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National energy systems require flexibility to accommodate increasing amounts of variable renewable energy. This flexibility can be provided by demand-side management (DSM) from industry. However, the flexibility potential depends on the characteristics of each industrial process. The enormous diversity of industrial processes makes it challenging to evaluate the total flexibility provision from industry to sector-coupled energy systems. In this work, we quantify the maximum cost reductions due to industrial DSM in the net-zero sector-coupled Swiss energy system, and the relationship between cost reductions and various industrial process characteristics. We analyze the flexibility of industrial processes using a generic, process-agnostic model. Our results show that industrial DSM can reduce total energy system costs by up to 4.4%, corresponding to 20% of industry-related energy costs. The value of flexibility from industrial DSM depends not only on the process characteristics but also on the system’s flexibility alternatives, particularly for flexibility over seasonal time horizons. As one specific option for industrial DSM, we find that thermal energy storage (TES) technologies available today could realize between 28% and 61% of the maximum cost reductions from industrial DSM, making TES a promising DSM solution and showing that industrial DSM is an accessible and cost-effective flexibility option.

Summary: The energy transition is a multinational challenge to mitigate climate change, with a joint reduction target for greenhouse gas emissions. Simultaneously, each country is interested in minimizing its own energy supply cost. Still, most energy system models neglect national interests when identifying cost-optimal transition pathways. We design the European energy system transition until 2050, considering competition between countries in a shared electricity and carbon market using bilevel optimization. We find that national objectives substantially impact the transition pathway: Compared to the model solved using the common centralized optimization, the overall installed capacity increases by just 3% when including national interests. However, the distribution of the installed capacity changes dramatically by more than 40% in most countries. Our results underline the risk of miscalculating the need for national capacity expansion when neglecting stakeholder representation in energy system models and demonstrate the need for cooperation for an efficient energy transition.

The production of hydrogen, and the refining of hydrocarbons, are significant contributors to the CO2 emissions of the chemicals industry. Coupled electrification of these processes has the potential to reduce total emissions by up to 39% even when using the electricity mix available today.

Several theoretical studies have predicted that refrigerant mixtures with glides of more than 20 K can yield COP improvements in heat pumps for operating conditions where the temperature difference between the heat source and heat sink is large, but experimental validations and quantifications are scarce. The application of high-glide mixtures (>20 K) in industrial heat pumps in the field is, therefore, still hampered by concerns about the behavior and handling of the mixtures. This study experimentally investigates hydrocarbon (HC) mixtures R-290/600 (propane/butane) and R-290/601 (propane/pentane) and compares them to previously tested mixtures of synthetic refrigerants. Comprehensive evaluations are presented regarding COP, compressor performance, pressure drop, heat transfer, and the possibility of inline composition determination. The mixtures were tested over a range of compositions at a source inlet temperature of 60 °C and a sink outlet temperature of 100 °C, with the heat sink and heat source temperature differences controlled to 35 K. R-290/601 at a mass composition of 70%/30% was found as the best mixture with a COP improvement of 19% over R-600 as the best pure fluid. The overall isentropic compressor efficiency was similar for HC and synthetic refrigerants, given equal suction and discharge pressures. Pressure drops in heat exchangers and connecting lines were equal for synthetic and HC mixtures at equal mass flow rates. This allows higher heating capacities of HC mixtures at a given pressure drop (mass flow rate) due to their wider vapor dome. A previously developed evaporator heat transfer correlation for synthetic refrigerant mixtures was applicable to the HC mixtures. A condenser heat transfer correlation previously fitted for synthetic refrigerants performed significantly worse for HC mixtures. Composition determination during operation and without sampling was possible with a deviation of at most 0.05 mass fraction using simple temperature and pressure measurements and REFPROP for thermodynamic property calculations. Overall, high-glide HC mixtures, just like mixtures of synthetic refrigerants, showed significant COP improvements for specific operating conditions despite a decreased heat transfer coefficient. Potential problems like composition shift or poor compressor performance were not encountered. As a next step, testing high-glide mixtures in pilot-plant installations is recommended.

The growing need for balancing power combined with the shutdown of conventional power plants requires new balancing-power providers. In this context, industrial energy systems are particularly promising. However, the main task of industrial energy systems is to provide various energy forms. For this purpose, they operate interconnected units to maximize efficiency, but the interconnected operation also increases complexity, limiting flexibility due to the need to supply fixed demands. Energy storage can increase the flexibility of current and future industrial energy systems, thus enhancing the potential for sector coupling within the overall energy system at a low cost. To improve the flexibility of industrial energy systems, we propose a design optimization framework that accounts for investment in energy storage and for the provision of balancing power. Since the request of balancing power is uncertain, we present a stochastic program for the balancing-power market and propose two ways to model storage that both derive feasible storage operations while being computationally efficient. In a case study of a multi-energy system, cost savings between 6% and 17% can be achieved by increasing flexibility for participation in the balancing-power market with investment in heat storage. The sensitivity analysis identifies heat storage as particularly advantageous for heat-driven energy systems. Our method combines long-term investment decisions with short-term operational uncertainties to identify optimal investment decisions, which enhance the energy system’s flexibility for the provision of balancing power.

