Although rare, unexpected events such as financial crises, geopolitical conflicts, and pandemics have reshaped reality in recent years. Despite their strong potential to affect the energy transition, such events are still largely overlooked in energy planning studies. Ignoring them can lead to poorly informed decisions that may jeopardize the transition. Identifying early-stage decisions that remain robust under unexpected events is therefore essential. To address this challenge, EnergyScope Pathway, a whole-energy system model with limited foresight, is applied to Belgium. To increase the likelihood of a successful transition, the Modeling to Generate Alternatives approach is used to diversify early-stage decisions in 2035. These alternatives are allowed to be up to 10% more expensive than the cost-optimal solution. However, the large number of alternative designs is difficult to navigate for decision makers. To address this, a decision-support framework based on the Multi-Armed Bandi... [more]

Stochastic programming (SP) is a powerful framework for decision-making under uncertainty, but its practical adoption in industry is often hindered by the difficulty in understanding the causal relationships that drive optimal solutions. In the two-stage SP, strategic first-stage decisions are coupled with operational second-stage recourse decisions. When the number of scenarios under consideration is large, understanding the direct link between the uncertainty realization and optimal recourse strategy becomes computationally and cognitively demanding. Common approaches to improve interpretability include trained classification trees or scenario reduction, replacing the large scenario set with a representative subset. This is often achieved through post-hoc clustering (e.g., k-means) based on uncertainty realizations or optimal recourse decisions. While useful, these methods only provide a statistical approximation of the solution space and may fail to reveal the underlying structural... [more]

Computer-Aided Process Engineering (CAPE) has transformed how we analyze, design, and optimize energy processes. Yet, even advanced models rest on uncertain ground: their reliability depends on how well future operating environments are described-environments that are dynamic, complex, and deeply uncertain. In practice, uncertainty is often reduced to local parameter variations, driven by limited data, computational burden, and overconservative robust formulations. This narrow treatment creates a false sense of confidence: Designs that perform well in theory often fail in real-world operation. In a century marked by economic, climatic, and technological volatility, designing under uncertainty is no longer optional; it is essential.We have developed approaches that place uncertainty at the core of energy process modeling and design. This paper provides an overview of these methods and how uncertainty can be explicitly represented, quantified, and embedded into the design process.We pres... [more]

This work develops a high-spatial resolution multi-stage stochastic programming (MS) model for power grid capacity expansion that co-optimizes generation, transmission, and energy storage system investments under uncertainty. Traditional two-stage stochastic programming (TS) models determine all investments in a single stage, limiting their ability to adapt to changing conditions such as evolving capital costs, policies, or supply chain disruptions. In contrast, the proposed MS formulation introduces sequential decision stages where partial information is revealed over time, allowing for adaptive, scenario-contingent investments. We compare TS and MS formulations using a modified IEEE 24-bus case study to quantify the Value of the Multistage Solution, which measures the economic benefit of allowing investment decisions to adapt over time as uncertainty is progressively resolved. Results show that while MS models are computationally more challenging, they achieve lower expected costs an... [more]

Thermal management and heat transfer optimization remain central challenges in next-generation concentrated solar power (CSP) systems employing solid particles for thermal energy storage and heat transfer. Conventional particle receiver concepts, such as fluidized beds and falling particle curtains are constrained by limited particle-wall contact, flow instabilities, and restricted operating temperature. This work presents a combined computational and experimental investigation of a gravity-driven dense-phase moving packed bed receiver featuring a flat conduit geometry and sub-millimeter particles. A multiphase modeling framework is developed and validated against pressure-drop measurements and particle velocity data obtained from dedicated experimental setups. The validated model is subsequently used to quantify dense-flow stability and thermal performance under indirect heating conditions. Results demonstrate stable dense-phase operation with particle volume fractions of approximatel... [more]

Hydrogen is increasingly recognized as a vital energy carrier for a sustainable future. Among the various methods for hydrogen production, alkaline water electrolysis (AWE) stands out as a well-established and commercially viable option. However, their more effective deployment requires more advanced, portable, and scalable designs. This study explores systematic model-based shape optimization of the next generation AWE based on computational fluid dynamic (CFD) aimed to enhance the hydrodynamics and electrochemical performance. Several design geometries and arrangements were proposed including flow baffles to enhance hydrodynamic and facilitate detachment of oxygen and hydrogen bubbles. The findings indicate that the optimal design and location of the baffles improve fluid mixing and enhance bubble detachment, resulting in a more uniform electrolyte distribution and decreased concentration polarization. Several key performance indicators were considered to analyse the performance of p... [more]

