance numbers (178.3 MW delivered DR, RES matching 0.257 to 0.587, 15.68% cost reduction) are model outputs on case-study inputs rather than tautological re-statements of the inputs themselves. The external field-data validation anchors the feasible-region component, satisfying the self-contained benchmark criterion.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claims rest on the assumption that the feasible-region model and operator-behavior penalty accurately represent real plant dynamics; these are domain assumptions validated only at the aggregate 4.1% error level in the abstract.

free parameters (1)

nonlinear asymmetric penalty coefficients
Introduced to reflect operator aversion to process deviations; coefficients are not stated and must be chosen or fitted.

axioms (1)

domain assumption The EAF operating feasible region model derived from field data captures all relevant constraints for DR scheduling.
Invoked to justify tractability and accuracy of the DR potential evaluation.

invented entities (1)

H2-DRI-EAF-MeOH architecture no independent evidence
purpose: Eliminate residual emissions via methanol synthesis while enabling DR
New system configuration proposed in the paper with no independent external evidence cited.

pith-pipeline@v0.9.0 · 5601 in / 1409 out tokens · 42459 ms · 2026-05-10T20:10:53.960973+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

an operating feasible region model is developed... Γ={z|Aeq z=beq, zmin≤z≤zmax}
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

nonlinear asymmetric penalty formulation... Φt,DD u,s =exp(βu,s(σs Δψt,DD u −ϵu,s)+)−1

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages

[1]

Global iron and steel plant co2 emissions and carbon- neutrality pathways,

T. Lei, D. Wang, X. Yu, S. Ma, W. Zhao, C. Cui, J. Meng, S. Tao, and D. Guan, “Global iron and steel plant co2 emissions and carbon- neutrality pathways,”Nature, vol. 622, no. 7983, pp. 514–520, 2023

work page 2023
[2]

Frequency control and optimal operation of low-inertia power systems with hvdc and renewable energy: A review,

K. Yan, G. Li, R. Zhang, Y . Xu, T. Jiang, and X. Li, “Frequency control and optimal operation of low-inertia power systems with hvdc and renewable energy: A review,”IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4279–4295, 2024

work page 2024
[3]

Dynamic modeling and stability analysis for repeated lvrt process of wind turbine based on switched system theory,

Q. Lai, C. Shen, and D. Li, “Dynamic modeling and stability analysis for repeated lvrt process of wind turbine based on switched system theory,”IEEE Transactions on Power Systems, vol. 40, no. 3, pp. 2711–2723, 2025

work page 2025
[4]

Demand side management: Demand response, intelligent energy systems, and smart loads,

P. Palensky and D. Dietrich, “Demand side management: Demand response, intelligent energy systems, and smart loads,”IEEE transactions on industrial informatics, vol. 7, no. 3, pp. 381–388, 2011

work page 2011
[5]

Demand response potential evaluation of aggregated high-speed trains toward power system operation,

H. Yu, C. Ye, Y . Ding, L. Qiu, Y . Fang, and Y . Song, “Demand response potential evaluation of aggregated high-speed trains toward power system operation,”IEEE Transactions on Smart Grid, vol. 14, no. 5, pp. 3614–3626, 2023

work page 2023
[6]

A survey of industrial applications of demand response,

M. H. Shoreh, P. Siano, M. Shafie-khah, V . Loia, and J. P. Catal ˜ao, “A survey of industrial applications of demand response,”Electric Power Systems Research, vol. 141, pp. 31–49, 2016

work page 2016
[7]

Demand-side management in industrial sector: A review of heavy industries,

H. Golmohamadi, “Demand-side management in industrial sector: A review of heavy industries,”Renewable and Sustainable Energy Reviews, vol. 156, p. 111963, 2022

work page 2022
[8]

Electrification of industry: potential, challenges and outlook,

M. Wei, C. A. McMillan, and S. de la Rue Du Can, “Electrification of industry: potential, challenges and outlook,”Current Sustainable/Renewable Energy Reports, vol. 6, no. 4, pp. 140–148, 2019

work page 2019
[9]

Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives,

W. Sun, Q. Wang, Y . Zhou, and J. Wu, “Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives,” Applied Energy, vol. 268, p. 114946, 2020

work page 2020
[10]

