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[Display omitted] • Office paper waste is processed using physiochemical and enzymatic hydrolysis. • The yeast is isolated from rotten banana for the fermentation process. • Box-Behnken analysis was used to determine optimal conditions for key variables. • A blended membrane was applied for the purification process. • Amicon cell and polymeric membrane dehydrate ethanol–water combinations. One potential replacement for fossil fuels is the generation of biofuel from biomass feedstock. To reach the global sustainability goals, it is also necessary to address a number of issues, such as reducing environmental pollution, climate change, and carbon emissions. This work was aimed to convert the office paper waste as biomass feedstock into bioethanol through a sustainable pathway. The office paper waste was hydrolyzed via physiochemical and enzymatic hydrolysis, the liberated glucose was estimated at 15.72 g/L using the optimum conditions of 15 % HCl and 6 % office paper waste followed by enzymatic hydrolysis. The yeast isolate B2 that was applied for the fermentation process was isolated from rotten banana, identified as Hanseniaspora uvarum (STDF-B2) and submitted to GenBank with accession number (OP800106). The Box-Behnken design was applied to estimate the optimal conditions for the three most significant variables depending on the one variable at a time results. A blended membrane of sulphonated polyvinyl chloride with poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (SPVC-PAMPS) was applied for the purification of both standard ethanol and produced bioethanol via pervaporation technique (PV). The morphology, chemical composition, mechanical properties and permeability of the membrane were all analyzed in various ways. Following purification using a membrane-integrated amicon cell, the total permeate flows of the laboratory-prepared ethanol and generated bioethanol were 289.54 and 253.06 (g/m2 h), respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • A two-phase mass transfer model of paper-based microfluidic fuel cell is proposed. • The coupling characteristics of capillary action and two-phase flow are revealed. • The fluctuation mechanism of cell performance and gas distribution is explored. • Gas removal strategies are summarized, with a maximum gas removal rate of 42.30%. Paper-based microfluidic fuel cell powered by capillary force has the advantages of compactness, low cost, and high sensitivity, presenting broad application prospects in portable electronic devices. However, the gas-liquid mass transfer mechanism within the porous medium remains unclear, with a lack of two-phase flow theories adapted to the unique paper-based system. In this study, a two-dimensional two-phase numerical model of a Y-shaped paper-based microfluidic fuel cell is developed to reveal the two-phase flow characteristics under capillary action and explore gas removal strategies. The reliability of the model is validated by real experimental data. By coupling multiple physical fields, the study elucidates the working principle and gas-liquid flow phenomena within the cell. Then, the effects of contact angle, inlet fuel concentration, and paper type on output performance, gas distribution, parasitic effect, and fuel utilization are discussed. The results indicate that increasing the contact angle and using paper with a larger average pore size and porosity as the channel substrate are ideal for promoting gas discharge. The optimal gas removal rate reached 42.30 %, with a favorable power density of 33.92 mW/cm2. This study provides a theoretical basis for a deeper understanding of the two-phase mass transfer process in paper-based microfluidic fuel cell. [ABSTRACT FROM AUTHOR]

• Efforts to enhance PMFC performance are reviewed. • Both exciting advancements and current limitations are discussed. • An innovative strategy of multi-scale simultaneous optimization is proposed. Paper-based microfluidic fuel cell (PMFC) has attracted great attention in the microfluidic fuel cell field in recent years. It utilizes the spontaneous capillary flow of reactant solutions in paper-based porous substrate to achieve passive transportations of fuel and oxidant, solving the fluid driving issue encountered in traditional microfluidic fuel cells and thus having broad application prospects in medical detection, wearable devices, micro sensors, environmental monitoring, and many other fields. However, the commercialization of this technology is impeded by the low output performance caused by the limited mass and energy transfer in PMFCs. To enhance the mass and energy transfer in PMFCs, numerous research studies have been conducted via experimental optimization of the cell materials and structures. Numerical analyses focusing on the structure–activity relationship of PMFCs were also performed recently. To provide a comprehensive and thorough review about the efforts devoted to improving performance of PMFC, research papers relevant to PMFC since its invention in 2014 have been extracted in this work and significant works were filtered to highlight the exciting advancements. The experimental studies were classified and discussed based on the key components involved in the PMFC system, followed by a critical review of the limited computational models. Potential directions for future research were also provided, aimed at overcoming the current technological challenges in PMFCs. Importantly, an innovative strategy of multi-scale simultaneous optimization of the cell properties is proposed considering the typical multi-scale feature of the PMFC system, which could inspire the designing of advanced cell materials with optimal multi-scale structures for applications of PMFCs. [ABSTRACT FROM AUTHOR]

• Operation and construction of an energy hub based upon the flexibility. • The flexibility index covers maximum system capacity and system response time. • Effect of energy hub flexibility on battery aging. • Consumer participation rate prediction in demand response programs by deep learning. Energy hubs (EHs), hold promise for improving energy efficiency and mitigating carbon emissions. The increasing renewable energy resources, with its inherent uncertainty and variability in power generation, presents challenges for power systems, thereby highlighting the importance of flexibility. This paper proposes a multi-objective framework for enhancing EH flexibility. It is expected that flexibility can induce battery aging which is demonstrated in this paper. The framework leverages a novel flexibility index encompassing both capacity and response time to optimize EH configuration and operation. In this paper employs a data-driven approach to analyze battery degradation under varying flexibility demands. This analysis informs the acceleration of aging associated with increased flexibility. Given the critical role of flexibility, this paper investigates the tripartite relationship between flexibility, time, and battery lifespan. Additionally, considering Demand Response Programs as crucial flexibility resources, the proposed framework utilizes deep learning to forecast consumer participation rates. Utilizing historical data on temperature, price, and participation, various deep learning models are compared. Results indicate a 30% reduction in operating costs associated with a 12% increase in flexibility. However, this flexibility comes at the cost of increased manufacturing expenses and a 20% decrease in battery lifespan due to operational fluctuations. [ABSTRACT FROM AUTHOR]

