Power Production Research Articles

Design and performance optimization of a novel CAES system integrated with coaxial casing geothermal utilization

At present, the heat storage sub-system in the adiabatic compressed air energy storage system generally has low heat storage density and limited heat storage temperature, resulting in mediocre power generation efficiency of the system. To address these issues, this paper proposes a novel approach for coupling the energy storage system with geothermal energy utilization. This study innovatively uses coaxial casing wells to extract geothermal energy and to preheat compressed air in front of the expander. Detailed numerical investigations and dynamic thermodynamic analysis are carried out to evaluate system performance. Sensitivity and optimization are analyzed to identify the operational and design parameters of the proposed system. In addition, an economic analysis is conducted to estimate the investment and payback of the energy storage system. The results show that geothermal energy utilization can directly and significantly enhance the electric, heating and cooling energy output of compressed air energy storage system. During one 24-hours operation cycle, the electricity output is 104.45GJ, the heating supply output is 81.59 GJ, and the cooling supply output is 16.69 GJ. The RTE and energy efficiency are 64.26 % and 122.68 %, respectively. Economic analysis shows that the static payback period is 12.17 years, and the net present value can reach 3.46 million USD. It can be concluded that geothermal energy plays a greater role in energy storage systems than geothermal power production alone. The compressed air energy storage system combined with geothermal energy addresses current issues and leads to improved system performance.

Exponential PID controller for effective load frequency regulation of electric power systems

Load frequency regulation (LFR) is an indispensable scheme in planning electrical power production to provide consumers with stable, reliable and uninterrupted power. In the face of complicated power system (PS) structures with increasing and intricate power demand, new controllers that offer not only good performance, but also easy commissioning in practice are required. To this end, this research introduces an exponential PID (EXP-PID) controller as a new control scheme to ameliorate the LFR performance of PSs. This controller is simple to design and has a nonlinear feature inherited from two tunable exponential functions, which are placed in front of the PID controller and act on the error signal and its time derivative individually. To achieve the utmost performance, the EXP-PID controller’s parameters are procured by a corrected variant of the snake optimizer (co-SO). To validate the proposed control scheme, various single-/multi-area single-/multi-source PSs favored in the area are considered as test benches. A thorough comparison with the state-of-the-art approaches is performed to disclose the true efficacy of our proposal. Among the rivals, co-SO tuned EXP-PID controller, despite its simplicity, is found to render credible and promising performance in mitigating frequency and tie-line power deviations effectively.

Unlocking the benefit of active stretch: the eccentric muscle action, not the preload, maximizes muscle-tendon unit stretch-shortening cycle performance.

Stretch-shortening cycles (SSCs) outperform shortening contractions preceded by isometric contractions in terms of enhanced force/torque, work, and power production during shortening. This so-called SSC effect is presumably related to the active muscle stretch before shortening in SSCs. However, it remains unclear whether the effects of stretch-induced higher preload level or stretch-induced history dependence maximize the SSC effect. Therefore, we analyzed fascicle behavior, muscle-tendon unit (MTU) shortening work, and torque/force (n = 12 participants) via ultrasound and dynamometry during electrically stimulated submaximal plantar flexion contractions from 10° plantarflexion to 15° dorsiflexion. To elucidate the effects of preload level and preload modality (i.e., contraction type) on shortening performance, muscle-tendon unit shortening was preceded by fixed-end (SHO), active stretch (SSC), and preload-matched fixed-end (MATCHED) contractions. Before shortening, MATCHED and SCC had the same preload level (1% torque difference), similar joint position, and muscle fascicle lengths. Compared with SHO, shortening work was significantly (P < 0.001, partial η2 = 0.749) increased by 85% and 55% for SSC and MATCHED, respectively, with SSC shortening work being significantly higher than MATCHED (P = 0.016). This indicates that preload contributes by 65% to the overall SSC effect so that 35% needs to be referred to stretched-induced history-dependent mechanisms. In addition, SSC showed larger fascicle forces at the end of shortening (P < 0.001) and 20% less depressed isometric torque following shortening compared with MATCHED (P < 0.001). As potential decoupling effects by the series elastic element were controlled by matching the preload levels, we conclude that the difference between SSC and MATCHED is related to stretch-induced long-lasting history-dependent effects.NEW & NOTEWORTHY Using a torque-matched preload protocol, we found that 2/3 of the performance enhancement in muscle-tendon unit stretch-shortening cycles (SSCs) is caused by the increased preload level. The remaining 1/3 is owed to the long-lasting history-dependent effects triggered during the stretch in SSCs. This increased performance output is attributed to passive elastic structures within the contractile element that do not require additional muscle activation, therefore contributing to the higher efficiency of the neuromuscular system in SSCs.