Current advances in slag foaming processes toward reduced co2 emission for electric arc furnace steelmaking,

X. Liu and W. Yan, “Current advances in slag foaming processes toward reduced co2 emission for electric arc furnace steelmaking,” Journal of CO2 Utilization, vol. 90, p. 102979, 2024

work page 2024
[11]

Technological pathways for cost-effective steel decarbonization,

X. Wu, J. Meng, X. Liang, L. Sun, D. Coffman, A. Kontoleon, and D. Guan, “Technological pathways for cost-effective steel decarbonization,”Nature, pp. 1–9, 2025

work page 2025
[12]

A critical review of planning and scheduling in steel-making and continuous casting in the steel industry,

M. Lee, K. Moon, K. Lee, J. Hong, and M. Pinedo, “A critical review of planning and scheduling in steel-making and continuous casting in the steel industry,”Journal of the operational research society, vol. 75, no. 8, pp. 1421–1455, 2024

work page 2024
[13]

A comprehensive overview of industrial demand response status in europe,

M. Ranaboldo, M. Arag ¨u´es-Pe˜nalba, E. Arica, A. Bade, E. Bullich- Massagu´e, A. Burgio, C. Caccamo, A. Caprara, D. Cimmino, B. Domenechet al., “A comprehensive overview of industrial demand response status in europe,”Renewable and Sustainable Energy Reviews, vol. 203, p. 114797, 2024

work page 2024
[14]

Demand response potential evaluation of a zero carbon hydrogen metallurgy system considering shaft furnace’s flexibility,

Q. Ji, L. Cheng, K. Huang, J. Lv, Y . Zhou, and Z. Liang, “Demand response potential evaluation of a zero carbon hydrogen metallurgy system considering shaft furnace’s flexibility,” 2026. [Online]. Available: https://arxiv.org/abs/2604.00379

work page arXiv 2026
[15]

Optimal sizing of isolated renewable power systems with ammonia synthesis: Model and solution approach,

Z. Yu, J. Lin, F. Liu, J. Li, Y . Zhao, and Y . Song, “Optimal sizing of isolated renewable power systems with ammonia synthesis: Model and solution approach,”IEEE Transactions on Power Systems, vol. 39, no. 5, pp. 6372–6385, 2024

work page 2024
[16]

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span,

P. Su, Y . Zhou, and J. Wu, “Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span,”Journal of Cleaner Production, vol. 428, p. 139350, 2023

work page 2023
[17]

A review of simulation and numerical modeling of electric arc furnace (eaf) and its processes,

M. M. Abadi, H. Tang, and M. M. Rashidi, “A review of simulation and numerical modeling of electric arc furnace (eaf) and its processes,” Heliyon, vol. 10, no. 11, 2024

work page 2024
[18]

Enhancing flexibility in wind-powered hydrogen production systems through coordinated electrolyzer operation,

Z. Bai, W. Hao, Q. Li, R. Yan, B. Ding, W. Shao, L. Gao, T. Jiang, Y . Wang, and C. Wen, “Enhancing flexibility in wind-powered hydrogen production systems through coordinated electrolyzer operation,”Advances in Applied Energy, p. 100228, 2025

work page 2025
[19]

Cost-effective scheduling of steel plants with flexible eafs,

X. Zhang, G. Hug, and I. Harjunkoski, “Cost-effective scheduling of steel plants with flexible eafs,”IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 239–249, 2016

work page 2016
[20]

Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry,

J. Wang, Q. Wang, and W. Sun, “Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry,”Applied Energy, vol. 342, p. 121178, 2023

work page 2023
[21]

Models and methods for planning and scheduling in iron and steel making: Review and prospects,

N. M. Matsveichuk, Y . N. Sotskov, and L. Sun, “Models and methods for planning and scheduling in iron and steel making: Review and prospects,”International Journal of Chemical Engineering and Materials, vol. 3, pp. 145–161, 2024

work page 2024
[22]

Energy and environmental performance of iron and steel industry in real-time demand response: A case of hot rolling process,