• Optimize retrofitting strategies by simulation-based multi-objective optimization. • Compare the retrofitting strategy and reconstruction strategy by emergy analysis. • Consider the large domain strategy space of building retrofitting strategy. • NSGA-III algorithm, EnergyPlus software, emergy analysis and LCA are integrated. • The retrofitting strategy is better than the reconstruction strategy. Building renewal has become a promising approach for improving energy efficiency and reaching carbon neutrality targets. Two renewal strategies, namely, the retrofitting of existing buildings and their demolition and reconstruction of a new one, are often explored and compared to determine which strategy is the optimal one. However, the decision is still uncertain due to the large domain space of strategies and the lack of comprehensive methods. This paper, therefore, presents a novel hybrid method to help decision-makers identify the optimal building renewal strategy based on the integration of multi-objective optimization algorithm with emergy analysis. A simulation-based non-dominated sorting genetic algorithm is developed to select the optimal retrofitting strategy, where dynamic simulation model is created in EnergyPlus software and non-dominated sorting genetic algorithm is written in Python. Then, the equivalent reconstruction strategy is projected by the optimal retrofitting strategy while meeting mandatory standards. A comparison between the optimal retrofitting strategy and the equivalent reconstruction strategy can be made by applying emergy analysis considering the whole life cycle phase. A fictional office building was chosen as a case study to illustrate the practical implementation of the proposed method. High-cost building retrofitting strategies were found to consistently yield the highest energy savings and thermal comfort, and the energy use decreased by 4.63 % to 48.25 %. The input emergy of the original building, retrofitting building and reconstruction building are 3.16E + 20 sej , 1.41E + 21 sej , and 1.46E + 21 sej , respectively, and the output emergy are 2.39E + 20 sej , 1.66E + 20 sej , and 4.76E + 19 sej. The results also indicate that the sustainability of the retrofitting strategy is better than that of the reconstruction strategy. This paper provides a guidance method and framework for decision-makers and policy-makers to identify the optimal building renewal strategy for reducing the energy use and achieving carbon peak targets. [ABSTRACT FROM AUTHOR]

Design of hybrid Nano-Grid system (HNGS) necessitates a careful thought to benchmark reliable and cost effective solutions for remote areas. Optimal sizing of HNGS using Honey Badger Algorithm (HBA) for futuristic residential area in Mahendragarh, Haryana, India is presented in this paper. The HNGS comprise combination of solar panels, wind turbines, storage batteries, diesel generator, inverter and residential load components. The primary objective functions considered in this paper are Cost of Electricity (COE) and Net Present Cost (NPC), in continuation these problems are stated as multi objective optimization issues. Numerous permutations of diverse resources were examined to assess parameters such as NPC, COE, battery storage, land utilization, emission and potential employment opportunities. HBA is almost independent of traps in the local minimum, therefore the results obtained are superior as compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Biogeography Basic Optimization (BBO), Improved Artificial Ecosystem Optimization (IAEO) and Grey Wolf Optimization (GWO) as well as HOMER software, in order to determine the quality of solution the proposed HBA algorithm has obtained. HNGS system can be readily and efficiently optimized using the suggested HBA algorithm a detailed sensitivity analysis is done to better understand the system behavior for a wider region use. • A well optimized HNGS design is cost-effective, highly reliable and simple system for remote areas. • An optimization problem is formulated and solved by means of Honey Badger algorithm. • The optimization aim is to find HNGS size in terms of number of individual sources which supplies demand successfully. • Outcomes show that HBA effectively optimizes HNGS by lowering the NPC and CoE, ensuring the electricity supply. • The HBA's results has been compared to GA, PSO, BBO, IAEO, and GWO, as well as HOMER software. • Sensitivity analysis has been used to explore the impact of weather and component parameters on COE and NPC. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for predicting the irradiance of PV greenhouse in many cases was proposed. • Four models were discussed at variable time, data size and climatic conditions. • The instantaneous irradiance prediction under 9 weather conditions was provided. • Transformer model had the best prediction effect for dataset at one year of usage. • Pyraformer model had the best prediction effect on winter and summer. Photovoltaic power generation can provide energy for greenhouses and achieve high quality and high yield of crops. In reality, solar irradiance is fluctuating and intermittent. Thus, the key to ensure efficient photovoltaic power use under greenhouse environmental conditions is to provide an accurate prediction of solar irradiance. Yet, currently studies on irradiance prediction in terms of the variable time lengths, multi-parameter and full climate conditions are rather limited. To improve the comprehensive performance of the prediction model, this paper proposes models of irradiance time series prediction such as Pyraformer, Informer, Transformer and TimesNet. Those models were tested based on the synergistic combination of weather conditions (WC), sunshine time accumulation (STA/h), instantaneous total irradiance (ITI/(W/m2)) and irradiance daily accumulation (IDA/(MJ/m2)). The model performance was rigorously evaluated with 9 prediction lengths, 5 training days, 4 seasons and 5 days for 9 weather conditions. The results showed that the Transformer model had the best overall prediction performance for STA, ITI, IDA and WC at different time steps of 10 min to 24 h. All models were suitable for predicting time series within 1 h. However, TimesNet model was not suitable for predicting time series with steps outside 1 h. On the other hand, by using the sun combination dataset, the Transformer model had the best performance at a time step of 10 min. The mean absolute error (MAE), mean-square error (MSE), root mean squared error (RMSE) and coefficient of determination (R2) of ITI were 0.118 W/m2, 0.059 W/m2, 0.243 W/m2 and 93.9 %, respectively. When exploring the minimum dataset, with the increase of data samples, the prediction effect of TimesNet showed an increasing trend. While, Transformer had the best prediction effect for the dataset with one year of use. When exploring seasonality, Pyraformer model had the best prediction effect on winter and summer, and TimesNet had the best prediction effect on autumn and spring. Local prediction of 9 climate conditions showed that the effects of snow and dust storm were not ideal. The research results showed that the characterization factors that were closely linked to irradiance. The prediction scheme proposed in this paper combined the advantages of different time steps, different factor combination datasets, different data volumes and seasonality, which greatly improves the generalization ability of the model. This study can provide a reference for irradiance prediction and more refined management of photovoltaic (PV) greenhouse. [ABSTRACT FROM AUTHOR]