AI-assisted Optimal Energy Conversion for Cost-Effective and Sustainable Power Production from Biomass-Fueled SOFC Equipped with Hydrogen Production/Injection

This study introduces a novel energy conversion and management framework to reduce carbon emissions in the energy sector and expedite the global shift towards sustainable practices. The system is driven by biomass-based solid oxide fuel cells for efficient power generation. Central to this approach lies the integration of additional hydrogen injection provided by a thermally-driven vanadium chloride cycle, aiming to enhance the quality of the syngas entering the fuel cells. The system is also combined with a super-critical CO2 cycle that generates power by passively enhancing performance through flue gas condensation. The proposed model's feasibility is evaluated in depth, techno-economically, considering thermodynamics and specific cost theories. As part of artificial intelligence, a neural network model is coupled with the genetic algorithm to determine the best operating status while minimizing computation time. According to the results, the suggested new integration results in higher efficiency and lower cost than a similar system without hydrogen injection. The results further show that the triple-objective optimization achieves output power, second-law efficiency, and overall system cost of 3425kW, 48.5%, and 2.3M$/year, respectively. Eventually, the gasifier is the main contributor to the highest level of exergy destruction, and fuel utilization and current density are the most important parameters in modeling.

AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency

This paper comprehensively analyzes AI-driven solar energy generation and smart grid integration, focusing on enhancing renewable energy efficiency. The study examines applying advanced artificial intelligence techniques in optimizing solar power production, forecasting, and grid management. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are evaluated for effectiveness in solar irradiance prediction and PV system performance estimation. The integration of AI in smart grids is explored, highlighting its role in demand-side management, energy storage optimization, and grid stability control. A holistic approach to improving renewable energy efficiency is proposed, encompassing integrated AI frameworks for solar-plus-storage systems, multi-objective optimization techniques for energy management, and AI-enabled microgrids and virtual power plants. The paper also addresses the challenges and future trends in AI application to renewable energy systems, including scalability issues, regulatory considerations, and ethical implications. By leveraging big data analytics and advanced AI algorithms, this research demonstrates the potential for significant improvements in overall system efficiency, reliability, and sustainability of solar energy systems integrated with smart grids.

Nanoarchitectonics in Advanced Membranes for Enhanced Osmotic Energy Harvesting.

Osmotic energy, often referred to as "blue energy", is the energy generated from the mixing of solutions with different salt concentrations, offering a vast, renewable, and environmentally friendly energy resource. The efficacy of osmotic power production considerably relies on the performance of the transmembrane process, which depends on ionic conductivity and the capability to differentiate between positive and negative ions. Recent advancements have led to the development of membrane materials featuring precisely tailored ion transport nanochannels, enabling high-efficiency osmotic energy harvesting. In this review, ion diffusion in confined nanochannels and the rational design and optimization of membrane architecture are explored. Furthermore, structural optimization of the membrane to mitigate transport resistance and the concentration polarization effect for enhancing osmotic energy harvesting is highlighted. Finally, an outlook on the challenges that lie ahead is provided, and the potential applications of osmotic energy conversion are outlined. This review offers a comprehensive viewpoint on the evolving prospects of osmotic energy conversion.

