The placement of wind turbines is a crucial design element in wind farms, given the energy losses resulting from the wake effect. Despite numerous studies addressing the Wind Farm Layout Optimization (WFLO) problem, considering multiple directions to determine wind turbine spacing and layout remains limited. However, relying solely on one predominant direction may lead to overestimating energy production, and loss of energy generation. This work introduces a novel mathematical programming optimization framework to solve the WFLO problem, emphasizing the wind energy's nonlinear characteristics and wake effect losses. Comparisons with traditional layout approaches demonstrate the importance of optimizing wind farm layouts during the design phase. By providing valuable insights into the renewable energy sector, this research aims to guide future wind farm projects towards layouts that balance economic considerations with maximizing energy production.

In view of achieving the decarbonization target, green hydrogen is commonly regarded as the alternative capable of reducing the share of fossil fuels. Despite its wide application as a chemical on industrial scale, hydrogen utilization as an energy vector still suffers from unfavorable economics, mainly due to its high cost of production, storage and transportation. To overcome the last two of these issues, different hydrogen carriers have been proposed. Hydrogen storage and transportation through these carriers involve: 1. the carrier hydrogenation, exploiting green hydrogen produced at the loading terminal, where renewable sources are easily accessible, 2. the storage and transportation of the hydrogenated species and 3. its subsequent dehydrogenation at the unloading terminal, to favour H2 release. Although there is a number of studies in literature on the economic feasibility of hydrogen transport through different H2 vectors, very few of them delve into the technical evaluation of... [more]

The transition to net-zero greenhouse gas emissions requires a rapid redesign of energy systems. However, the redesign may shift environmental impacts to other categories than climate change. To assess the sustainability of the resulting impacts, the planetary boundaries framework provides absolute limits for environmental sustainability. This study uses the planetary boundaries framework to assess net-zero sector-coupled energy system designs for absolute environmental sustainability. Considering Germany as a case study, we extend the common focus on climate change in sustainable energy system design to seven additional Earth-system processes crucial for maintaining conditions favorable to human well-being. Our assessment reveals that transitioning to net-zero greenhouse gas emissions reduces many environmental impacts but is not equivalent to sustainability, as all net-zero designs transgress at least one planetary boundary. However, the environmental impacts vary substantially betwe... [more]

Biogas is often considered as a source of renewable energy, for heat and power production. However, biogas has greater promise as a source of concentrated CO2 in addition to methane, making it a rich supply of carbon and hydrogen for the generation of fuel and chemicals. In this work, we use the concept of attainable region in the enthalpy-Gibbs free energy space to identify opportunities for effective biogas valorization that maximizes the conversion of CO2. The AR concept allows us to study a chemical process without knowing the exact reaction mechanism that the species in the process use. Deriving Material Balance equations that relate a reactive process's output species to its input species is sufficient to identify process limits and explore opportunities to optimize its performance in terms of material, energy, and work. The conversion of biogas to valuable products is currently done in two steps; the high temperature and endothermic reformer step, followed by the low temperatur... [more]

We study the impact of switching from combustion heating to electric heating in processes comprising high temperature reaction/separation sequences, where the heat supporting the reaction(s) is substantially provided by combusting a reaction byproduct (fuel gas). A canonical process structure is de?ned. It is shown that the conventional combustion- based process presents signi?cant interactions. An asymptotic analysis is utilized to investigate and compare the dynamic responses of the conventional and electric process configurations. It is demonstrated that the dynamic behavior of the two processes exhibits two timescales, with the faster corresponding to the evolution of the temperatures of the units with high heat duty, and the slow time scale capturing the variables involved in the material balance. A simpli?ed ethylene cracking process example is used to demonstrate these findings.