X. Liu, W. Sun, T. Chen, X. Xu, and T. Huang, “Energy and environmental performance of iron and steel industry in real-time demand response: A case of hot rolling process,”Applied Energy, vol. 389, p. 125717, 2025

work page 2025
[23]

Integrated scheduling of hot rolling production planning and power demand response considering order constraints and tou price,

F. Gong, S. Chen, S. Tian, J. Qin, H. Zhang, B. Sun, J. Yuan, L. Jiang, Y . Xu, and Y . Wang, “Integrated scheduling of hot rolling production planning and power demand response considering order constraints and tou price,”IET Generation, Transmission & Distribution, vol. 16, no. 14, pp. 2840–2851, 2022

work page 2022
[24]

Dynamic process operation under demand response–a review of methods and tools,

E. Esche and J.-U. Repke, “Dynamic process operation under demand response–a review of methods and tools,”Chemie Ingenieur Technik, vol. 92, no. 12, pp. 1898–1909, 2020

work page 1909
[25]

Flexibility analysis for demand-side management in large-scale chemical processes: An ethylene oxide production case study,

B. Bruns, A. Di Pretoro, M. Gr ¨unewald, and J. Riese, “Flexibility analysis for demand-side management in large-scale chemical processes: An ethylene oxide production case study,”Chemical Engineering Science, vol. 243, p. 116779, 2021

work page 2021
[26]

Scheduling chemical processes for frequency regulation,

J. I. Otashu and M. Baldea, “Scheduling chemical processes for frequency regulation,”Applied Energy, vol. 260, p. 114125, 2020

work page 2020
[27]

Energy-carbon comprehensive efficiency evaluation of a hydrogen metallurgy system with low-temperature waste heat recovery,

Q. Ji, L. Cheng, Y . Zhou, Z. Liang, F. Shi, J. Zhang, and K. Li, “Energy-carbon comprehensive efficiency evaluation of a hydrogen metallurgy system with low-temperature waste heat recovery,”Applied Energy, vol. 401, p. 126646, 2025

work page 2025
[28]

A novel fractional programming-based planning model for 100% renewable poly-generation of electricity and methanol,

Z. Yu, Y . Chi, J. Lin, F. Liu, Y . Song, and F. You, “A novel fractional programming-based planning model for 100% renewable poly-generation of electricity and methanol,”IEEE Transactions on Sustainable Energy, 2025

work page 2025
[29]

Grid- aware real-time dispatch of microgrid with generalized energy storage: A prediction-free online optimization approach,

K. Huang, L. Cheng, N. Qi, D. W. Gao, A. Mujeeb, and Q. Guo, “Grid- aware real-time dispatch of microgrid with generalized energy storage: A prediction-free online optimization approach,”IEEE Transactions on Smart Grid, 2025

work page 2025

[1] [1]

Global iron and steel plant co2 emissions and carbon- neutrality pathways,

T. Lei, D. Wang, X. Yu, S. Ma, W. Zhao, C. Cui, J. Meng, S. Tao, and D. Guan, “Global iron and steel plant co2 emissions and carbon- neutrality pathways,”Nature, vol. 622, no. 7983, pp. 514–520, 2023

work page 2023

[2] [2]

Frequency control and optimal operation of low-inertia power systems with hvdc and renewable energy: A review,

K. Yan, G. Li, R. Zhang, Y . Xu, T. Jiang, and X. Li, “Frequency control and optimal operation of low-inertia power systems with hvdc and renewable energy: A review,”IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4279–4295, 2024

work page 2024

[3] [3]

Dynamic modeling and stability analysis for repeated lvrt process of wind turbine based on switched system theory,

Q. Lai, C. Shen, and D. Li, “Dynamic modeling and stability analysis for repeated lvrt process of wind turbine based on switched system theory,”IEEE Transactions on Power Systems, vol. 40, no. 3, pp. 2711–2723, 2025

work page 2025

[4] [4]

Demand side management: Demand response, intelligent energy systems, and smart loads,

P. Palensky and D. Dietrich, “Demand side management: Demand response, intelligent energy systems, and smart loads,”IEEE transactions on industrial informatics, vol. 7, no. 3, pp. 381–388, 2011

work page 2011

[5] [5]

Demand response potential evaluation of aggregated high-speed trains toward power system operation,