Seismic response of jacket-supported offshore wind turbines for different operational modes considering earthquake directionality

This study aims to analyse how jacket-supported OWTs functioning in three different operational modes (power production, emergency shutdown and parked mode) respond to seismic actions when subject to different incoming wind directions and ground motion shaking directions. To do so, the seismic response of the NREL 5MW OWT founded on the OC4 jacket substructure is simulated using an OpenFAST model that includes multi-support seismic input, soil–structure interaction and kinematic interaction. The impact of the working conditions and of the directionality of the loads on the jacket substructure is analysed and discussed. The tower top accelerations and displacements are examined for different sets of cases, and the seismic response of the jacket is studied in terms of internal forces and von Mises stresses along the different levels of the substructure. The results show, and quantify, the relevance of considering the different operational modes for a correct design of the substructure and of the foundation. The maximum structural stresses within the jacket substructure appear almost always in power production, with the exception of the top part of the legs, where higher stresses arise during emergency shutdown in several cases.

Process Modeling and Optimization of Supercritical Carbon Dioxide-Enhanced Geothermal Systems in Poland

This paper presents a comprehensive analysis of supercritical carbon dioxide (sCO2)-enhanced geothermal systems (EGSs) in Poland, focusing on their energetic performance through process modeling and optimization. EGSs harness the potential of geothermal energy by utilizing supercritical carbon dioxide as the working fluid, offering promising avenues for sustainable power generation. This study investigates two distinct configurations of sCO2-EGS: one dedicated to power generation via a binary system with an organic Rankine cycle and the other for combined power and heat production through a direct sCO2 cycle. Through accurate process modeling and simulation, key parameters influencing system efficiency and performance are identified and optimized. The analysis integrates thermodynamic principles with geological and operational constraints specific to the Polish context. The results highlight the potential of sCO2-EGSs to contribute to the country’s energy transition, offering insights into the optimal design and operation of such systems for maximizing both power and thermal output while ensuring economic viability and environmental sustainability.

Impacts of El Nino on renewable energy industry and fishery and aquaculture industry around the pacific

The effects of climate change on the world economy come into view in recent years, in El Nino is one of the most intricate and solid problems in predicting its occurrence and mitigating its effects. Former studies include its impacts on fishery and aquaculture, agriculture and major crops and the renewable energy market. Possible impacts include reproductive failure of fish, worsening well-being of people in coastal consortiums, decreasing production in maize, especially in warmer regions and decreasing efficiency and quantity of wind power production. Observed regions varied from the South American Coast and Peruvian waters to Malaysia, but neither of them compared the differences between each region as their major part. In this paper, fixed effects and dummy variable regression are used to evaluate El Ninos impact on fishery and renewable energy industries and contrast the differences between Japan, the USA and Australia. Results show that El Nino has a clear restraint on both industries but a stable increase in renewable energy production makes the relationship positive. Among the countries, Japan is the most restrained in both industries while the USA is the least affected. Among the different renewable energy sources, solar energy is promoted while hydro energy is restrained.

Performance analysis of spherically curbed hydrokinetic turbine arranged in ln-line array in a closed conduit

The present study aims to explore the applicability of hydrokinetic turbine (HKT) in a closed conduit. Spherically curbed Savonius HKT (SSHKT) was designed to calculate turbine performance (CP). Pipe flow velocity (V) is kept as 1.0, 2.0 and 3.0 m/s. Turbine performance declines as velocity increases, due to obstruction created by the turbine in the flow passage, flow instability, and separation. Performance of multiple SSHKTs was investigated installed in inline array. These turbines were arranged at varying distance of 2.5D, 3D, 3.5D, 4D, 4.5D and 5D, where D is the turbine diameter. The SSHKTs performed best at 3.5D irrespective of flow velocity. The number of the turbines increase the power production; however, efficiency of the system doesn't increase in that proportion. The CP of the first SSHKT was always higher compared to the next one along the array. In the pipeline, for single turbine, the high flow velocity causes a more significant pressure drop than the low flow velocity. At V = 1.0 m/s, the pressure drop for a single SSHKT is 8.52 %; whereas, at V = 2.0 m/s, the pressure drop is 28.69 %. Due to more blocked places in the arrayed turbines, the percentage pressure loss is greater.