The manufacturing industry is the second largest emitter of CO2, with the cement industry being one of the main contributors (7-8 % of the global emissions). Carbon capture and utilization (CCU) technologies are promising decarbonization solutions for the cement industry, addressing both fossil fuel-related (40 %) and process-derived emissions (60 %). Within a cement plant, producing synthetic natural gas (SNG) from captured CO2 is particularly suitable, as it is sufficient to fully replace solid fuels in the rotary kiln. On the other hand, the use of zero-carbon fuels, such as green ammonia, is also recognized as a promising approach for decarbonization. In this work, a superstructure was developed to explore alternative routes for producing SNG and green ammonia from CO2 and N2 in cement plant flue gas, respectively. The routes were modelled in Aspen Plus® V14, and their economic viability was assessed. Currently, the most promising route, at a cost of 109 €/tonne of flue gas, involv... [more]

Industrial decarbonization has heightened interest in electrifying major chemical processes, but existing planning methods typically assume fixed electricity prices and overlook how industrial power use affects the grid. This work introduces a grid-aware optimization framework that captures two-way interactions between industrial electricity usage and the power flows within the grid. We use the DC Optimal Power Flow (DC-OPF) model to generate Locational Marginal Prices across refinery demand levels and embed a surrogate reflecting the relationship between the power demand and the prices into an operational optimization problem for a partially electrified refinery. The surrogate model is embedded within the optimization problem using disjunctive reformulations and off-the-shelf packages such as OMLT (Optimization and Machine Learning Toolkit). In a case study considering an oil refinery with installed electric boilers, electrolyzers, H2 storage, and post-combustion carbon capture infras... [more]

Decarbonization of hydrogen-intensive industrial clusters is essential to meet the European Union's net-zero targets. Although hydrogen can replace fossil-based feedstocks and fuels in refineries and chemical industries, its production remains largely dependent on natural gas. Therefore, cost-effective and low-emission supply routes require a system-level approach that integrates regional resources, technologies, and industrial demand. This study applies a multi-objective optimization framework to design a low-carbon hydrogen supply system for Galicia (northwestern Spain), addressing two gaps in regional energy system modeling: model transferability across regions and integration of social criteria beyond techno-economic assessment. The model quantifies trade-offs between total system cost and greenhouse gas emissions, and an employment indicator is integrated via post-processing using TOPSIS. The results show that meeting 100% of the projected 2030 demand (105 kt H2/a) yields a single... [more]

CO2-to-methanol process is an attractive option to simultaneously reducing the anthropogenic CO2 while producing value-added chemicals. In this work, two distinct CO2-to-methanol process routes specifically, single step and dual step are evaluated based on their economic and environmental performance. First, a multiobjective optimization (MOO) framework is formulated to develop the optimal process configurations. Three conflicting objectives including methanol production rate, total annual cost (TAC) and carbon intensity of methanol are considered. For this MOO, the elitist non-dominated sorting genetic algorithm (NSGA-II) is employed to get the Pareto front. From the Pareto front, a balanced compromise solution is identified by the technique for order of preference by similarity to ideal solution (TOPSIS) with entropy information as weighting criteria. Then, the comparative performance analysis is conducted across the Pareto front. At the TOPSIS-selected configuration, the single step... [more]

Achieving net climate neutrality will likely require negative-emission technologies such as Direct Air Capture (DAC). Potassium hydroxide (KOH) absorption is one of the most mature DAC approaches, but it can cause significant emissions due to natural-gas-based thermal regeneration. Electrochemical regeneration methods, such as electrolysis and electrodialysis, have recently been proposed as alternatives, yet their relative performance and environmental impacts remain unclear. We present a comparative cradle-to-gate life cycle assessment (LCA) of three KOH-based DAC configurations: (i) the established Ca-looping thermal regeneration, (ii) the electrolysis regeneration (DAC-ELY), which co-produces hydrogen, and (iii) the electrodialysis regeneration (DAC-ED). The results show that, expectedly, electricity demand dominates life cycle impacts across all configurations. With the current German electricity mix, the established DAC has the lowest overall impacts, while DAC-ELY and DAC-ED exhi... [more]

