The industrial sector accounts for nearly a quarter of US greenhouse gas emissions, ranking third after transportation and electricity generation [1]. The Energy Information Administration forecasts that by 2024, industrial emissions could overtake electric generation emissions as the electric sector deploys additional renewable generation sources, even though industry will also likely electrify some processes [2]. Specific heavy industries, such as iron and steel production, present significant challenges and are often labeled “difficult to decarbonize” [3]. Iron and steel comprise approximately 95% of US metal production and 10% of US industrial CO2 emissions [1] [3].

This study investigates the role of hydrogen as a decarbonization strategy for the US iron and steel sector in the presence of an economy-wide net zero CO2 emissions target. We use Temoa, an energy system optimization model, to simulate decarbonization pathways for US steel production in the context of economy-wide decarbonization. We simulate several pathways for iron and steel decarbonization, including carbon capture, electrification, and hydrogen. We seek to understand (1) how provisions of the IRA could affect the deployment of iron and steel decarbonization technologies, (2) what iron and steel decarbonization technologies contribute to a least-cost net-zero emission energy system, and (3) how the availability of particular technologies changes the contribution of the iron and steel sector to economy-wide decarbonization.

Our analysis shows that hydrogen-based direct reduced iron (H2DRI) provides a cost-effective decarbonization strategy only under a relatively narrow set of conditions. Using today’s best estimates of the capital and variable costs of alternative decarbonized iron and steelmaking technologies in a US economy-wide simulation framework, we find that carbon capture technologies can achieve comparable decarbonization levels by 2050, with greater cumulative emissions reductions from iron and steel production. Simulations suggest hydrogen contributes to economy-wide decarbonization, but H2DRI is not the preferred use case for hydrogen under most scenarios. The average abatement cost for US iron and steel production could be as low as $70/tonne CO2; the cost with H2DRI rises to over $500/tonne CO2. We also find that IRA tax credits are insufficient to spur hydrogen use in steelmaking and that a green steel production tax credit would need to be as high as $300/tonne to lead to sustained H2DRI use.

[1] EPA, “Inventory of US Greenhouse Gas Emissions and Sinks: 1990-2020,” US Environmental Protection Agency, 430-R-22–003, 2022. [Online]. Available: https://www.epa.gov/system/files/documents/2022-04/us-ghg-inventory-2022-main-text.pdf

[2] EIA, “Annual Energy Outlook 2022 Table 18: Energy-Related Carbon Dioxide Emissions by Sector and Source,” Energy Information Administration, Washington, DC., 2022. [Online]. Available: https://www.eia.gov/outlooks/aeo/data/browser/#/?id=17-AEO2022&region=1-0&cases=ref2022&start=2020&end=2050&f=A&linechart=ref2022-d011222a.6-17-AEO2022.1-0~ref2022-d011222a.13-17-AEO2022.1-0~ref2022-d011222a.20-17-AEO2022.1-0~ref2022-d011222a.26-17-AEO2022.1-0~ref2022-d011222a.33-17-AEO2022.1-0&map=ref2022-d011222a.4-17-AEO2022.1-0&ctype=linechart&sourcekey=0

[3] S. J. Davis et al., “Net-zero emissions energy systems,” Science, vol. 360, no. 6396, p. eaas9793, Jun. 2018, doi: 10.1126/science.aas9793.

Thailand has established a target of carbon neutrality by 2050. Reaching this goal will require coordinated efforts across the energy system at both the national and subnational levels. Robust decarbonization scenarios incorporating current plans and targets, additional measures needed, and trade-offs between strategies can help stakeholders make informed decisions in the face of uncertainty. Through iterative engagement with decision makers, we develop and analyze carbon neutral scenarios for Thailand that incorporate Bangkok’s role using a global integrated assessment model. We find that Thailand can reach carbon neutrality through power sector decarbonization, energy efficiency and widespread electrification in the buildings, industry, and transportation sectors, and advanced technologies including carbon capture and storage. Negative emissions technologies will also be needed to offset Thailand and Bangkok’s hardest-to-abate CO2 emissions. Bangkok, as a major population and economic center, contributes significantly to Thailand’s energy demand and emissions and can therefore play an important role in climate change mitigation. Accordingly, our results underscore the importance of subnational climate action in meeting Thailand’s carbon neutral goal. These insights can help energy system stakeholders identify priorities, consider tradeoffs, and make decisions that will impact Bangkok and Thailand’s long-term climate change mitigation potential. We also note that Thailand's carbon neutral 2050 pathway is based on a targeted emissions reduction of 40% by 2030 as stated in Thailand’s NDC. However, Thailand’s unconditional NDC is only 30% in 2030. Thus, Thailand may need international support to achieve carbon neutrality by 2050 through the pathways investigated here.

