A methodology for bottom-up modelling of energy transitions in the industry sector: The FORECAST model

Fleiter, Tobias; Rehfeldt, Matthias; Herbst, Andrea; Elsland, Rainer; Klingler, Anna-Lena; Manz, Pia; Eidelloth, Stefan

doi:10.1016/j.esr.2018.09.005

Cited by 85 publications

(37 citation statements)

References 39 publications

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“…The methodology enables the simulation of industrial transformation using detailed technology mix paths. Thus, as a result of the technology mix module in SmInd it is possible to support political decision-making and an efficient transition to greenhouse gas neutral industry [14]. The results show the implied transformation speed on the basis of natural reinvestment cycles.…”

Section: Discussionmentioning

confidence: 91%

“…(1) Measure identification number (2) Electricity change 3Fuel change (4) Application factor (5) Lifetime (6) Investment (7) Fixed operating costs (8) Measure category (9) Substitution process of process route change (10) CO 2 -capture rate (11) Identification number of process or cross-section on which the measure has an effect (12) Initial year measure implementation (13) End year measure implementation (14) Economic branch name, on which the measure has an effect (15) Process name on which the measure has an effect (16) Identification number substitution process of process route change (17) Identification number economic branch (18) Natural replacement rate (19) Function of replacement rate (20) Static GHG abatement costs (21) Annuity measure investment (22) Non-annuity fixed operating costs (23) Annuity measure costs (24) Yearly measure implementation amount (25) Cumulated measure implementation amount (26) Energy carrier change reference (27) Energy carrier change substitution…”

Section: Fundingmentioning

confidence: 99%

“…The implementation order in currently existing scenarios is often estimated exogenously by experts [4,13]. A few industrial models make it possible to compute technology-independent, consistent transformation paths that take interdependencies between individual small-scale GHG abatement measures into account [14]. With the many and complex ways in which GHG abatement measures affect industrial production volumes, energy the conclusion that GHG abatement measures in the Prognos industrial model are only implemented at branch-and not process-level.…”

Section: Introductionmentioning

confidence: 99%

“…When a process route is changed, the following equation is used for complete implementation due to the natural production fluctuations (see Equations (15) and (16)). The fluctuating production volume after the complete implementation of the process route change measure is distributed evenly over the remaining process routes via a production volume module:…”

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confidence: 99%

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Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry

Hübner

2020

Energies

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The dynamic bottom-up modelling of greenhouse gas (GHG) abatement measures in industry makes it possible to derive consistent transformation paths on the basis of heterogeneous, process-specific developments. The main focus is on the development of a transparent methodology for small-scale modelling and combination of individual GHG abatement measures. In this way, interactions between GHG abatement measures are taken into account when deriving industrial transformation paths. The presented three-part methodological approach comprises the preparation (1) and implementation (2) of GHG abatement measures as well as the resulting effects on the output parameters (3) in a technology mix module. In order to consider interactions in the measures implementation, year-specific overall measure matrices are created and prioritised based on the GHG abatement costs. Finally, the three-part methodology is tested in a consistent technology mix scenario. The results show that the methodology enables integrated industrial technology mix scenarios with a high level of climate ambition based on a plausible development of energy consumption and emissions. Compared to the reference scenario, the process-and energy-related emissions decrease by 90 million tCO2 (77% of the 1990 level in 2050). The developed methodology and the related technology mix scenario within the framework of the bottom-up industry model SmInd can support strategic decision-making in politics and an efficient transition to a greenhouse gas neutral industry.

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Section: Discussionmentioning

confidence: 91%

Section: Fundingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry

Hübner

2020

Energies

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“…An alternative option would be to couple existing technically detailed bottom-up simulation models, e.g. the FORECAST model [38] to IAMs. However, this can also be challenging as there are many differences in model structures [39], regional breakdown etc.…”

Section: Scope For Adding Bottom-up Detail To Long-term Energy Modelsmentioning

confidence: 99%

The scope for better industry representation in long-term energy models: Modeling the cement industry

et al. 2019

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The paper illustrates the importance of capturing cross-sectoral relationships between industries in IAMs. • Retrofitting and clinker to cement ratio is not sufficiently represented in IAMs. • Retrofitting can offer considerable energy savings in the short-term. • Reducing the clinker content in cement can offer significant energy savings. • Limited future fly ash availability reduces the potential for clinker substitution.

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Decarbonisation of steel production by the low‐carbon DRI‐EAF route

Gervásio,

da Silva

2023

ce papers

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The effects of climate change, induced by global warming, are leading to huge economic, environmental and social impacts worldwide. The world is considered to be in a state of climate emergency, and urgent decarbonisation measures are required to control the temperature rise and mitigate such effects. In this context, EU targets for decarbonisation are to reduce, at least 55% of GHG emissions until 2030, and then to become climate‐neutral, by 2050. The steel industry, being among the biggest producers of CO2, is committed to fulfil this goal. However, the production of 1 tonne of steel, in the coal‐dependent BF/BOF route, can lead to about 2.3 tonnes of CO2, and this route is responsible for more than half of all steel produced in EU. Therefore, finding alternative breakthrough technologies to reduce CO2 emissions is a priority for the steel industry, and different decarbonisation strategies are being implemented in different European plants. Such strategies include the optimization of the DRI and EAF route. This paper focused on this strategy and it was found that the combined route DRI and EAF, based on green hydrogen and carbon free electricity, may reduce CO2 emissions in about 98% compared to the BF/BOF route.

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A methodology for bottom-up modelling of energy transitions in the industry sector: The FORECAST model

Cited by 85 publications

References 39 publications

Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry

Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry

The scope for better industry representation in long-term energy models: Modeling the cement industry

Decarbonisation of steel production by the low‐carbon DRI‐EAF route

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