• Develop algorithms for sizing grid-connected bifacial PV power plants. • HHOSA identifies the optimal configuration of components and installation parameters for bifacial PV power plants, maximizing or minimizing performance metrics. • HHOSA efficiently handles multiple design options within a short timeframe. • Techno-economic analysis of bifacial PV power plant. • Sensitivity analysis on bifacial PV parameters, cost considerations, and tailored ground cover enhance bifacial PV power plant success. This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm's stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system's Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations. [ABSTRACT FROM AUTHOR]

• An adaptive MAE evaluation for the lithium battery in hybrid UAV applications was proposed. • An OSMO was developed to estimate the real-time lithium battery OCV. • Adaptive MAE evaluation for the lithium battery can be achieved with the real-time estimated OCV. • The MAE evaluation method was adaptive to dynamic ambient temperature and uncertain aging levels. Accurate evaluation of the maximum available energy (MAE) of the lithium battery is crucial for energy management and optimization in hydrogen-electric hybrid unmanned aerial vehicle (UAV) applications. However, uncertainties in ambient temperature and cell aging level would affect the MAE of the lithium battery, posing challenges for its accurate state evaluation. This paper presents an adaptive MAE evaluation method based on an optimized sliding mode observer (OSMO) for the lithium battery. The study begins by conducting comprehensive long-term experimental tests to analyze the dependencies of the lithium battery's MAE on different ambient temperatures and cell aging levels. Subsequently, an OSMO with a Kalman filter is developed to estimate the variations of the battery's open circuit voltage (OCV) on the basis of a dynamic lithium battery model. Furthermore, the real-time OCV is successfully used to achieve accurate adaptive MAE evaluation for the lithium battery. The originality of this paper is that the model parameters and proportional coefficients can be updated adaptively based on the OSMO with the Kalman filter. In this case, the OCV can converge to their actual value such that accurate MAE evaluation can be guaranteed, even with uncalibrated model parameters under dynamic ambient temperature and uncertain aging levels. Finally, experimental and simulation results verify that the proposed method exhibits a convergence speed four times higher than the traditional method. The mean absolute percentage error (MAPE) of MAE evaluation is less than 1.5 % under dynamic ambient temperature and uncertain aging levels. [ABSTRACT FROM AUTHOR]

• High specific speed Francis turbine as an alternative to low specific speed Kaplan/semi-Kaplan/Propeller turbine. • Comprehensive experimental studies of the high specific speed Francis turbine. • Multi-aspect experimental approach to determining the Thoma cavitation number. • Comprehensive vortex visualization for various cavitation states in the draft tube. • Saddles were found on the performance characteristics for high guide vane opening angles. The paper presents comprehensive experimental research on a high specific speed model Francis turbine with a characteristic runner diameter of 250 mm under the head of 12 m. Essentially, the turbine was designed for use at heads between 10 and 50 m, which are typical for installations with low specific speed Kaplan turbines. The basic performance, cavitation and runaway characteristics of the machine, which were determined as a result of the work, are quite rarely available in the literature in the case of high specific speed Francis turbines. The turbine achieved the maximum efficiency of 91%, kinematic specific speed of 82, and relatively wide operation range, so that it can be attractive as an alternative to the low specific speed Kaplan, semi-Kaplan or propeller turbines. In result of the basic laboratory tests, the performance characteristics of the machine operation in a wide range of guide vane opening angles between 9° and 36° as well as the shell efficiency characteristics were determined. On the characteristics plotted vs. rotational speed, the saddle effect was identified for the guide vane opening angles with values greater than the optimal one (22°). During the operation of the machine in the area of saddles, the change in the flow rate took quite surprising patterns. The share of the turbine thrust torque in the total torque was also determined, the characteristics of which are also burdened with a similar saddle effect. In the case of cavitation tests, a multi-aspect approach to cavitation diagnostics was used to characterize the cavitation phenomenon from the viewpoint of various criteria. Following this approach, the signals of the draft tube wall acceleration, acoustic emission at the draft tube cone upper flange, and pressure fluctuations inside the draft tube were used. The determined cavitation characteristics indicate little cavitation susceptibility of the Francis turbine blade system, despite the presented turbine having been designed to operate at higher flow rates than the classic high head Francis turbines with long interblade channels. The cavitating flow in the draft tube, directly below the runner, was also visualized during the cavitation tests. The paper presents the results of visualization for all cavitation states at five selected settings of the guide vane opening angle (two states in Partial Load, two states in High Load area and one state in the Best Efficiency Point), thanks to which it was possible to track the development of vortices during pressure reduction in the flow system. The runaway coefficient was determined as a result of the runaway tests. The value of 1.497 was reached at the optimum guide vane opening angle. Additionally, the work also presents in detail the efficiency scale effects to be expected at prototype turbines, i.e. those intended for operation at a higher head and with larger runner diameters. The efficiency of the machine with a runner diameter of 1.5 m and working at a head of 50 m reaches ∼93%. The influence of water viscosity on the change in efficiency of the prototype turbine is also shown. The presented methodology can be used for the proper selection of turbines intended for hydropower plants and for the proper installation of the runner in respect to the tailwater level in order to avoid the phenomenon of cavitation erosion. [ABSTRACT FROM AUTHOR]