Summary: Blue and green ammonia production have been proposed as low-carbon alternatives to emissions-intensive conventional ammonia production. Although much attention has been given to comparing these alternatives, it is still not clear which process has better environmental and economic performance. We present a techno-economic analysis and full life cycle assessment to compare the economics and environmental impacts of blue and green ammonia production. We address the importance of time horizon in climate change impact comparisons by employing the Technology Warming Potential, showing that methane leakage can exacerbate the climate change impacts of blue ammonia in short time horizons. We represent a constrained renewable electricity availability scenario by comparing the climate change impact mitigation efficiency per kWh of renewable electricity. Our work emphasizes the importance of maintaining low natural gas leakage for sustainability of blue ammonia, and the potential for technological advances to further reduce the environmental impacts of photovoltaics-based green ammonia.

Abstract This paper is written in response to the paper “How green is blue hydrogen?” by R. W. Howarth and M. Z. Jacobson. It aims at highlighting and discussing the method and assumptions of that paper, and thereby providing a more balanced perspective on blue hydrogen, which is in line with current best available practices and future plant specifications aiming at low CO2 emissions. More specifically, in this paper, we show that: (i) the simplified method that Howarth and Jacobson used to compute the energy balance of blue hydrogen plants leads to significant overestimation of CO2 emissions and natural gas (NG) consumption and (ii) the assumed methane leakage rate is at the high end of the estimated emissions from current NG production in the United States and cannot be considered representative of all‐NG and blue hydrogen value chains globally. By starting from the detailed and rigorously calculated mass and energy balances of two blue hydrogen plants in the literature, we show the impact that methane leakage rate has on the equivalent CO2 emissions of blue hydrogen. On the basis of our analysis, we show that it is possible for blue hydrogen to have significantly lower equivalent CO2 emissions than the direct use of NG, provided that hydrogen production processes and CO2 capture technologies are implemented that ensure a high CO2 capture rate, preferably above 90%, and a low‐emission NG supply chain.

New study concludes that environmental tradeoffs of direct air capture and sequestration technologies are linked to the energy system in which they will operate, and their deployment should not equate to a relaxation of decarbonization or resource use efficiency targets.

Optimization models can support decision-makers in the synthesis and operation of multi-sector energy systems. To identify the optimal design and operation of a low-carbon system, we need to consider high temporal and spatial variability in the electricity supply, sector coupling, and environmental impacts over the whole life cycle. Incorporating such aspects in optimization models is demanding. To avoid redundant research efforts and enhance transparency, the developed models and used data sets should be shared openly. In this work, we present the SecMOD framework for multi-sector energy system optimization incorporating life-cycle assessment (LCA). The framework allows optimizing multiple sectors jointly, ranging from industrial production and their linked energy supply systems to sector-coupled national energy systems. The framework incorporates LCA to account for environmental impacts. We hence provide the first open-source framework to consistently include a holistic life-cycle perspective in multi-sector optimization by a full integration of LCA. We apply the framework to a case-study of the German sector-coupled energy system. Starting with few base technologies, we demonstrate the modular capabilities of SecMOD by the stepwise addition of technologies, sectors and existing infrastructure. Our modular open-source framework SecMOD aims to accelerate research for sustainable energy systems by combining multi-sector energy system optimization and life-cycle assessment.

The mitigation of climate change requires research, development, and deployment of new technologies that are not only economically viable but also environmentally benign. Systematic and continuous technology assessment from early technology maturity onwards allows assessment practitioners to identify economic and environmental characteristics. With this information, decision-makers can focus time and resources on the most promising technologies. A broad toolset for technology assessment exists—stretching from the well-established life cycle assessment (LCA) methodology to more loosely defined techno-economic analysis (TEA) methods and the increasingly popular principles of technology maturity assessment such as the concept of technology readiness levels (TRL). However, current technology assessment practice faces various challenges at early stages, resulting in a potential mismatch of study results and stakeholders' needs and an escalation of assessment effort. In this practice review, we outline current challenges in the interplay of LCA, TEA, and TRL and present best practices for assessing early-stage climate change mitigation technologies in the field of carbon capture and utilization (CCU). The findings help practitioners systematically identify the TRL of a technology and adapt technology assessment methodologies accordingly. We highlight the methodological challenges for practitioners when adapting the goal and scope, identifying benchmark technologies, creating a comprehensive inventory, comparing early stage to commercial stage, ensuring clarity of recommendations for decision-making under high uncertainty, and streamlining conventional LCA and TEA assessment approaches and provide actionable recommendations. Overall, this work contributes to identifying promising technologies faster and more systematically, accelerating the development of new technologies for climate change mitigation and beyond.