As a promising electricity storage system, Liquid Air Energy Storage (LAES) has the main advantage of being geographically unconstrained. LAES has a considerable potential in energy efficiency improvement by utilizing compression heat and integrating with other systems. In this work, the Stirling Engine (SE) is introduced to improve the energy efficiency of the LAES system. Three LAES-SE systems are modelled in Aspen HYSYS and optimized by the Particle Swarm Optimization (PSO) algorithm. The studied systems include (i) the LAES system with 3 compressors and 3 expanders (3C+3E) using an SE to recover the compression heat, (ii) the 3C+3E LAES system with LNG regasification and SE, and (iii) the 3C+3E LAES system with solar energy and SE. The optimization results show that the Round-Trip Efficiencies (RTEs) of the LAES-SE system and the LNG-LAES-SE systems are 68.2% and 73.7%, which are 3.2% and 8.7% points higher than the basic 3C+3E LAES-ORC system with an RTE of 65.0%. For the Solar-LA... [more]

An important measure to achieve global reduction in CO2 emissions is CO2 capture, transport, and storage. The deployment of CO2 capture requires the development of a shared CO2 transport infrastructure, where CO2 can be transported with different transport modes. Furthermore, the cost of CO2 transport can be subject to significant economies of scale effects with respect to the amount of CO2 transported, also mentioned as clustering effects. Therefore, optimizing the shared infrastructure of multiple CO2 sources can lead to significant reductions in infrastructure costs. This paper presents a novel formulation of the clustered CO2 transport network. The Markov Decision Process formulation defined here allows for more detailed modeling of non-linear, discrete transport costs and increased geographical resolution. The clustering effects are modeled through cooperative multi-agent interactions. A multi-agent, reinforcement learning-based algorithm is proposed to optimize the shared transpo... [more]

Post-combustion carbon capture technologies have the potential to contribute significantly to achieving the environmental goals of reducing CO2 emissions in the short term. However, these technologies are energy and cost-intensive, and the variability of flue gas represents important challenges. The optimal design and optimization of such systems are critical to reaching the net zero and net negative goals, in this context, the use of computer-aided process design can be very effective in overcoming these issues. In this study, we explore the implementation of carbon capture technologies within an industrial complex, by considering the pooling of CO2 streams. We present an optimization formulation to design carbon capture plants with the goal of enhancing efficiency and minimizing the capture costs. Capital and operating costs are represented via surrogate models (SMs) that are trained using rigorous process models in Aspen Plus, each data point is obtained by solving an optimization p... [more]

Hydrogen, as a clean and versatile energy carrier, holds immense promise for addressing the world’s growing energy and environmental challenges. However, hydrogen-based energy systems face challenges related to efficient storage methods, energy-intensive production, refueling processes, and overall cost-effectiveness. To solve this problem, a superstructure was developed that integrates overall technologies related to hydrogen energy transportation. This study synthesizes process pathways for hydrogen energy transportation method including energy carrier production, storage, and refueling, based on the developed superstructure. The techno-economic analysis was conducted to evaluate the performance of each transportation pathway and compare it with conventional fossil fuel transportation system. Process performance criteria, including unit production cost (UPC), energy efficiency (EEF), and net CO2 equivalent emissions (NCE), serve as indicators for process performance. By comparing tec... [more]

As a low-carbon fuel, feedstock, and energy source, hydrogen is expected to play a vital role in the decarbonization of high-temperature process heat during the pyroprocessing steps of clinker production in cement manufacturing. However, to accurately assess its potential for reducing CO2 emissions and the associated costs in clinker production applications, a techno-economic analysis and a study of facility-level CO2 emissions are necessary. Assuming that up to 20% hydrogen can be blended in clinker fuel mix without significant changes in equipment configuration, this study evaluates the potential reduction in CO2 emissions (scopes 1 and 2) and cost implications when replacing current carbon-intensive fuels with hydrogen. Using the direct energy substitution method, we developed an Excel-based model of clinker production, considering different hydrogen–blend scenarios. Hydrogen from steam methane reformer (gray) and renewable-based electrolysis (green) are considered as sources of hyd... [more]

Thermal separation processes, such as distillation, play a pivotal role in the chemical and petrochemical sectors, constituting a substantial portion of the industrial energy consumption. Consequently, owing to their huge application scales, these processes contribute significantly to greenhouse gas (GHG) emissions. Decarbonizing distillation units could mitigate carbon emissions substantially. Heat Pumps (HP), that recycle lower quality heat from the condenser to the reboiler by electric work present a unique opportunity to electrify distillation systems. In this research we try to answer the following question in the context of multi-component distillation – Do HPs actually reduce the effective fuel consumption or just merely shift the fuel demand from chemical industry to the power plant? If they do, what strategies consume minimum energy? To address these inquiries, we construct various simplified surrogate and shortcut models designed to effectively encapsulate the fundamental phy... [more]

