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High-temperature heat pumps are a key technology for enabling the complete integration of renewables into the power grid. Although these systems may come with several variants, Brayton heat pumps are gaining more and more interest because of the higher heat sink temperatures and the potential to leverage already existing components in the industry. Because these systems utilize renewable electricity to supply high-temperature heat, they are particularly suited for industry or energy storage applications, thus prompting the development of various demonstration plants to evaluate their performance and flexibility. Adapting to varying load conditions and swiftly responding to load adjustments represent crucial aspects for advancing such systems. In this context, this study delves into assessing the transient capabilities of Brayton heat pumps during thermal load management. A transient model of an emerging prototype is presented, comprising thermal and volume dynamics of the components. Furthermore, two reference scenarios are examined to assess the transient performance of the system, namely a thermal load alteration due to an abrupt change in the desired heat sink temperature and, secondly, to a sudden variation in the sink mass flow rate. Finally, two control methodologies—motor/compressor speed variation and fluid inventory control—are analyzed in the latter scenario, and a comparative analysis of their effectiveness is discussed. Results indicate that varying the compressor speed allows for a response time in the 8–20 min range for heat sink temperature regulation (first scenario). However, the regulation time is conditioned by the maximum thermal stress sustained by the heat exchangers. In the latter scenario, regulating the compressor speed shows a faster response time than the inventory control (2–5 min vs. 15 min). However, the inventory approach provides higher COPs in part-load conditions and better stability during the transient phase., Article; Export Date: 20 March 2024; Cited By: 0; Correspondence Address: M. Pettinari; Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Pisa, 56122, Italy; email: matteo.pettinari@phd.unipi.it; L. Ferrari; Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Pisa, 56122, Italy; email: lorenzo.ferrari@unipi.it

Existing combined heat and power plants usually operate on part-load conditions during low heating demand seasons. Similarly, there are boilers designated for winter use that remain inactive for much of the year. This brings a concern about the inefficiency of resource utilization. Retrofitting existing CHP plants (especially for those with spare boilers) for biofuel production could increase revenue and enhance resource efficiency. This study introduces a novel approach that combines biomass gasification and pyrolysis in a polygeneration process that is based on utilizing existing CHP facilities to produce biomethane, bio-oil, and hydrogen. In this work, a detailed analysis was undertaken of retrofitting an existing biomass combined heat and power plant for biofuel production. The biofuel production plant is designed to explore the polygeneration of hydrogen, biomethane, and bio-oil via the integration of gasification, pyrolysis, and renewable-powered electrolysis. An Aspen Plus model of the proposed biofuel production plant is established followed by a performance investigation of the biofuel production plant under various design conditions. An economic analysis is carried out to examine the profitability of the proposed polygeneration system. Results show that the proposed polygeneration system can achieve 40% carbon efficiency with a payback period of 9 years and an internal rate of return of 17.5%, without the integration of renewable hydrogen. When integrated with renewable-power electrolysis, the carbon efficiency could be significantly improved to approximately 90%; however, the high investment cost associated with the electrolyzer system makes this integration economically unfavorable.

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors' knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion.

Hybrid-electric propulsion is recognized as an enabling technology for reducing aviation’s environmental impact. In this work, a serial/parallel hybrid configuration of a 19-passenger commuter aircraft is investigated. Two underwing-mounted turboprop engines are connected to electrical branches via generators. One rear fuselage-mounted electrically driven ducted fan is coupled with an electric motor and respective electrical branch. A battery system completes the selected architecture. Consistency in modeling accuracy of propulsion systems is aimed for by development of an integrated framework. A multipoint synthesis scheme for the gas turbine and electric fan is combined with physics-based analytical modeling for electrical components. Influence of turbomachinery and electrical power system design points on the integrated power system is examined. An opposing trend between electrical and conventional powertrain mass is driven by electric fan design power. Power system efficiency improvements in the order of 2% favor high-power electric fan designs. A trade-off in electrical power system mass and performance arises from oversizing of electrical components for load manipulation. Branch efficiency improvements of up to 3% imply potential to achieve battery mass reduction due to fewer transmission losses. A threshold system voltage of 1 kV, yielding 32% mass reduction of electrical branches and performance improvements of 1–2%, is identified. This work sets the foundation for interpreting mission-level electrification outcomes that are driven by interactions on the integrated power system. Areas of conflicting interests and synergistic opportunities are highlighted for optimal conceptual design of hybrid powertrains.