Upcycling potential of hazardous tannery sludge to value-added products: Process modelling, simulation, and 3E analysis

The sustainability of the tannery sector largely depends on the eco-friendly management of its solid and liquid wastes. Unfortunately, the global tannery sector is strappingly suffering a significant challenge to meet environmental standards towards sustainability due to a lack of tannery sludge (TS) management facilities as well as lacking research on hazardous TS upcycling facilities to value-added products. Hence, TS from leather processing generally undergoes open dumping, responsible for environmental pollution, including water, soil, and air pollution. To ensure the sustainability of tannery sector, pioneeringly, this study attempts to conceptually design gasification based on four novel upcycling potentials of TS-to-value added products such as (i) TS to syngas with steam generation (TS-SS), (ii) TS to hydrogen with power generation (TS-HP), (iii) TS to power generation with carbon capture (TS-PCC), and (iv) TS to methanol production with power production (TS-MPP) using ASPEN Plus® simulator. Further, this research offers a comprehensive techno-economic analysis to evaluate the economic feasibility of the proposed four TS upcycling processes. The TS-PCC and TS-HP processes outperformed for their net energy efficiencies of 72.80 % and 50.22 %, correspondingly, compared to TS-HP (39.13 %) and TS-MPP (40.38 %). Regarding exergy efficiency, the TS upcycling processes coded TS-MPP and TS-HP demonstrated better net exergy efficiencies of 34.34 % and 27.20 %, respectively, compared to TS-SS and TS-PCC upcycling processes. These results confirm that TS-HP is much more efficient in terms of exergy. Furthermore, techno-economic analysis has confirmed that the developed processes, namely TS-SS and TS-PCC, are economically viable at a 70 % plant efficiency, whereas other processes, such as TS-HP and TS-MPP, are financially viable only if a subsidy of $33/ton subsidy is provided. The study findings have a significant impact on the development of circular economy-based industrial-scale TS management facilities in developing countries, which can ensure the sustainability of the leather sector.

Day-Ahead Dynamic Economic Emission Dispatch Considering Photovoltaic Predicted Output Using Sailfish Optimizer and Simulated Annealing

The Indonesian government continues to focuson boosting equitable access to environmentally friendlyelectricity to all levels of Indonesian society. This can be shownthrough the achievement of an electrification ratio of 99.40 percent in the third quarter of 2021 coupled with promisinggrowth in the capacity of Renewable Energy (RE) powerplants. Economic Dispatch (ED) aims to optimize the outputpower of generating units to minimize the total cost of powersystem operations. With the application of ED, the minimumgeneration cost will be obtained for the production of electricpower generated by generating units in an electricity systemwhile still paying attention to constraints. For hybrid plantssuch as the Selayar Solar Power Plant, the challenge toperforming ED is the intermittency of solar energy. So thatbefore the ED is carried out, solar radiation forecasting iscarried out using LSTM. To measure the accuracy offorecasting, RMSE, MSE MAE, and MAPE are used withaverage results of 6.311, 40.11, 4.61, and 4.87. With theapplication of ED, the minimum generation cost is obtained forthe production of electric power generated while stillconsidering the constraints. Through ED, fuel allocation can beoptimized, helping to reduce production costs and carbonemissions, and making a positive contribution toenvironmental sustainability. To improve ED results in thesailfish optimizer, the use of simulated annealing is added sothat the results have lower total cost and emission values thanED without simulated annealing. Rp 128.024 and 398 kg,respectively.

Recent Development in Nanoparticle-Assisted Microbial Fuel Cell for Enhanced Reduction of Chromium.

Chromium metal is a potential toxin released by various industries as by products. Reduction of the same costs an ample amount of manpower and wealth. Alternate, economical, efficient, and sustainable form of chromium reduction while generating electricity is a boon that microbial fuel cell (MFC) has provided to man. It paves way for an attractive technique to process hazardous elements. Nature as well as the type of electrode modulates the efficiency of reduction and power production. Many previously published studies have reviewed chromium removal from effluents as well as through MFCs, but utilization of nanoparticle-based MFC for chromium removal has not been exclusively done before. Hence, the objective of the current review is to provide exclusive study on nanoparticle-assisted MFC for chromium reduction. Reputed published data from the past 5years have been studied meticulously to compare the best outcomes of MFC in chromium removal. Chromium is found to be removed mostly in double-chambered MFC with a maximum removal of 100% when iron is used as an electrode. Removal of the same has led to generation of maximum power of 1965.4 mW m-2 when palladium nanoparticles are used at the electrode. Removal rates of Cr(VI) from a mixture of NiCo2O4, MoS2, and graphite felt in a dual-chamber MFC showed an 8.13% increase after 24h of light exposure. Another efficient setup used MoS2 nanosheets and Alpha-FeOOH nanoparticles in a dual-chamber MFC to completely remove Cr(VI) and achieve a high removal ratio of 91.45%. The current study reviews the recent updates in chromium reduction through MFC and its significance in future as a potential instrument for bioremediation and energy source.