Background

The Information and Communication Technology (ICT) industry accounts for about 330M tons Green House Gase (GHG) emissions annually [1]. The industry manages its GHG impact with a move to 100% renewable energy, and aggressive acquisition of Renewable Energy Certificates (RECs) [2] through Power Purchase Agreements (PPAs) [3], [4]. Variability in renewable energy sources and their dislocation from the actual area of operational demand, made possible through the PPAs, make net GHG emissions intractable. This problem has been identified by the ICT industry [1].

Google, Microsoft, and Iron Mountain have announced 2030-2040 targets to source and match zero-carbon electricity on a 24/7 basis within each grid where demand is located [1]. The goal is to bring time-space coherence of energy supply with demand, to make their GHG emissions tractable. This would require tools to track the switching of energy to renewable sources in time, spatial location on the transmission grid, energy storage in the grid, to the net demand delivered by renewable sources in each 24-hour window of operation. However, such tools are not readily available.

Motivation

The ICT industry is subject to Environmental, Social, and corporate Governance (ESG) audits [5]. ESG scores that drive stock valuations of the industry [6] are based on Corporate Sustainability Reports (CSR)s. GHG protocol [7] is the de facto standard for the energy and emission footprint reporting in the annual CSRs. Gaps in emission targets are covered by the acquisition of RECs through PPAs. PPAs push emission commitments upstream to the energy/utility grid suppliers, dislocating the time-space coherence from the point of energy demand. The emissions footprint published in the CSRs therefore do not represent realistic values.

The CO2 Emission Factor (CEF), the total CO2 emissions/total energy [8], is a relatively new emissions metric the industry uses. CEF can be accounted down to ‘0’ with sufficient PPA acquisition. This further distorts the actual emissions footprint in the industry’s operational metrics.

Our Contribution

Open-Source energy Modelling System (OSeMOSYS) [9], [10] together with Climate Land Energy Water Systems (CLEWs) [11] provide a starting framework to model energy supply-demand and understand impacts of land water usage on the climate i.e. GHG emissions. Our contribution will extend the OSeMOSYS-CLEWs framework, develop the plug-ins needed and provide a comprehensive set of tools to track energy supply sources with demand, in time, space, energy buffering or storage in the transmission grid. Our work will provide data on how much of the demand was met from renewable sources in each 24-hour operational window of the ICT infrastructure. The end-to-end integrated datacenter NetZero (dcNZ) emissions tool set we develop will be open sourced for ready availability to the industry.

We will use our work to extend the GHG protocol standards to enable the ICT industry to publish dcNZ measurements of absolute emission, increase/decrease in carbon footprint, effect on climate, land, energy, and water systems in their annual CSRs. The time-space correlated emissions measurements generated by our dcNZ tool set will provide realistic CEF values, reducing distortions in operational metrics.

Energy system optimization models serve as analytical frameworks for strategizing energy infrastructure development, integrating economic, technical, and environmental dimensions. They facilitate optimal resource allocation, technology selection, and policy analysis by simulating the energy system from cradle to grave – from the extraction and processing of raw materials all the way to the technologies meeting energy service demands. These models support robust decision-making, allowing stakeholders to better understand system behaviour, evaluate variable renewable integration, and ensure system reliability under diverse scenarios. Crucial for sustainable development, they provide insights into cost-effective, reliable, and environmentally sound energy pathways.

In a focused extension, power system planning models specifically address the unique challenges in the electricity sector, including maintaining equilibrium between generation and fluctuating demand, integrating variable renewable energy sources, and ensuring grid stability. By delving into the dynamics of hourly generation, grid constraints, and reliability assessments, these models offer an in-depth analysis, bridging the broader implications of energy systems with the specific operational details of the power system.

The Model Intercomparison Project, an ongoing project led by researchers at the Environmental Defense Fund, critically evaluates four leading open-source power system planning models: GenX, Switch, Temoa, and USENSYS. These models differ in various respects, and there is a need to gain insight into how these individual energy system models compare. To fully unlock the decision-making usefulness of these next-generation power system models, a nuanced understanding is needed to determine how the evaluations produced by these models vary and what features of the models explain such variance.

The results from this study will highlight the structural differences between these leading open-source models, providing model users and policymakers with a better understanding of their relative strengths and weaknesses. Additionally, by running each model across a variety of deep-decarbonization scenarios, the ensemble approach will offer policymakers insights into which aspects of the transition are unanimous across models and which aspects show a significant degree of variance. By providing a clear, empirical basis, the project enhances the robustness of decisions in energy policy development and system planning.

This presentation will introduce the audience to energy and power system planning models, provide motivation for the Model Intercomparison Project, highlight the project's key findings, and discuss the policy implications stemming from the study. The goal is to acquaint the audience with the field of energy system modeling and to provide a clear example of how these models are influencing policy implications.

Project Partners by Model:

- USENSYS: Environmental Defense Fund

- TEMOA: Carnegie Melon University; NC State University; Sutubra Research

- GenX: Priceton

- SWITCH: The University of Hawaii; UC San Diego