The optimal operation of multi-energy systems requires optimization models that are accurate and computationally efficient. In practice, models are mostly generated manually. However, manual model generation is time-consuming, and model quality depends on the expertise of the modeler. Thus, reliable and automated model generation is highly desirable. Automated data-driven model generation seems promising due to the increasing availability of measurement data from cheap sensors and data storage. Here, we propose the method AutoMoG 3D (Automated Model Generation) to decrease the effort for data-driven generation of computationally efficient models while retaining high model quality. AutoMoG 3D automatically yields Mixed-Integer Linear Programming models of multi-energy systems enabling efficient operational optimization to global optimality using established solvers. For each component, AutoMoG 3D performs a piecewise-affine regression using hinging-hyperplane trees. Thereby, components can be modeled with an arbitrary number of independent variables. AutoMoG 3D iteratively increases the number of affine regions. Thereby, AutoMoG 3D balances the errors caused by each component in the overall model of the multi-energy system. AutoMoG 3D is applied to model a real-world pump system. Here, AutoMoG 3D drastically decreases the effort for data-driven model generation and provides an accurate and computationally efficient optimization model.

National energy models provide decarbonization strategies. Most national energy models focus on costs and greenhouse gas emissions only. However, this focus carries the risk that burdens shift to other environmental impacts. Energy models have therefore been extended by life-cycle assessment (LCA). Furthermore, deep decarbonization is only possible by targeting all high-emission sectors. Thus, we present a holistic national energy model that includes high-emission sectors and LCA. The model provides detailed environmental impacts for electricity, heat, and transport processes in Germany for meeting the climate targets up to 2050. Our results show that renewable energies and storage are key technologies for decarbonized energy systems. Furthermore, sector coupling is crucial and doubles electricity demand. Our LCA shows that environmental impacts shift from operation to infrastructure highlighting the importance of an impact assessment over the full life cycle. Decarbonization leads to many environmental cobenefits; however, it also increases freshwater ecotoxicity and depletion of metal and mineral resources. Thus, holistic planning of decarbonization strategies should also consider other environmental impacts.

Carbon Capture and Utilization (CCU) is an emerging field proposed for emissions mitigation and even negative emissions. These potential benefits need to be assessed by the holistic method of Life Cycle Assessment (LCA) that accounts for multiple environmental impact categories over the entire life cycle of products or services. However, even though LCA is a standardized method, current LCA practice differs widely in methodological choices. The resulting LCA studies show large variability which limits their value for decision support. Applying LCA to CCU technologies leads to further specific methodological issues, e.g., due to the double role of CO2 as emission and feedstock. In this work, we therefore present a comprehensive guideline for LCA of CCU technologies. The guideline has been development in a collaborative process involving over 40 experts and builds upon existing LCA standards and guidelines. The presented guidelines should improve comparability of LCA studies through clear methodological guidance and predefined assumptions on feedstock and utilities. Transparency is increased through interpretation and reporting guidance. Improved comparability should help to strengthen knowledge-based decision-making. Consequently, research funds and time can be allocated more efficiently for the development of technologies for climate change mitigation and negative emissions.

Two-stage synthesis problems simultaneously consider here-and-now decisions (e.g., optimal investment) and wait-and-see decisions (e.g., optimal operation). The optimal synthesis of energy systems reveals such a two-stage character. The synthesis of energy systems involves multiple large time series such as energy demands and energy prices. Since problem size increases with the size of the time series, synthesis of energy systems leads to complex optimization problems. To reduce the problem size without loosing solution quality, we propose a method for time-series aggregation to identify typical periods. Typical periods retain the chronology of time steps, which enables modeling of energy systems, e.g., with storage units or start-up cost. The aim of the proposed method is to obtain few typical periods with few time steps per period, while accurately representing the objective function of the full time series, e.g., cost. Thus, we determine the error of time-series aggregation as the cost difference between operating the optimal design for the aggregated time series and for the full time series. Thereby, we rigorously bound the maximum performance loss of the optimal energy system design. In an initial step, the proposed method identifies the best length of typical periods by autocorrelation analysis. Subsequently, an adaptive procedure determines aggregated typical periods employing the clustering algorithm k-medoids, which groups similar periods into clusters and selects one representative period per cluster. Moreover, the number of time steps per period is aggregated by a novel clustering algorithm maintaining chronology of the time steps in the periods. The method is iteratively repeated until the error falls below a threshold value. A case study based on a real-world synthesis problem of an energy system shows that time-series aggregation from 8,760 time steps to 2 typical periods with each 2 time steps results in an error smaller than the optimality gap of the synthesis problem (2%). This corresponds to a reduction of the number time steps and thus a reduction of the size of the synthesis problem by a factor of 1,000 with excellent accuracy in cost estimation. Thus, the proposed method enables an efficient and accurate synthesis of energy systems.