H. Yu, C. Ye, Y . Ding, L. Qiu, Y . Fang, and Y . Song, “Demand response potential evaluation of aggregated high-speed trains toward power system operation,”IEEE Transactions on Smart Grid, vol. 14, no. 5, pp. 3614–3626, 2023

work page 2023

[6] [6]

A survey of industrial applications of demand response,

M. H. Shoreh, P. Siano, M. Shafie-khah, V . Loia, and J. P. Catal ˜ao, “A survey of industrial applications of demand response,”Electric Power Systems Research, vol. 141, pp. 31–49, 2016

work page 2016

[7] [7]

Demand-side management in industrial sector: A review of heavy industries,

H. Golmohamadi, “Demand-side management in industrial sector: A review of heavy industries,”Renewable and Sustainable Energy Reviews, vol. 156, p. 111963, 2022

work page 2022

[8] [8]

Electrification of industry: potential, challenges and outlook,

M. Wei, C. A. McMillan, and S. de la Rue Du Can, “Electrification of industry: potential, challenges and outlook,”Current Sustainable/Renewable Energy Reports, vol. 6, no. 4, pp. 140–148, 2019

work page 2019

[9] [9]

Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives,

W. Sun, Q. Wang, Y . Zhou, and J. Wu, “Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives,” Applied Energy, vol. 268, p. 114946, 2020

work page 2020

[10] [10]

Current advances in slag foaming processes toward reduced co2 emission for electric arc furnace steelmaking,

X. Liu and W. Yan, “Current advances in slag foaming processes toward reduced co2 emission for electric arc furnace steelmaking,” Journal of CO2 Utilization, vol. 90, p. 102979, 2024

work page 2024

[11] [11]

Technological pathways for cost-effective steel decarbonization,

X. Wu, J. Meng, X. Liang, L. Sun, D. Coffman, A. Kontoleon, and D. Guan, “Technological pathways for cost-effective steel decarbonization,”Nature, pp. 1–9, 2025

work page 2025

[12] [12]

A critical review of planning and scheduling in steel-making and continuous casting in the steel industry,

M. Lee, K. Moon, K. Lee, J. Hong, and M. Pinedo, “A critical review of planning and scheduling in steel-making and continuous casting in the steel industry,”Journal of the operational research society, vol. 75, no. 8, pp. 1421–1455, 2024

work page 2024

[13] [13]

A comprehensive overview of industrial demand response status in europe,

M. Ranaboldo, M. Arag ¨u´es-Pe˜nalba, E. Arica, A. Bade, E. Bullich- Massagu´e, A. Burgio, C. Caccamo, A. Caprara, D. Cimmino, B. Domenechet al., “A comprehensive overview of industrial demand response status in europe,”Renewable and Sustainable Energy Reviews, vol. 203, p. 114797, 2024

work page 2024

[14] [14]

Demand response potential evaluation of a zero carbon hydrogen metallurgy system considering shaft furnace’s flexibility,

Q. Ji, L. Cheng, K. Huang, J. Lv, Y . Zhou, and Z. Liang, “Demand response potential evaluation of a zero carbon hydrogen metallurgy system considering shaft furnace’s flexibility,” 2026. [Online]. Available: https://arxiv.org/abs/2604.00379

work page arXiv 2026

[15] [15]

Optimal sizing of isolated renewable power systems with ammonia synthesis: Model and solution approach,

Z. Yu, J. Lin, F. Liu, J. Li, Y . Zhao, and Y . Song, “Optimal sizing of isolated renewable power systems with ammonia synthesis: Model and solution approach,”IEEE Transactions on Power Systems, vol. 39, no. 5, pp. 6372–6385, 2024

work page 2024

[16] [16]

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span,

P. Su, Y . Zhou, and J. Wu, “Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span,”Journal of Cleaner Production, vol. 428, p. 139350, 2023

work page 2023

[17] [17]

A review of simulation and numerical modeling of electric arc furnace (eaf) and its processes,

M. M. Abadi, H. Tang, and M. M. Rashidi, “A review of simulation and numerical modeling of electric arc furnace (eaf) and its processes,” Heliyon, vol. 10, no. 11, 2024

work page 2024

[18] [18]