• A novel wind power prediction method based on influencing factors is proposed. • A multivariate model based on TCN-ReOSELM is proposed for the first time. • PC technology is utilized to process the input data of the model. • An improved AHA algorithm is introduced to optimize the TCN-ReOSELM model. • The superiority of the proposed model is verified by ten benchmark models. Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial auto-correlation function and cross-correlation function (PACF&CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high-dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60–64%, 55–58%, and 10–36%, respectively, compared to the PC-TCN-ReOSELM model. [ABSTRACT FROM AUTHOR]

• The novel preignition event (PEMC) was explored on HDI engines. • PEMC event occurred in the early/mid compression stroke and with the peak pressure at TDC. • PEMC events worsened NOx emissions, BP, and BTE in most cases. • Delaying injection timing beyond 180° CA BTDC could totally prevent PEMC events. • The established RF prediction model has excellent robustness and reliability. The potential use of hydrogen in automotive internal combustion engines could aid in the energy transition and contribute to achieving carbon neutrality. This paper has investigated the impact of operating parameters boundary for a novel preignition event on a 2.0L hydrogen direct injection (HDI) engine to address the challenge of abnormal combustion limiting the application of HDI engines. The identified characteristics of the event and its potential harm to the engine has been summarized. Experiments have shown that the novel preignition event predominantly occurred mainly during 170−80°CA BTDC, hence the name PEMC (preignition in the early/mid compression stroke) event, and caused a surge in cylinder pressure. In 77.2 % of the selected cases, the PEMC events resulted in increased NOx emissions. In 93.1 % and 91.5 % of the cases, there was a decrease in brake power and brake thermal efficiency, respectively. Unlike the low speed, high load preignition of gasoline engines, PEMC events were more likely to occur in the torque range of 70–100 Nm and could be monitored in a lean mixture environment with λ = 1.5–3.3. PEMC events were found in the interval of 180−320° CA BTDC at injection timing and were most intense at 240−250° CA BTDC. To prevent PEMC events completely, it is recommended to delay the injection timing beyond 180° CA BTDC. The paper has employed a model based on experimental data and Random Forest algorithms to predict PEMC events. The model demonstrated an accuracy of 98.76 % and 97.69 % for the training and test data, respectively. It has been effective in predicting the occurrence of PEMC events under various operating conditions. The paper has offered an alternative solution for the novel preignition in HDI engines. [ABSTRACT FROM AUTHOR]

[Display omitted] • Calcium looping (CaL) captures carbon dioxide and waste heat from industrial flue gases. • Methane dry reforming (MDR) produces blue hydrogen with high thermal energy penalty. • Synergy of MDR and CaL shows huge potentials for industry decarbonisation. • Major challenges are catalyst deactivation, sorbent sintering and system integration. Industry is the biggest sector of energy consumption and greenhouse gas emissions, whose decarbonisation is essential to achieve the Sustainable Development Goals. Carbon capture, energy efficiency improvement and hydrogen are among the main strategies for industrial decarbonization. However, novel approaches are needed to address the key requirements and differences between sectors to ensure they can work together to well integrate industrial decarbonisation with heat, CO 2 and hydrogen. The emerging Calcium Looping (CaL) is attracting interest in designing CO 2 -involved chemical processes for heat capture and storage. The reversibility, relatively high-temperature (600 to 900 °C) and high energy capacity output, as well as carbon capture function, make CaL well-fit for CO 2 capture and utilisation and waste heat recovery from industrial flue gases. Meanwhile, methane dry reforming (MDR) is a promising technology to produce blue hydrogen via the consumption of two major greenhouse gases, i.e., CO 2 and CH 4. It has great potential to combine the two technologies to achieve in-situ CO 2 utilization with multiple benefits. In this paper, progresses on the reaction conditions and performance of CaL for CO 2 capture and industrial waste heat recovery, as well as MDR were screened. Secondly, recent approaches to CaL-MDR synergy have been reviewed to identify the advantages. The major challenges in such a synergistic process include MDR catalyst deactivation, CaL sorbents sintering and system integration. Thirdly, the paper outlooks future work to explore a rational design of a multi-function system for the proposed synergistic process. [ABSTRACT FROM AUTHOR]