With growing concern over the effects of green-house gas emissions, there has been an increase in emission-reducing policies by governments around the world, with over 70 countries having set net-zero emission goals by 2050-2060. These are ambitious goals that will require large investments into the expansion of renewable and low-carbon technologies. The decisions about which technologies should be invested in can be difficult to make since they are based on information about the future, which is uncertain. When considering emerging technologies, a source of uncertainty to consider is how the costs will develop over time. Learning curves are used to model the decrease in cost as the total installed capacity of a technology increases. However, the extent to which the cost decreases is uncertain. To address the uncertainty present in multiple aspects of the energy sector, multistage stochastic programming is employed considering both exogenous and endogenous uncertainties. It is observed... [more]

Increasing wind and solar electricity generation in power systems increases temporal variability in electricity prices which incentivizes the development of flexible processes for electricity generation and electricity-based fuels/chemicals production. Here, we develop a computational framework for the integrated design and optimization of multi-product processes interacting with the grid under time-varying electricity prices. Our analysis focuses on the case study of nuclear-based hydrogen (H2) and electricity generation, involving nuclear power plants (NPP) producing high temperature heat and electricity coupled with a high temperature steam electrolyzers (HTSE) for H2 production. The ability to co-produce H2 along with nuclear is widely seen as critical to improving the economics of nuclear energy technologies. To that end, our model focuses on evaluating the least-cost design and operations of the NPP-HTSE system while accounting for: a) power consumption variation with current den... [more]

Solid oxide cells (SOCs) are a promising dual-mode technology that generates hydrogen through high-temperature water electrolysis and generates power through a fuel cell reaction that consumes hydrogen. Reversible operation of SOCs requires a transition between these two modes for hydrogen production setpoints as the demand and price of electricity fluctuate. Moreover, a well-functioning control system is important to avoid cell degradation during mode-switching operation. In this work, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional integral (PI) control strategies, while ramping between the modes of hydrogen and power production. While both control methods provide similar performance in many metrics, NMPC significantly reduces cell thermal gradients and curvatures (mixed spatial temporal partial derivatives) during mode switching. A dynamic process flowsheet of the reversible SOC syste... [more]

As the United States continues efforts to decarbonize the power and transportation sectors, significant challenges associated with the reliance of clean energy technologies on rare earth elements (REEs) will have to be overcome. One potential approach for increasing the supply of these elements is to extract REEs from end-of-life (EOL) hard disk drives (HDDs). HDDs contain neodymium and praseodymium, which are among the most important REEs for the clean energy transition, as they are crucial to producing the permanent magnets needed for wind turbines and electric vehicles. Here, we propose a superstructure-based approach to find the optimal pathway for recovering REEs from EOL HDDs. The superstructure was optimized by maximizing the net present value (NPV) over 15 years. Projected prices for commercial rare earth oxides and the projected amount of EOL HDDs in the U.S. were estimated and used in the model. These projections were used to establish the base case optimal result, assuming t... [more]

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-economic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES®-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully gene... [more]

A novel process system which integrates an isopropanol-based chemical heat pump with a post-combustion carbon capture unit was proposed, designed, and analyzed. The system uses low-quality waste heat (~80°C) produced through the CO2 adsorption step of a carbon capture process and upgrades that heat to a higher temperature (~150°C) using the chemical heat pump. The chemical heat pump is powered mostly by the waste heat and requires only a small amount of electricity. The higher temperature heat produced can be used in the desorption stage of the CO2 capture process, displacing a portion of the existing fossil energy required. The energy and exergy performance characteristics of the chemical heat pump were computed using the results of a steady state simulation in a systems analysis. Using exergy cost correlations, the profitability of the chemical heat pump concept was estimated. It was found that for this particular configuration, the fossil energy load of desorption could be reduced b... [more]

