The unprecedented expansion of Artificial Intelligence is adding increasing electricity demand to Europe's power system. While incumbent plans pursue a net-zero future by 2050, they fail to consider the implications of large-scale AI-based data centres. In this study, a spatially explicit optimisation model is developed to assess how hyperscale data centres may reshape energy infrastructure investment, and emissions trajectories, across different AI demand growth scenarios. The results indicate that, after 2030, AI capacity deployment increasingly shifts toward regions with the ability to expand nuclear and gas-based generation, as firm and flexible power sources are essential for supporting the deployment of high-capacity AI data centres. By 2050, AI-driven electricity demand under high growth scenarios may reach up to 450 TWh, corresponding to 7% of total Europe's demand, with installed AI capacity reaching approximately 85 GW. This additional load leads to an increase of nearly 25 M... [more]

Cost reduction of green ammonia production is critical to advancing the hydrogen-ammonia economy, as ammonia capable of cost-effective storage and transportation is a promising hydrogen carrier and energy carrier to alleviate the intermittency and geographical limitations of renewables. Optimisation and techno-economic assessment based on rigorous model are essential to accurately investigate techno-economic feasibility and fully explore optimisation potential. This work estimates the levelized cost of ammonia (LCOA) of an integrated system including a hydrogen generation process employing Proton Exchange Membrane (PEM) water electrolysis, a nitrogen generation process from flue gas recovery, and an ammonia synthesis process based on Haber-Bosch Process. To enhance the reliability of LCOA, detailed equipment sizing and costing is conducted according to stream data from rigorous modelling in Aspen Plus. A novel optimisation strategy is proposed to enhance the computational robustness by... [more]

The increasing deployment of variable renewable energy (VRE) is essential for achieving a sustainable society; however, its inherent variability poses challenges for maintaining a stable electricity supply. Vehicle-to-grid (V2G) technology enables bidirectional electricity exchange between electric vehicles (EVs) and the power grid and can enhance the utilization of renewable electricity by charging EVs during periods of VRE output curtailment. This study developed a regional V2G system model and evaluated its potential through energy flow simulations and life cycle assessment (LCA). The model explicitly considered hourly operation schedules of individual EVs, the spatial distribution of V2G infrastructure, and minimum output constraints of thermal power generation. The number of EVs is assumed to increase to up to 10, 000 units. In the energy flow simulations, EV charging and discharging were calculated on an hourly basis over one year. LCA was conducted to assess greenhouse gas (GHG)... [more]

Human activities have already transgressed several planetary boundaries, yet energy system models remain largely focused on greenhouse gas mitigation, reflecting their original purpose of addressing climate change. Recent integrations of Planetary boundary-based Life Cycle Assessment into Energy System Optimisation Models show that even cost-optimal low-carbon pathways systematically violate multiple planetary boundaries, indicating that supply-side decarbonisation alone is insufficient for absolute environmental sustainability. At a 2050 horizon, where energy supply is largely decarbonised and technologies are assumed mature, further impact reductions through techno-economic optimisation become limited, positioning final energy demand as a key remaining lever for restoring feasibility under planetary constraints. To address this gap, we ex-tend an Energy System Optimisation framework coupled with a Planetary Boundary framework by explicitly treating final energy demand as a decision v... [more]

European industry accounts for approximately 20% of total European CO2 emissions, with heat demand representing one of the largest energy consumers. Solar thermal collectors offer an efficient renewable alternative to fossil fuels to cover the heat demand. However, due to the temporal mismatch between the solar thermal generation and process heat generation a thermal storage is needed to maximize the renewable utilization. This article presents a novel optimization framework for integrating an ideal heat storage with solar thermal systems in multiperiod heat exchanger network synthesis. We derive an analytical approach to optimize the heat storage by using physical insights from Pinch Analysis: heat can only charge the storage below the lowest pinch point in a given period and discharge above the highest pinch point. We show both how to do it for a storage of infinite size and of finite size, and that the infinite size storage is much more efficient to solve. The approach is validated... [more]