• A dynamic model for a 300 MW subcritical power plant boiler is developed. • A novel dynamic exergy analysis and assessment model is proposed. • The influence of drum level control on dynamic exergy behavior is evaluate. • The asynchronous responses are discussed in flow and heat transfer processes. • The dynamic exergy analysis reveals exergy destruction rates of heating surfaces. In the era of high penetration of renewable energy sources (RESs), traditional coal-fired power plants are facing increasing requirements of operational flexibility which is limited by the inherent slow dynamics of boiler. To this end, this paper presents a comprehensive dynamic model of the heating surfaces for a 300 MW power plant boiler and analyzes the exergy performance from the perspective of transient responses. Based on the object-oriented modelling language Modelica, the multi-domain first-principal model is developed by unifying the equations of thermal, mechanical, liquid and control components and interfaces. The model accuracy is validated by the field measurements. The model takes into account the multi-scale time constants in terms of the flow and heat transfer processes and drum level control. As an extension to the conventional steady-state thermodynamic analysis, a methodology of dynamic exergy analysis and evaluation is proposed. Dynamic simulation results well describe the initial reverse response of the drum level, i.e., false water level indicator, as a result of which the mass flow rate of feedwater exhibits the oscillatory performance. It is revealed that the step disturbances of flue gas mass flow and feedwater temperature, respectively, renders the temperature responses in different positions being of multi-scale setting-time and magnitudes. The exergy evaluation reveals that the water-wall suffers from the largest dynamic exergy destruction, approximately 8.2 and 8.5 times higher than the superheater and economizer, respectively. Economizer is the most sensitive component to the drum level control performance, whose dynamic exergy performance oscillates significantly. The proposed dynamic modelling and analysis method in this paper lays a solid foundation for the development of dynamic optimal control in power plants. [ABSTRACT FROM AUTHOR]

• An inflatable structure is designed for Savonius wind turbine. • A double-layer fabrication process is proposed for the inflatable wind turbine. • Geometry parameters of the inflatable wind turbine are optimized based on CFD. • Static and dynamic performances are investigated through wind tunnel tests. • The inflatable wind turbine shows high portability and rapid deployment capability. Faced with the increasingly severe issue of the greenhouse effect, wind power emerges as a sustainable solution. However, the large size, heavy weight, and high risk associated with traditional wind turbines restrict their widespread deployment in urban, rural, and outdoor settings , wasting a vast amount of untapped wind energy resources. To address these challenges, this paper proposes an inflatable structure for the Savonius wind turbine and explores a double-layer fabrication process to manufacture the inflatable Savonius wind turbine (ISWT). 2D simulations are carried out on ANSYS-Fluent. Within predetermined parameter ranges, this paper successfully identifies the optimal geometric parameters for the ISWT model, with a blade thickness of 7 cm and a blade arc angle of 120°. This model exhibits the maximum power coefficient (C p) of 0.1912 at the tip speed ratio (λ) of 0.7. The manufactured ISWTs are fabricated based on the optimal geometric parameters. Due to deformation after inflation, the actual blade arc angle of the first ISWT sample is only 89.7°. Therefore, by adjusting the fabric template, an approximate blade arc angle of 120° is achieved for the second ISWT sample. Both ISWT samples are subjected to open-section wind tunnel tests. The results revealed that while the C p curves of Sample 1 and Sample 2 display trends similar to those in numerical results, the overall performance is lower. The reasons for the deviation include the simplification of vertical flow in 2D simulation and the lack of endplates. Specifically, Sample 2 also achieves the highest C p value of 0.1486 at λ = 0.7. Due to an inflation time within 60 s to the required pressure of 1.4 bar and a compressed volume lower than 0.021 m3, the ISWTs demonstrate rapid deployment capability during installation, portability during transportation, and storability during severe weather. [ABSTRACT FROM AUTHOR]

• Spatio-temporal correlation analysis is performed for different power supply characteristics. • A combination model of MI-DFS-K2-Bayes is proposed to solve the spatio-temporal problem. • The improved k -means algorithm improves the accuracy of multiple power center curves. • Matching of running scenarios to point predictions is solved by setting upper and lower bounds. This paper proposes a novel method for scenario generation and scenario forecasting considering wind-photovoltaic-hydro-thermal power sources. With the rapid development and application prospects of energy internet and grid modernization, accurate source-side multiple output modeling and forecasting is essential to observe and estimate the multiple curves. It is becoming more and more important to optimize the operation efficiency of power grid. The traditional methods mainly focus on multi-scene generation of a single power source. Grid scheduling requirements that could not take into account multiple time correlations and multiple scenario forecasting. The paper proposes a scenario forecasting method considering scenario generation. Firstly, mutual Information (MI) and depth-first traversal (DFS) are combined to analyze the factors associated with power generation and build the MI-DFS-K2-Bayes network to generate multiple scenarios of wind- photovoltaic-hydro-thermal power. Secondly, the improved k -means algorithm is proposed to obtain the center curve of each power source. The algorithm improves the accuracy of the multivariate clustering center curve. It solves the problem that the initial clustering center and the number of clusters are difficult to select, and builds the multivariate running state. Finally, the upper and lower interval of point forecasting constrained by confidence parameters is proposed to make the running state scene match the point forecasting. The future state curves of each power source in short time scale are obtained. The future state scenario is implemented. Numerical examples demonstrate that the proposed method reduces the modeling error of wind- photovoltaic-hydro-thermal power curves by 0.13%–0.4%. The overall forecasting accuracy is significantly better than the state of the study. [ABSTRACT FROM AUTHOR]