China's onshore wind energy potential in the context of climate change

China has great potential for developing renewable energy to achieve its carbon neutrality goals. Orderly development of renewable energy is essential to enhance resource utilization efficiency and ensure safety during the energy system transition process. This study presents a thorough assessment of China's onshore wind power potential by considering the land suitability, the potential and temporal characteristics, and the impacts of climate change. The high-resolution maps combining wind resources with land conditions and climate scenarios are produced to provide insights into system planning, grid integration, and flexibility management. The results show that the capacity potential of onshore wind energy in China is 9.6 TW with an annual generation of 12.6 PWh, and 83 % of total capacity has a cost advantage with the levelized cost lower than the 60 $/MWh threshold. By comprehensively considering geographical, economic, and social criteria, around 8.1 % of the national territorial area is identified as the most suitable area for wind power development, primarily in Inner Mongolia and Xinjiang. The annual electricity generation from these areas can fulfill nearly 69 % of the nation's electricity demand. Future climate change projections indicate a remarkable generalized drop by 18 % in the north and a slight increase by 7 % in the south under the RCP 8.5 scenario. However, significant changes in wind resources are mostly within restricted areas, suggesting that future climate change would like to bring negative but limited impacts on wind power production in China.

Embedded hegemony and the evolution of the United States’ structural power

Whilst the United States’ (US) economic hegemony has existed continuously since the end of World War II, it has not been realised in the same way. In the early post-war period, the US’s hegemony rested on its dominance over gross world product and manufacturing output and exports. However, by the 1970s it began to transition to high-technology industries, services and foreign investment. Using a structural power analysis, this article argues that this transition in the economic foundations of US hegemony was not a reaction to an exogenous shift in the international division of labour, but was rather the result of the endogenous policies, decisions and priorities of the US. Moreover, the article illustrates how the interaction between the four aspects of structural power (security, production, finance and information) determined how these new hegemonic interests were embedded in international institutions and norms to better project US power globally. Through a historical analysis, the article demonstrates that the four aspects of structural power went from mutually reinforcing each other in the early post-war period to detracting from each other from the mid-1960s. This spurred a managed transition by the US from one embedded hegemonic order to another. The result was the construction of contemporary US embedded hegemonic order based on dollar hegemony (financial structural power) and the internationalisation of US corporate dominance (productive structural power), supported by the private ownership of knowledge by US corporations (informational structural power). The article also considers the implications of this analysis for current challenges to the contemporary hegemonic order.

Effects of Co3O4 modified with MoS2 on microbial fuel cells performance

In this study, Co3O4@MoS2 is prepared as anodic catalytic material for microbial fuel cells (MFCs). As the mass fraction of MoS2 is 20%, the best performance of Co3O4@MoS2 composite catalytic material is achieved, and the addition of MoS2 enhances both the electrical conductivity and catalytic performance of the composite catalyst. Through the structural characterization of Co3O4@MoS2 composite catalytic material, nanorod-like Co3O4 and lamellar MoS2 interweaved and stacked each other, and the agglomeration of Co3O4 is weakened. Among the four groups of single-chamber MFCs constructed, the Co3O4@MoS2-MFC shows the best power production performance with a maximum stable output voltage of to 539 mV and a maximum power density of up to 2221 mW/m2. Additionally, the ammonia nitrogen removal rate of the MFCs loaded with catalysts is enhanced by about 10% compared with the blank carbon cloth MFC. Overall, the findings suggest that Co3O4@MoS2 composite catalysts can significantly improve the performance of MFCs, making them more effective for both energy production and wastewater treatment.

