Time- and component-resolved energy system model of an electric steel mill

Dock, Johannes; Janz, Daniel; Weiss, Jakob; Marschnig, Aaron; Kienberger, Thomas

doi:10.1016/j.clet.2021.100223

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…As discussed in Section 1.1, understanding these times is crucial due to the EAF's influence on downstream processes. Notably, the work of Dock et al (2021) [19] is an exception, generating time-resolved synthetic load profiles of various electric steel mill components, including an EAF. However, for the day-ahead forecast of energy consumption and especially for the intra-hour forecast of energy demand, the synthetic load profile generation is not sufficiently accurate enough.…”

Section: Research Needsmentioning

confidence: 99%

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

Zawodnik,

Schwaiger,

Sorger

et al. 2024

Energies

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The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings.

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Section: Research Needsmentioning

confidence: 99%

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

Zawodnik,

Schwaiger,

Sorger

et al. 2024

Energies

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View full text Add to dashboard Cite

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“…For household, agriculture, and public and private service power consumption standardized load profiles (SLP) H0, L0, and G0 are used [57]. The reactive power is considered using literature and empiric values [58][59][60]. A cos(ϕ) of 0,98 is used.…”

Section: Temporally Resolved Consumption Datamentioning

confidence: 99%

Modelling and Simulation/Optimization of Austria’s National Multi-Energy System with a High Degree of Spatial and Temporal Resolution

Greiml¹,

Fritz²,

Steinegger³

et al. 2022

Energies

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The European Union and the Austrian government have set ambitious plans to expand renewable energy sources and lower carbon dioxide emissions. However, the expansion of volatile renewable energy sources may affect today’s energy system. To investigate future challenges in Austria’s energy system, a suitable simulation methodology, i.e., temporal and spatially resolved generation and consumption data and energy grid depiction, is necessary. In this paper, we introduce a flexible multi-energy simulation framework with optimization capabilities that can be applied to a broad range of use cases. Furthermore, it is shown how a spatially and temporally resolved multi-energy system model can be set up on a national scale. To consider actual infrastructure properties, a detailed energy grid depiction is considered. Three scenarios assess the potential future energy system of Austria, focusing on the power grid, based on the government’s renewable energy sources expansion targets in the year 2030. Results show that the overwhelming majority of line overloads accrue in Austria’s power distribution grid. Furthermore, the mode of operation of flexible consumer and generation also affects the number of line overloads as well.

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“…The remelting of steel scrap in the EAF is not only the most energy-intensive process step in electric steelmaking [9] but also causes significant oxygen consumption: During the melting phase, chemical energy input by the introduction of oxygen, carbon, and natural gas reduces the electric energy demand and tap-to-tap time. The following refining step aims at dephosphorization, decarburization, and temperature adjustment of the steel melt.…”

Section: Eaf Steel Production Process and Oxygen Demandmentioning

confidence: 99%

“…The load profiles for electricity demand and for the demand for natural gas and oxygen consumed in the steel mill are obtained from an energy system model developed and extended by the authors in previous studies [6,9]. This model describes a steel mill that produces heat-treated high-alloyed steel ingots from 100% steel scrap using a 65 t alternating current (AC) EAF.…”

Section: Load Profilesmentioning

confidence: 99%

“…Typical operational costs for the implemented equipment are fixed at 1 to 3% of CAPEX per year [35,46]. Investment costs for the PSA, electrolysis, and methanation plants and the storage tanks and compressors are allocated to the assessment period by applying the annuity method (9). The annuity (A CAPEX ) is calculated by multiplying the investment costs for the individual assets (I) by an annuity factor, which depends on the interest rate (i) and the depreciation period (n).…”

Section: Equipment Costmentioning

confidence: 99%

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Provision of Demand-Side Flexibility through the Integration of Power-to-Gas Technologies in an Electric Steel Mill

et al. 2022

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EAF steelmaking based on renewable electricity allows for low-CO2 steel production. However, the increased integration of volatile renewable energies into the energy system requires the provision of flexibility options. In view of the substantial oxygen consumption in the steel mill, flexible on-site generation and storage holds a significant potential for demand-side management. The utilization of by-product oxygen from an electrolysis plant not only contributes to load flexibility but also generates a climate-neutral fuel. In the present study, different process layouts are developed based on state-of-the-art technologies. The proposed supply systems for oxygen, hydrogen, and synthetic natural gas are subjected to design and operational optimization and assessed with respect to the overall demand-side flexibility, carbon dioxide emission reduction, and economic viability.

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Time- and component-resolved energy system model of an electric steel mill

Cited by 7 publications

References 34 publications

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

Modelling and Simulation/Optimization of Austria’s National Multi-Energy System with a High Degree of Spatial and Temporal Resolution

Provision of Demand-Side Flexibility through the Integration of Power-to-Gas Technologies in an Electric Steel Mill

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