[Display omitted] • Biomass gasification to produce tunable syngas for methanol model was developed. • Overcame the challenge that biomass syngas cannot be directly utilized for methanol. • Effects of Steam/BBF, CaO/BBF and CO 2 /Biochar on the syngas were investigated. • Conducted exergy and global warming potential analyses of the system. The production of methanol through biomass gasification is a hot topic in current research. However, the challenge lies in obtaining the correct H 2 /CO ratio, as the syngas generated from biomass gasification cannot be directly utilized for methanol synthesis. This paper proposed a biomass sorption-enhanced gasification coupled with carbon utilization to produce tunable syngas for methanol synthesis model in Aspen Plus. The effects of Steam/BBF, CaO/BBF and CO 2 fraction on the composition and high heating value (HHV) of syngas were evaluated. This paper focused on the H 2 /CO ratio, the exergy efficiency and the global warming potential (GW). The study found that with the CO 2 fraction of 0.6, the H 2 /CO ratio in the syngas approximated 2, with a yield of 257.19 kmol/h, making it suitable for methanol synthesis. The exergy efficiency of the system was ∼ 64 %, with the sorption enhanced water gas shift unit, gasification unit, power generation unit and methanol synthesis unit being the primary sources of exergy loss. Furthermore, in the global warming potential analysis, BBF gasification for methanol production stage and the biomass processing stage accounted for more than 97 % of the total GW. Improving the gasification for methanol production unit was crucial for enhancing the overall sustainability of the system. This study provides valuable insights into biomass sorption-enhanced H 2 production and biomass-to-methanol conversion. [ABSTRACT FROM AUTHOR]

Changes in wind speed directly affect the output of wind turbines, so in power system planning, accurate prediction of future wind speed can help adjust the stability and reliability of power networks. In order to solve the problem that unknown wind speed prediction systems are unable to select features autonomously and to alleviate the insufficient prediction accuracy of single interval type 2 fuzzy models, this paper proposes a new deep convolutional interval type 2 fuzzy system (DCIT2FS) based on adaptive feature selection. First off, type 2 fuzzy sets are employed in the construction of two stage interval type 2 fuzzy curves and surfaces to address the issue of dimensionality explosion brought on by complex system modelling. By removing inaccurate and redundant information, this strategy achieves automated screening of key input aspects of an unknown system. Second, the model's interpretability is improved by the addition of the interval type 2 fuzzy system to the deep convolutional structure, which also addresses the issue that a single interval type 2 fuzzy model cannot achieve the required level of prediction accuracy. Thirdly, the incorporation of the feature pyramid structure boosts the mining of underlying data to increase the model's capacity to predict outcomes accurately while simultaneously strengthening its resistance to interference. Finally, model prediction performance tests are completed using meteorological feature data for both time periods, and generalisability tests are conducted using the benchmark dataset. The experimental results show that the prediction accuracy of the model proposed in this paper is improved by at least 7.70% and 1.23% in two wind speed prediction experiments compared to other models. • A new deep convolutional interval type 2 fuzzy prediction model is designed. • The feature pyramid structure is added to DCIT2FS in an innovative way. • A method TSIT2FCS is designed to realise the automatic selection of input features. • The hybrid model TSIT2FCS-DCIT2FS is successfully applied in wind speed prediction. [ABSTRACT FROM AUTHOR]

• Hemicelluloses-rich aqueous byproducts from WTCPs are often disposed of as waste. • Valorization of hemicelluloses-derived products into fuels, chemicals, and bioproducts. • Circular bioeconomy and use of all biomass processing byproducts from WTCPs. • Challenges to integrate and scale up aqueous byproduct processing in biorefineries. Thermochemical pretreatments are employed prior to energy, chemicals, and fuels production from biomass. Wet thermochemical processes (WTCP) are treatments used to modify biomass properties in water as the primary solvent, with or without added reactants/catalysts. WTCP includes hot water extraction, steam explosion, hydrothermal liquefaction, hydrothermal carbonization (HTC), and supercritical water gasification. WTCP also includes processes that add chemicals to reduce reaction time and improve efficiency, i.e., organosolv, alkali, and low acid pretreatment. Operational parameters in WTCP are usually selected to optimize the yields of sugars after enzymatic hydrolysis of the resulting solids and biogas from the pretreated solids, or to ensure that hydrochar (e.g., from HTC) performs adequately in environmental applications. However, a key byproduct from WTCPs is an aqueous fraction (rich in nutrients, hemicellulose-derived sugars, and chemicals) often disposed of as waste. The necessity of resource conservation and proper management and the need to make WTCP-based biorefineries economically and environmentally sound require using all the byproducts of biomass processing. Options for downstream conversion of the WTCPs' aqueous byproducts are dispersed in the literature. Thus, this paper aims to put together works that report the parameters of WTCPs that allowed removing hemicellulose-derived fractions and nutrients from biomass (either partially or almost entirely), the yields and properties of this aqueous byproduct, methods of characterization, current and expected uses, and the challenges for scaling up WTCPs and using the aqueous stream. The paper focuses on expected and existing methods that allow the valorization of the aqueous fraction and reduce wastes within a circular bioeconomy framework. [ABSTRACT FROM AUTHOR]

• Proposed a novel advanced solar-driven combined heat and power system. • Constructed the overall heat current model and exergy analysis expression. • Analyzed the exergy loss and efficiencies of each component and the system. • Analyzed the influences of various parameters on the system exergy efficiency. Conducting research on advanced cycle of distributed energy system and improving energy utilization efficiency is an important approach to promote the development of distributed energy systems. The paper proposed a novel solar-driven supercritical carbon dioxide-heat pump cogeneration system based on solar power tower system and performed the overall modeling of the system using the heat current method. The overall exergy loss calculation model of the system was established by applying the exergy analysis method incorporating the standard thermal resistance-based entropy generation rate expression. The calculation results present that under rated condition, irreversible loss mainly occurs in the solar energy conversion part, accounting for 73.83 %, and the system exergy efficiency is 34.69 %. Besides, the paper investigated the steady state performance of the system under different working conditions and the influence of the turbine inlet temperature and the intermediate pressure on the system performance. When the direct normal irradiance increases, the proportion of exergy loss in solar energy conversion part increases by 62.13 %, while the change of system exergy efficiency depends on the operating state of the system. The optimum intermediate pressure is 11.72 kPa, when the net power output and the exergy efficiency of the whole system reaches the maximum. Finally, the proposed system contributes to the improvement of cascade utilization of energy, and the modeling method is effective for solar-driven supercritical carbon dioxide cogeneration systems. [ABSTRACT FROM AUTHOR]