Early-stage system-level assessment of emerging technologies is essential for achieving climate neutrality and a circular economy; however, such assessments are often constrained by the lack of representative life cycle inventory data. In thermal energy systems, performance strongly depends on scale, making direct application of laboratory- or bench-scale experimental data potentially misleading in life cycle assessment (LCA). This study investigates the influence of experimental scale on system-level evaluation using a zeolite-based thermal energy storage (TES) system as a case study.LCAs were conducted using performance data from laboratory-, bench-, and pilot-scale experiments and compared with predicted commercial-scale performance derived from numerical simulations. The TES system stores waste heat via water vapor desorption from zeolite and generates pressurized steam using a moving-bed with indirect heat exchanging system. Heat recovery ratios of 36%, 50%, and 61% were obtained... [more]

Heavy-duty road transport remains a challenging sector to decarbonize, as full electrification of long-distance trucking is currently constrained by limitations in energy density and charging infrastructure. Alternative fuels such as hydrogen, biodiesel, and e-fuels are thus gaining increasing attention. In parallel, the cement industry is a major source of unavoidable, process-related CO2 emissions, offering an opportunity to use captured industrial CO2 as a feedstock for e-fuel production. This study evaluates the production of e-methanol and Fischer-Tropsch (FT) diesel from captured CO2 at an Austrian cement plant as a base case. Several system configurations are analyzed, including different electricity supply options across Europe and the use of biogenic versus fossil CO2. An integrated framework combining process simulation, techno-economic analysis, and life-cycle assessment is applied to compare both fuel pathways. Results show that the climate impact of e-fuels is highly depen... [more]

The rapid expansion of AI data centers is straining electricity grids alarmingly, forcing data center planners to navigate two-pronged challenges: (1) lengthy interconnection queue delays undermining immediate grid access, and (2) volatile electricity prices that spike dramatically during high demand events. This convergence forces planners to reconsider traditional grid-only strategies. While behind-the-meter (BTM) generation offers a solution, existing research lacks comprehensive frameworks for identifying technology portfolios under combined uncertainties of grid access delays and market volatility. This study develops a two-stage stochastic optimization framework with binary capacity constraints co-optimizing data center location and BTM energy portfolios under these challenges. The model evaluates conventional (gas turbines), renewable (solar, wind, batteries), and emerging technologies (hydrogen fuel cells, small modular reactors) across four progressive scenarios spanning emiss... [more]

Recent geopolitical disruptions and extreme weather events have underscored the importance of resilience in global energy supply chains, particularly for import-dependent economies pursuing ambitious energy transition targets. These events have exposed the limitations of supply chain designs focused solely on cost minimization that lack the flexibility and redundancy required for secure operation under stress. As energy systems evolve toward higher shares of variable renewable energy and increased demand uncertainty, episodic manual re-planning becomes inadequate, highlighting the need for modeling frameworks that integrate predictive modeling, optimization, and control to enable intelligent and adaptive supply-chain design and operations under uncertainty. This work presents a comprehensive data-driven modeling and optimization framework for adaptive energy supply-chain networks under evolving demand. The framework integrates three layers: (i) a machine-learning model for demand forec... [more]

The aviation sector represents one of the most pressing challenges in the energy transition due to its strong reliance on energy-dense liquid fuels and established fuel infrastructure. Sustainable Aviation Fuel (SAF), particularly from agricultural residues, offers a near-term mitigation pathway; however, large-scale adoption is shaped by policy mandates, infrastructure expansion, market price formation, and passenger demand responses. These coupled dynamics are difficult to capture using aggregate or equilibrium-based models. This study develops an agent-based model to analyze SAF transition pathways and applies it to India's civil aviation system. Results show that SAF adoption emerges from the coordination between infrastructure entry, cost learning, and market responses rather than mandate ambition alone. Even moderate mandates fall short of intended adoption levels without timely infrastructure expansion, while aggressive mandates become infeasible under binding supply and price c... [more]