Thermodynamic evaluation of decarbonized power production based on solar energy integration

This study aims to create an environmentally friendly system by utilizing solar and biomass energies as renewable sources, employing high temperature and pressure ranges for analysis. A new energy system is introduced, using biomass to produce hydrogen and other valuable commodities. The study evaluates power and hydrogen production technologies from a thermodynamic perspective, including conventional biomass gasification, calcium and iron thermochemical looping cycles, and gasification with supercritical water, incorporating carbon capture techniques. It assesses and compares the performance of locally available biomass material, such as cotton stalk, during gasification. Results show that employing the supercritical water gasification cycle yields the highest hydrogen production (8330 kg/h) and carbon capture rate (92.91 %), with potential for significant reduction in CO2 emissions. Supercritical water gasification also demonstrates electricity, heating, and CO2 production rates of 51.48 MW, 23.3 MW, and 16.6 MW, respectively. Integrating supercritical water gasification with solar energy enhances syngas productivity and system efficiency. Additionally, the highest performance is demonstrated in the SCWG case, which exhibits the lowest irreversibility at 320.32 MW. In conclusion, the system with supercritical water gasification emerges as the most efficient biomass conversion method, with higher energy and exergy efficiencies of 67.59 % and 49.74 %, respectively, under specific operating conditions. The developed model facilitates integration of solar energy into biomass gasification, enabling prediction and comparison of performance in terms of energy and exergy efficiencies.

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Development &amp; Validation of a Packed Bed Thermal Energy Storage Model

The rapid growth of economies and population around the world have exponentially increased the demand for energy. This demand has invoked increased annual global CO2 emissions and necessitated a transition to clean energy technologies. Reliable and affordable energy storage technologies are paramount for increasing renewable energy penetration onto the grid, supporting periods with reduced or no renewable energy generation. This project is aimed at developing a Thermal Energy Storage (TES) solution that can deliver heat to a heat engine for power production or to an industrial process for prolonged periods. TES long duration energy storage (LDES) presents many potential benefits, including 1) Low Cost 2) scalability 3) high energy density 4) low carbon footprint and 5) resiliency via ability to produce synchronous power (i.e., spinning turbomachinery). The broader objective of this project is to build, test, and model the TES concept at a 2 MWh scale to determine the economic and physics-based practicality of the system. The work presented here describes the first phase objectives of the project, including benchtop scale testing, model development, and Model Validation.

Multiscale multiphysics modeling of ammonia-fueled solid oxide fuel cell: Effects of temperature and pre-cracking on reliability and performance of stack and system

Ammonia is a promising carbon-free fuel for solid oxide fuel cells (SOFCs). However, direct feeding of ammonia into the SOFC may lead to serious degradation due to the nitriding. Conversely, the pre-cracking of ammonia introduces complexity to the system design and causes increased power losses in the system. Therefore, it is crucial to explore all these aspects simultaneously. In this context, this study introduces a novel multiscale modeling approach by integrating a 3D multiphysics simulation of the ammonia-fueled SOFC stack with system-level modeling to investigate the reliability and performance of the stack and system. Two different cell technologies developed for low temperature (LT) and high temperature (HT) operation are investigated at LT (600 – 700°C) and HT (700 – 800°C) ranges. The results indicate that fuel inlet temperature should be 55°C and 18°C higher than the minimum temperature in 0% pre-cracking case for HT and LT cases, respectively. The increase in the required air flow rate for cooling in the 100% pre-cracking case compared to the 0% pre-cracking case is around 100% and 216% for the HT and LT cases, respectively. However, stack power production and power losses in the system components are comparable for LT and HT cases which leads to similar system performance. A larger share of the active area is affected by nitriding in LT cases than HT ones. However, a smaller cracking ratio at LT (∼ 82%) compared to HT conditions (∼ 92%) is needed for elimination of nitriding. While the LT and HT cases are comparable in terms of system power production, the lower stack outlet temperatures in LT cases require novel and more expensive catalysts for ammonia pre-cracking and HT cases need more expensive steels.