Existing combined heat and power plants are seeking additional heat sinks to address challenges arising from the declining district heating demand and the increasing share of renewable energy in primary energy use in the coming decades. In the meantime, the world’s demand for sustainable fuel production keeps increasing due to the need to reduce carbon emissions and mitigate the effects of climate change. Fast pyrolysis, as a thermochemical conversion process based on widely available feedstocks such as lignocellulosic biomass, is promising to provide a long‐term supply of sustainable fuels, and could be integrated into existing combined heat and power plants due to the scalability and maturity of this method. This work focuses on techno‐economic analysis of integrating fast pyrolysis into existing combined heat and power plants for biofuel production. A process model of fast pyrolysis and bio‐oil upgrading is established in Aspen Plus to simulate the integration process. In this work, particular attention is given to the profitability analysis based on different final fuel products(crude pyrolysis oil and upgraded bio‐oil). Different hydrogen generation solutions (electrolysis, and gasification) for onsite bio‐oil upgrading are also examined. This study also performs an analysis of several economic indicators, such as payback period, net present value, and internal rate of return to provide insights for the future business model development for such systems. Sensitivity analysis is also carried out to further reveal the impacts of key variables in the economic evaluation process on the system’s profitability.

Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.

Hydrogen is considered one of the most promising alternative fuels for aviation, which can be used to power aircraft and airport ground services. Onsite hydrogen production from renewables can be suitable for small- size airports, while the larger size airports can be supplied through transportation either from dedicated green hydrogen production plants or other sources of hydrogen. This paper presents a study of two hydrogen supply scenarios, one taking the small airport of Stockholm Skavsta as a case study for in-house hydrogen production. The second is evaluating offshore green hydrogen supply to the large size airport of Arlanda. The in-house hydrogen production evaluates 18 scenarios covering all possible scenarios for alkaline, PEM, and solid oxide electrolysis as production means and compressed, cryo- compressed, and liquid gas as storage, with power supply from grid and grid plus in-house solar system. The optimum production and storage facility size is determined in association with the levelized cost and carbon emissions for each scenario. For the large-size airport, the study evaluates the hydrogen supply from offshore production facilities transported as compressed, cryo-compressed, or liquid gas via offshore pipeline and onshore pipeline, Offshore pipeline and truck, Ship and onshore pipeline, or Ship and truck. The results showed the levelized cost to be between 2.93-2.44 Euro/kg H2 in the case of in-house production. Compressed hydrogen offshore and onshore pipeline is the least cost for Arlanda airport hydrogen supply. This paper demonstrates a direction for aviation sector decarbonization and establishes a pathway for airports' in-house hydrogen production and outsourced hydrogen supply.

The emerging need for cost- and energy-efficient propulsion systems have shifted the research interest to radical concepts, with several challenges to overcome in order to enter into service. However, revitalizing old yet provably successful engine designs-a practice followed by engine manufacturers over the years-can provide low-risk alternative solutions to move immediate market demands. The paper reviews this approach, known as concept of growth engines or core commonality, and investigates how a geometrically fixed or geometrically similar engine core can be utilized across a family of engines. The development programs of the highly successful CFM56 and PT6 engine families are analyzed, proving the success of this concept over the years. The idea is based on the engineering paradigm of product families and is briefly presented from a theoretical perspective. Implementation methods and simulation tools to initiate, conceptualize, design, and evaluate an engine family program are reviewed. The potential application of engine core commonality into electrified propulsion systems is investigated. Design challenges and opportunities that electrification imposes to the growth engine concept are discussed. Finally, the concept of growth electrified propulsion systems is introduced, conveying future research directions to achieve a successful family of engines for electrified applications., Article; Export Date: 02 April 2024; Cited By: 0; Correspondence Address: M.D. Kavvalos; Future Energy Center, Mälardalen University (MDU), Västerås, 721 23, Sweden; email: mavroudis.kavvalos@mdu.se; CODEN: JETPE

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the "weekly Na & iuml;ve" benchmark with a relative Root Mean Square Errors of 32-34% and 39-40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

Energy storage systems play a crucial role in supporting the integration of renewable energy sources. In this framework, Brayton Pumped Thermal Energy Storage is an emerging technology thanks to many positive features, including geographical and raw materials independence, long lifetime, and peculiar sector-coupling capabilities. By storing electric energy as thermal exergy, this technology offers the flexibility to discharge energy directly for heating or cooling applications or convert it back into electricity as needed by the grid. This dual functionality fits well with the multi-energy intrinsic nature of urban districts in which electrical and thermal energy carriers are involved. This paper aims then to evaluate the potential economic benefit due to the usage of a Brayton based Pumped Thermal Energy Storage as multi-energy device instead of a solely electric-to-electric. An urban district with thermal and electric requirements is used as a case study to investigate the techno-economic performance of the mentioned storage capacity when coupled to photo-voltaic plants to simulate deep-decarbonization scenarios. The system day-ahead optimization, performed through a Mixed Integer Linear Programming approach, aims to minimize the operational cost computed over a 24-h horizon. The results highlight that operational yearly cost savings are 5–10 % when using the multi-energy storage functionalities compared to the standard electric-to-electric operation. Despite the cost reduction, allowing only direct heating causes unavoidable thermal curtailment losses in the 6–10 % range. However, these losses can be reduced to 3 % by introducing the additional direct cooling functionality, bringing the best performances from the economic and thermodynamic standpoints.