"Green" hydrogen production via polymer electrolyte membrane (PEM) electrolyzers must overcome significant energy penalties and high costs to become competitive in renewables-based energy systems. Adaptive operating strategies for PEM electrolyzers-by dynamically adjusting current density, pressure, and temperature-have demonstrated efficiency improvements in simple energy systems. However, their effectiveness in the context of complex power-to-X energy systems featuring variable downstream synthesis processes remains unclear. This work shows that integrated optimization of PEM electrolyzer operating parameters in conjunction with downstream methanation processes (MP) delivers substantial system-wide efficiency and cost benefits under dynamic hydrogen demand and pressure conditions. To demonstrate this, an equation-oriented process model of a PEM electrolysis system is embedded within a higher-level energy system model to compare sequential optimization (where the electrolyzer adapts t... [more]

This study aims to compare different pathways for achieving CO2 emission reductions during the production of methanol and, subsequently, formaldehyde, i.e., its major derivative. An equation-oriented model of the formaldehyde sector is developed, incorporating a superstructure of various transition pathways including feedstock switching (biomass, biogas, waste), process electrification (Power-to-X), and CO2 capture. The OSMOSE tool is used to evaluate the superstructure and compare the alternative production pathways on the basis of thermodynamic, environmental, and economic key performance indicators for future scenarios (2025 and 2050). Furthermore, to cope with the limitations of predefined pricing scenarios, a parameter sweep is performed, exploring a broader set of economic conditions and seeking to identify the zones of economic optimality associated with each configuration through the solving of a Mixed-Integer Linear Programming cost minimization problem, while generalizing the... [more]

The transition toward low-carbon ammonia production increasingly relies on highly efficient routes for renewable hydrogen generation, with Solid Oxide Electrolysis (SOE) representing a particularly promising solution. SOEs, operating with steam at elevated temperatures, offer intrinsic thermodynamic advantages and are attractive when integrated with processes that can effectively utilize or supply high-grade heat. This context opens the possibility for advanced energy coupling between hydrogen production and the exothermic ammonia synthesis process. This work investigates such an energy integration strategy by simulating a small-scale ammonia plant where hydrogen is produced through an SOE system thermally coupled to the ammonia synthesis loop. Specifically, high-grade heat available in the Haber-Bosch (HB) reactor outlet is recovered via a "Heat Recovery Steam Generator" (HRSG) to provide a substantial fraction (55 wt%) of the steam required by the electrolyzer. The assessment demonst... [more]

Refineries are major sources of direct CO2 emissions, primarily from steam generation, fluid catalytic cracking, and hydrogen production. This study develops a superstructure optimization framework to evaluate the economic and environmental viability of retrofitting existing refineries with small modular nuclear reactors (SMRs) for cogeneration of heat and electricity. A multi-period mixed-integer quadratically constrained program is formulated, simultaneously minimizing the present cost of retrofitting and CO2 emissions over the time horizon. This problem is solved to generate a Pareto frontier via the e-constraint method. Two cases are analyzed for a medium-scale refinery, considering 1) inflexible operation under average annual electricity prices and 2) flexible operation under hourly prices with the possibility of installation of storage devices. Compared to a benchmark without SMRs in the superstructure, allowing their installation leads to reduced costs at lower or comparable emi... [more]

Heat pumps offer the possibility of reducing CO2-emissions in the chemical industry. However, the integration of heat pumps, especially in non-continuous processes, faces several challenges. Energy storage facilitates a way to enhance heat integration by providing a continuous supply of heat flows. By doing so, the question arises as to whether this implementation should be applied to the process or to the utility level. At the process level, there is usually more freedom, as one is not bound by the existing temperature levels of the utility system, which are mostly difficult to retrofit. Therefore, this study presents an approach that generates heat integration concepts at the process level based on two different criteria. These criteria influence which process streams are grouped for a storage implementation and therefore influence the heat integration. The aim is to maintain the heat flows as continuous as possible by integrated heat storages. Finally, the possible heat integration... [more]