• The thermal, rheological, and optical properties of ferrofluids and how they change with magnetic fields were discussed. • The utilization of ferrofluids in solar energy systems was detailed in a state-of-the-art analysis. • The recommendations aimed to optimize the use of ferrofluids and magnetic fields in energy systems to enhance performance. Solar energy technologies play a crucial role in the transition to sustainable energy sources; however, there is an ongoing challenge in optimizing their efficiency and performance. This review paper thoroughly exposes the potential for enhancing solar systems by incorporating ferrofluids and utilizing magnetic fields. It provides a comprehensive examination of the rheological, thermal, and optical properties of ferrofluids, as well as their behavior when exposed to magnetic fields. The paper emphasizes the ability of ferrofluids to improve heat transfer efficiency, adapt to changing conditions, and enable precise control in the conversion of solar energy. Furthermore, the review addresses the obstacles and limitations associated with integrating ferrofluids into solar technologies, proposing potential solutions and outlining future research directions. [ABSTRACT FROM AUTHOR]

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building's heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively. • A novel cloud-based MPC scheme is proposed for controlling the educational building HVAC system. • Real-world case installation details are provided with a focus on cost and sensor information. • A comprehensive discussion on thermal modeling, occupants' comfort, and control performance. [ABSTRACT FROM AUTHOR]

Medium and long-duration energy storage systems are expected to play a critical role in the transition towards electrical grids powered by renewable energy sources. ACAES is a promising solution, capable of handling power and energy ratings over hundreds of MW and MWh, respectively. One challenge with ACAES is achieving the required highly efficient operation in the compressor over the range of conditions encountered in the system as the pressure in the air store changes. In this paper, an application-oriented axial-flow compressor is designed, aiming towards efficient operation throughout the operation range, whilst also associating the performance prediction to a practical compressor geometry. A two-step design methodology based on inviscid, axisymmetric flow conditions has been implemented, leading to the flowtrack, blade-row geometries and the compressor performance map. The compressor model is integrated into an ACAES model, including two compression spools, two expansion stages with preheat, a constant volume high pressure storage operating between 5.5 and 7.7 MPa and two separate Thermal Energy Storage units. While the existing ACAES literature either ignores the transient off-design operation or uses generic numerical correlations (which are not associated to a particular geometry), the key novelty of this paper is the application of a detailed design method for turbomachinery to ACAES. The results indicate that the designed compressor requires 33 stages over the two spools, and is able to operate efficiently over the storage pressure range, showing that if the application-oriented design procedure is applied to the compressor, it does not stop ACAES reaching 70% round-trip efficiency, outputting 35MW for approximately 15 h. Importantly, the specific ACAES requirement of conserving heat at higher temperatures has been fulfilled by decreasing the number of intercoolers. Finally, it is recommended that a similar level of scrutiny is applied to the other components (i.e. expanders, heat exchangers and TES units), keeping in mind the unique set of operational requirements of ACAES. This work is an important step towards removing the common misconception that off-the-shelf components can be easily be used in typical ACAES designs. • ACAES imposes unique conditions on its components, aiming system reversibility. • The compressors must run efficiently and keep the airflow temperature high. • This paper develops an axial-flow compressor geometry and performance map for ACAES. • The compressor is integrated into an ACAES model, operating within 5.5 and 7.7 MPa. • The ACAES system results indicate the system reaches 70% RTE after stabilisation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Assessed concepts combine biogas- and electricity-based methanol production • Adding electricity-based hydrogen increases methanol production by 28 to 67% • Tri-reforming enables high biogas conversion for a total carbon utilization of 97% • Bi-reforming exceeds energy efficiency of tri-reforming by 5%pt. in overall concept • Methanol production costs are estimated at 1,300 € 2022 /t The limited potential of sustainably available biomass requires efficient conversion to defossilize future demands of carbon-based chemicals and energy carriers. Therefore, integrating electricity-derived hydrogen into biomass-based production concepts appears promising, maximizing carbon utilization while minimizing biomass requirement. However, specific process concepts and analyses are still lacking to quantify the potential technical and economic benefits of such hybrid production processes. Thus, this research paper investigates this novel approach for decentralized methanol production from biogas within a techno-economic analysis, considering two production configurations. One configuration (BiRef) utilizes electrically heated bi-reforming for syngas generation, while the other configuration (TriRef) uses autothermal tri-reforming. Based on stationary process simulations, in-depth knowledge about process behavior is gained, efficiencies and costs are determined, and influencing factors are assessed. Within the reference case, the BiRef configuration achieves a carbon efficiency of 93 %, while the TriRef configuration reaches 97 % due to its enhanced methane conversion. Energy efficiency is 5 %pt. higher within the BiRef configuration (74 %), primarily due to the lower hydrogen demand. Carbon and energy efficiency are crucially affected by reforming temperature and inert gas fraction in the syngas. The economic analysis shows methanol production costs of ca. 1,300 € 2022 /t for both configurations. Variations in economic parameters emphasize advantages for the TriRef configuration when low hydrogen costs (<6 € 2022 /kg) are achievable. The comparison with purely biogas-based methanol production shows that hydrogen addition increases production capacity by up to 67 %. Cost advantages result against purely electricity-based production, while cost parity is achieved with purely biogas-based production. This paper demonstrates the potential of hybrid methanol production for efficient biogas utilization and highlights decisive parameters influencing technical and economic efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sorting strategies based on source, physical and chemical properties were compared. • The gasification efficiency of sorted RMSW was 40.89% higher than that of unsorted. • The yield of syngas can be increased by 37.14% after waste sorting. • The emission of CO 2 can be reduced by 81 t·per year after waste sorting. • Elemental composition based sorting method benefited to efficient gasification. Gasification is a promising way to convert residual municipal solid waste (RMSW) to high-valued syngas. Waste sorting is helpful to alleviate the heterogeneity problem of waste on gasification. This paper aimed to search for an optimal sorting strategy and evaluate the influence of waste sorting on downstream gasification. A sorting strategy based on elemental composition was proposed and compared with common source and density based strategies. The results validated the enhancement of waste sorting on RMSW gasification, where the elemental composition based strategy showed optimal gasification. The yield of syngas, cold gas efficiency, the lower heating value of syngas and H 2 /CO were increased by 37.14 %, 38.95 %, 2.18 % and 8.81 %, respectively. It also showed a good effect on the improvement of air gasification compared with the literature, especially for H 2 /CO. This paper also discussed the economic and environmental potential of the elemental composition based sorting method. The waste sorting can help increase 29,250 RMB/year and reduce CO 2 emission by 81 t/year. This method has potential for industrial application. It is suggested that further fast elemental composition characterization technologies and intelligent sorting equipment can be developed. Limitations and future perspectives were discussed from gasification simulation models, more energy utilization technologies, the underlying logic of waste sorting and sustainability evaluation. It is hoped that this work can provide insightful fundamentals to their applications in RMSW sorting, thus benefiting resource recovery and circular economy. [ABSTRACT FROM AUTHOR]