This research is dedicated to designing and economically evaluating the green ammonia supply chain, considering the fluctuating nature of renewable energy sources and energy demand across both hourly and seasonal variations. It also explores the impact of economies of scale and the delays associated with long-distance shipping to meet energy demands in a timely manner. These considerations require the formulation of a Mixed-Integer Nonlinear Programming model, further complicated by the necessity for a two-stage stochastic programming approach. We introduce a hierarchical optimization framework that utilizes a decomposition method to differentiate between one-time design decisions and subsequent operational choices. At the upper level, potential design solutions are identified through the Bayesian Optimization and Hyperband algorithm, which effectively navigates the non-linear challenges posed by economies of scale. The lower level then addresses a Mixed-Integer Linear Programming prob... [more]

This study presents a comprehensive approach to optimizing hydrogen supply chain network (HSCN), focusing initially on Texas, with potential scalability to national and global regions. Utilizing mixed-integer nonlinear programming (MINLP), the research decomposes into two distinct modeling stages: broad supply chain modeling and detailed hub-specific analysis. The first stage identifies optimal hydrogen hub locations, considering county-level hydrogen demand, renewable energy availability, and grid capacity. It determines the number and placement of hubs, county participation within these hubs, and the optimal sites for hydrogen production plants. The second stage delves into each selected hub, analyzing energy mixes under variable solar, wind, and grid profiles, sizing specific production and storage facilities, and scheduling to match energy availability. Iterative refinement incorporates detailed insights back into the broader model, updating costs and configurations to converge upo... [more]

The energy transition is driven both by the motivation to decarbonize as well as the decrease in cost of low carbon technology. Net-carbon neutrality over the lifetime of technology use can neither be quantitatively assessed nor realized without accounting for the flows of carbon comprehensively from cradle to grave. Sources of emission are disparate with contributions from resource procurement, process establishment and function, and material refining. The synergies between the constituent value chains are especially apparent in the mobility transition which involves (i) power generation, storage and dispatch, (ii) synthesis of polymeric materials, (iii) manufacturing of vehicles and establishment of infrastructure. Decision-making frameworks that can coordinate these aspects and provide cooperative sustainable solutions are needed. To this end, we present a multiscale modeling and optimization framework for the simultaneous resolution of the material and energy value chains. A case... [more]

The decarbonization of the society has a very high effect on the power grids as especially the energy generation will be almost completely shifted to CO2-neutral sources such as wind and solar. This implies significant design changes to the power grids and power systems, which lie between the electricity producers and consumers. In this paper, we discuss both the generation and consumer side, including the grid changes and required data exchange to support the transition.

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LC... [more]

Biogas is a promising energy source for sustainable hydrogen production due to its high concentration of CH4. However, determining the optimal process configuration is challenging due to the uncertainty of the fed biogas composition and the sensitivity of the operating conditions. This necessitates early-stage evaluation of the biomass-to-hydrogen process's performance, considering economics, energy efficiency, and environmental impacts. A data-driven model was introduced for early-stage assessment of hydrogen production from biogas without whole process simulation and optimization. The model was developed based on various biogas compositions and generated parameters for mass and energy balance. A database of unit processes was created using simulation models. Sensitivity analysis was performed under four techno-economic and environmental evaluation criteria: Unit Production Cost (UPC), Energy Efficiency (EEF), Net CO2 equivalent Emission (NCE), and Maximum H2 Production (MHP). The ea... [more]

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

The chemical industry is actively pursuing energy transition and decarbonization through renewables and other decarbonization initiatives. However, navigating this transition is challenging due to uncertainties in capital investments, electricity costs, and carbon taxes. Adapting to decarbonization standards while preserving existing valuable infrastructure presents a dilemma. Early transitions may lead to inefficiencies, while delays increase the carbon footprint. This research proposes a framework to find an optimal retrofit decarbonization strategy for existing oil refineries. We start with a generic process flowsheet representing the refinery's current configuration and operations, and consider various decarbonization alternatives. Through superstructure optimization, we identify the most cost-effective retrofit strategy over the next three decades to achieve decarbonization goals. We develop a Mixed-Integer Linear Programming (MILP) model, integrating simplified process equations... [more]