Cryogenic fuel tanks used in ships are continuously subjected to heat ingress and motions which affect the thermal behavior of the fluid inside the tanks. In this study, the ullage space of a liquid hydrogen (LH2) tank subjected to heat ingress and periodic rolling motion is analyzed using Computational Fluid Dynamics (CFD). A two-dimensional transient model utilizing the dynamic mesh approach is created to represent the ullage space of a partially filled LH2 tank. Three cases are studied where the properties of the thermal insulation of the tank model are varied, resulting in a different heat ingress for each case. A low-frequency motion is applied to the model domain, which induces mixing of temperature layers and cooling due to vapor contact with wetted walls. After 60 s of tank motion, most mixing is observed in the case with the smallest heat ingress, whereas in the cases with larger heat ingress and, consequently, larger thermal and density gradients, separation into a warmer and... [more]

The reduction of CO2-emissions in the chemical industry is essential to meet European climate targets. Particularly, the reliance on fossil fuels for process heat supply is a key factor for CO2-emissions. Electrically driven compression heat pumps are a promising option to reduce fossil fuel consumption by upgrading low-temperature waste heat to a higher temperature level, provided that low-carbon electricity is available. However, the integration of heat pumps into chemical utility systems remains a challenge due to economic constraints and the high complexity associated with site-wide heat integration and retrofit of existing structures. This work presents a mixed-integer linear programming (MILP) approach for the optimization of utility systems with integrated heat pumps. To address computational complexity, candidate utility temperature levels are pre-selected, and feasible heat pump coefficients of performance (COP) are precomputed. The framework is applied to both greenfield and... [more]

The present work explores computationally the potential of hybrid operation of a direct reduction shaft furnace with a feed gas mixture of methane and hydrogen, considering the operation of the associated gas handling and conversion units (reformers, condensers, heat exchangers, compressors, heaters, etc.). The state of the system is dependent on both the performance of the shaft furnace, which is sensitive to the feed gas composition and quantity, and the reformer, which converts the methane and the recycled top gas from the shaft furnace into feed gas. This coupling gives rise to nonlinear behavior of the system with respect to changes in the boundary conditions, which justifies a model-based approach for holistic analysis. The system is modelled in Aspen Plus to simulate the operation of an industrial-scale plant using a detailed model of the process units. Three configurations of the system are evaluated: (i) co-feeding of hydrogen with methane, (ii) feeding hydrogen directly to th... [more]

This study presents the modeling and operation optimization of an adsorption heat storage to improve the supply of renewable heat to a house. The system configuration is an open system with water being carried by an air flow and adsorbed on zeolite 13X beads in a packed bed. A numerical model is developed based on mass and energy balances, using a Langmuir adsorption isotherm and a Linear Driving Force (LDF) mass transfer equation. The model is implemented in Pyomo and solved with the NLP solver IPOPT. A sensitivity analysis on the discretization parameters is performed to choose a good compromise between accuracy and computational time. The chosen model is then validated against experimental data from the literature, with a mean absolute percentage error less than 5%. The dynamic optimization of the operation of the system to satisfy a heat demand is then performed. The trajectory for the inlet fluid velocity is optimized in several heat demand scenarios. The results show that this nu... [more]

Direct Air Capture (DAC) is a negative emissions technology whose performance is inherently linked to ambient conditions, which directly affect its primary feed stream (air). A common simplification in DAC model simulations is the use of fixed weather conditions, which can bias the predicted performance under weather variability. In response, this study quantifies the impact of local meteorological variability and temporal weather aggregation on the performance of DAC units. Building on a previously developed and validated 1D mechanistic model of a fixed-bed Steam-assisted Temperature Vacuum Swing Adsorption (S-TVSA) DAC process, we simulate its operation using weather data from the Met Office station at Buchan (UK), near the Saint Fergus terminal - a strategic hub for Carbon Capture and Storage (CCS) activities in Scotland. A two-branch methodological framework is developed combining optimization and forward simulations. Operating conditions are optimized using a multi-objective genet... [more]