Enhancing flexibility in wind-powered hydrogen production systems through coordinated electrolyzer operation,

Z. Bai, W. Hao, Q. Li, R. Yan, B. Ding, W. Shao, L. Gao, T. Jiang, Y . Wang, and C. Wen, “Enhancing flexibility in wind-powered hydrogen production systems through coordinated electrolyzer operation,”Advances in Applied Energy, p. 100228, 2025

work page 2025

[19] [19]

Cost-effective scheduling of steel plants with flexible eafs,

X. Zhang, G. Hug, and I. Harjunkoski, “Cost-effective scheduling of steel plants with flexible eafs,”IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 239–249, 2016

work page 2016

[20] [20]

Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry,

J. Wang, Q. Wang, and W. Sun, “Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry,”Applied Energy, vol. 342, p. 121178, 2023

work page 2023

[21] [21]

Models and methods for planning and scheduling in iron and steel making: Review and prospects,

N. M. Matsveichuk, Y . N. Sotskov, and L. Sun, “Models and methods for planning and scheduling in iron and steel making: Review and prospects,”International Journal of Chemical Engineering and Materials, vol. 3, pp. 145–161, 2024

work page 2024

[22] [22]

Energy and environmental performance of iron and steel industry in real-time demand response: A case of hot rolling process,

X. Liu, W. Sun, T. Chen, X. Xu, and T. Huang, “Energy and environmental performance of iron and steel industry in real-time demand response: A case of hot rolling process,”Applied Energy, vol. 389, p. 125717, 2025

work page 2025

[23] [23]

Integrated scheduling of hot rolling production planning and power demand response considering order constraints and tou price,

F. Gong, S. Chen, S. Tian, J. Qin, H. Zhang, B. Sun, J. Yuan, L. Jiang, Y . Xu, and Y . Wang, “Integrated scheduling of hot rolling production planning and power demand response considering order constraints and tou price,”IET Generation, Transmission & Distribution, vol. 16, no. 14, pp. 2840–2851, 2022

work page 2022

[24] [24]

Dynamic process operation under demand response–a review of methods and tools,

E. Esche and J.-U. Repke, “Dynamic process operation under demand response–a review of methods and tools,”Chemie Ingenieur Technik, vol. 92, no. 12, pp. 1898–1909, 2020

work page 1909

[25] [25]

Flexibility analysis for demand-side management in large-scale chemical processes: An ethylene oxide production case study,

B. Bruns, A. Di Pretoro, M. Gr ¨unewald, and J. Riese, “Flexibility analysis for demand-side management in large-scale chemical processes: An ethylene oxide production case study,”Chemical Engineering Science, vol. 243, p. 116779, 2021

work page 2021

[26] [26]

Scheduling chemical processes for frequency regulation,

J. I. Otashu and M. Baldea, “Scheduling chemical processes for frequency regulation,”Applied Energy, vol. 260, p. 114125, 2020

work page 2020

[27] [27]

Energy-carbon comprehensive efficiency evaluation of a hydrogen metallurgy system with low-temperature waste heat recovery,

Q. Ji, L. Cheng, Y . Zhou, Z. Liang, F. Shi, J. Zhang, and K. Li, “Energy-carbon comprehensive efficiency evaluation of a hydrogen metallurgy system with low-temperature waste heat recovery,”Applied Energy, vol. 401, p. 126646, 2025

work page 2025

[28] [28]

A novel fractional programming-based planning model for 100% renewable poly-generation of electricity and methanol,

Z. Yu, Y . Chi, J. Lin, F. Liu, Y . Song, and F. You, “A novel fractional programming-based planning model for 100% renewable poly-generation of electricity and methanol,”IEEE Transactions on Sustainable Energy, 2025

work page 2025

[29] [29]

Grid- aware real-time dispatch of microgrid with generalized energy storage: A prediction-free online optimization approach,

K. Huang, L. Cheng, N. Qi, D. W. Gao, A. Mujeeb, and Q. Guo, “Grid- aware real-time dispatch of microgrid with generalized energy storage: A prediction-free online optimization approach,”IEEE Transactions on Smart Grid, 2025

work page 2025