Power-to-X (PtX) technologies play a central role in renewables-based energy systems by enabling the conversion of renewable electricity into multiple energy carriers. However, due to the multiple energy conversion stages inherent to such energy systems, they often suffer low system efficiencies and high operational costs. In this context, waste product utilization offers significant potential for improving system performance. Directly integrating waste product utilization into energy system operational problems, however, is computationally challenging, as it requires high model granularity to capture waste product characteristics and introduces additional complex constraints.This work proposes a method to integrate waste heat utilization into operational optimization problems, aiming to improve the overall performance of PtX energy systems. Detailed process models, together with pinch analysis, are used to generate surrogate models for the thermal (by-)products and their associated te... [more]

Trinidad and Tobago emits 11.3 metric tonnes of CO2-eq per capita per year, making it one of the highest per capita per year greenhouse gas (GHG) emitters globally. An estimated 87% of these emissions are linked to the industrial sector, including power generation. This study aims to reduce the national environmental impact of the power generation and waste disposal sectors, with the goal of reducing the country's reliance on natural gas and promoting sustainable power generation via waste-to-energy (WTE) pathways. This work seeks to implement a mixed-integer linear programming (MILP) framework, along with techno-economic analysis (TEA) and life-cycle assessment (LCA) to determine potential sustainable objectives with respect to T&T's power system. The study implements supply chain optimisation considering single objective optimisation (SOO) with life-cycle (LC) endpoint externalities included. The key constraint of the system was the production of sufficient electricity to sustain the... [more]

The development of energy technologies with low CO2 emissions is increasingly important for achieving the United Nations Sustainable Development Goals. In this context, power plants based on the Allam-Fetvedt Cycle appear promising because this cycle (introduced in 2012) features inherent CO2 capture. In this context, its association with biogas as fuel enables its application as a Bioenergy with Carbon Capture and Storage (BECCS) system. This study evaluates the technical, energetic, and economic feasibility of an Allam-Fetvedt Cycle power plant fueled by biogas. The methodology is based on detailed process simulations performed using Aspen Plus® v14. Two biogas production scenarios were assessed: Case 1 and Case 2, corresponding to the processing of 8 and 24 million tons of sugarcane per year, respectively. The economic analysis indicated a high capital investment, primarily in the Air Separation Unit (ASU) and the Balance of Plant (BOP). Nevertheless, a significant reduction in the... [more]

The aviation sector is difficult to decarbonize due to limits on aircraft electrification, making sustainable aviation fuel (SAF) a critical near-term solution. This study integrates Aspen-based process modeling with game-theoretic optimization to design a multi-agent SAF production network comprising coal gasification and CO2-assisted natural gas reforming for syngas production, and Fischer-Tropsch (FT) synthesis for SAF production. Techno-economic parameters from Aspen simulations inform an agent-based model in which agents maximize their net present value subject to capacity and demand constraints. Three decision-making frameworks are compared: (i) social welfare optimization, (ii) cooperative bargaining - symmetric (equal bargaining power) and asymmetric (bargaining power weighted by agents' competitiveness outside cooperation), and (iii) competitive equilibria modeled as generalized Nash equilibrium. The results show that social welfare maximization excludes coal and yields the hi... [more]

Biogas offers a promising biogenic carbon source for renewable methanol, but differences in CH4/CO2 ratio across feedstocks and possible upstream CO2 handling can shift syngas stoichiometry away from the methanol synthesis target range. This work quantifies how biogas composition and reformer operation influence the stoichiometric number (SN) and the associated conditioning requirement needed to meet methanol synthesis targets. A steady-state Aspen Plus® model of an integrated biogas-to-methanol process is used as the analysis framework. A base-case operating point is defined, followed by parametric evaluation of biogas CH4/CO2 ratio, reformer temperature, reformer pressure and steam-to-methane (S/C) ratio. The studied CH4/CO2 ratio range covers CO2-rich to CH4-rich cases that may occur across sites and upgrading levels. The resulting SN shifts are tracked and converted into a quantitative correction requirement to maintain the methanol design target (SN = 2.01). Temperature determines... [more]

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]

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]