Behind-the-meter generation from variable renewable energy is a potential pathway for new data centers to obtain power more quickly and more sustainably than interconnecting to existing electrical grids. Energy storage is needed to accommodate the variability of wind and solar energy across multiple timescales. Hydrogen from electrolysis and ammonia made from this hydrogen can be used as fuel for dispatchable power generation while offering lower $/MWh storage costs than batteries. In this work, we analyze the economics of using hydrogen, and/or ammonia along with batteries in hybrid energy storage systems to enable data centers to be powered by 100% renewables. We perform this analysis using an optimization model for the selection, sizing, and coordinated hourly operation of constituent energy storage technologies toward minimizing the levelized cost of energy (LCOE). The model uses an hourly resolution scheduling horizon of five years to account for hourly, seasonal, and interannual... [more]

Flexible, electrified systems for chemical and energy production are promising alternatives to traditional, hydrocarbon-based processes. Flexible systems have the potential to reduce costs and emissions, but the interconnection between design and operation makes these systems challenging to implement. We use an operation-informed design framework to model a flexible, electrified ammonia synthesis system. We examine the levelized cost and carbon intensity of ammonia in response to different grid emissions (0-420 kg/MWh). We find levelized costs from 700-1200 $/ton-NH3 and observe non-monotonicity in carbon-intensity with respect to grid emissions. We rationalize this trend as a design transition from large, grid-reliant systems to smaller, flexible designs that are grid independent. We then study how synergies in demand and unit-operation flexibility can lower both the price and carbon-intensity of ammonia production. We find that for seasonal, or yearly demand (rather than hourly), a f... [more]

The objective of this paper is to present a single equation format for quantifying the net carbon balance (NCB) in the evaluation of CO2 capture technologies, and to discuss the benefits of this approach. The equation must take into account indirect emissions, especially the contributions from utility generation systems (heating, cooling and electricity), making use of efficiency values and emission factors. The idea is to synthesize, in a single expression, the quantification of the environmental footprint of a technology, in a practical way so that it could be used as an efficient metric in technical evaluation studies, or as objective function/constraint in optimization problems. It also facilitates demonstrating the relationship between capture efficiency and environmental performance, as well as the contribution of each term to total emissions, and to compare different technologies in terms of time, location and available energy sources. To illustrate the application of the propos... [more]

Green hydrogen constitutes a strategic energy vector for achieving the Sustainable Development Goals (SDGs 7, 9, 12, and 13) due to its high energy density, flexibility for renewable energy storage, and direct emission-free operation. However, its production critically depends on the supply of high-purity water, which is unsustainable in the context of a projected 40% global water deficit by 2030. Given that more than 97% of available water is saline, integrating desalination processes with electrolysis constitutes an essential strategy for transitioning toward circular economy models in water resource management. This work presents the conceptual design, detailed modeling, and optimization of an integrated process for the sustainable production of green hydrogen from saline water. The system couples a desalination technology (Solar Distillation) with two electrolysis technologies (AEL and SOEC), modeled through physicochemical, electrochemical, and thermodynamic principles. The object... [more]

When designing and planning Green Hydrogen Supply Chains (GHSCs), sustainability considerations are increasingly recognized as essential, particularly in light of decarbonization goals and climate policy targets. Existing research has largely focused on economic and environmental however, social sustainability aspects remain significantly underexplored. This work aims to develop a mathematical programming model to design a GHSC, considering simultaneously economic and social aspects. Solar PV, wind power, and PPA (wind) as energy sources are integrated, while transportation options include the construction of new pipelines, compared to the use of existing highways for trucks carrying liquefied or compressed hydrogen to deliver hydrogen to an oil refinery. The model is applied to a case study conducted in the Brazilian state of Bahia, where different social indicators will be explored, characterizing the case study context while allowing generalization to other contexts. Results allow u... [more]