The objective of this paper is to implement an automated system for the control of all energy resources and devices within smart buildings. The paper develops a Two-Layer Electric Water Heater Management (TL-EWHM) system tailored for efficient power distribution within a smart microgrid connected to both a photovoltaic (PV) system and the Electric Grid (EG). The TL-EWHM consists of two distinct layers: the Offline Optimization Layer (OFF-OL) and the Online Optimization Layer (ON-OL). Within the OFF-OL, an optimization algorithm known as Particle Swarm Optimization (PSO) is employed to determine the optimal power settings for Electric Water Heaters (EWH) based on forecasts of several factors including PV generation, ambient temperature (A T), water demand (W D), and the consumption of fixed appliances (F A C) for the upcoming 24 h. These parameters are forecasted using a novel prediction algorithm referred to as regressor stacking, which is developed based on actual data. Within the ON-OL, real-time data is effectively utilized, and an Extremum-Seeking (ES) Controller is employed to continuously adapt the EWH power settings in response to uncertainties in the predictions. Furthermore, a strategic approach is being devised to generate the water temperature setpoints (W T S) by considering dynamic data factors like P V power, total energy consumption (P L), and the water demand profile to ensure that user comfort is maintained. A comprehensive comparative analysis is carried out to evaluate the effectiveness of our strategy approach. This strategy is characterized by the inclusion of a Water Temperature Setpoint Generator (W T S G) and the integration of a two-layer optimization methodology that combines the OFF-OL and the ON-OL. In this comparative study, our strategy is assessed in comparison to other conventional approaches, with the objective of highlighting the distinctive advantages of our method in terms of cost reduction and the enhancement of user comfort. On the one hand, comparisons are made with strategies that give priority to offline optimization. On the other hand, the strategy that maintains a constant water temperature setpoint throughout the day. The results of this paper establish the effectiveness of the proposed strategy. In particular, the energy cost savings associated with the two-layer optimization approach is 13.73%. Even more noteworthy is the significant reduction in energy costs, which reaches an impressive 18.63% when the variation of water temperature setpoints is taken into account. These results underscore the significant benefits of using a two-stage optimization strategy over relying solely on offline optimization. They also underscore the value of incorporating a WTSG as opposed to maintaining WTSC. • Real-time optimization of a electric water heater (EWH) under model uncertainties. • The two-layer management approach enhances robustness to operating conditions. • This paper proposes a new strategy for variable water temperature setpoints. • The objective is to minimize energy costs while maximizing occupant comfort. • MLP stacking reduces overfitting, boosts flexibility by diverse dataset training. [ABSTRACT FROM AUTHOR]

• This study reports a novel approach for hydrogen production and measurement of coated 3D-printed electrodes for hydrogen evolution reaction in alkaline water electrolysis. • The initial hydrogen rate is reported to be 2.48 × 10-9 kg/s and 2.37 × 10-9 kg/s respectively for nickel-copper non-conductive PLA-conductive nickel paint electrodes and nickel-copper-coated conductive PLA. • The approaches investigated provide the potential for optimizing the electrodes for electrolysis, offering a promising avenue for design improvements. This paper introduces a unique approach for hydrogen production and measurement of coated 3D-printed electrodes for hydrogen evolution reaction in alkaline water electrolysis. The method investigated in this study involve using non-conductive and conductive polylactic acids (PLAs) followed by various coating techniques to fabricate cathodes for alkaline water electrolysis. During the electrodeposition process, nickel and nickel alloys, including nickel-copper, nickel-iron and nickel-molybdenum, are investigated as coating materials. This paper also provides valuable data on hydrogen production and the respective cathode energy and exergy efficiencies. The study finds that the hydrogen production rate is the highest at the start and declines over time. The hydrogen rate for the first 15 min is reported to be 2.48 × 10-10 kg/s and 1.38 × 10-10 kg/s after 30 min for nickel-copper coated electrode where non-conductive PLA is used along with conductive paints. In comparison, the hydrogen rate for the electrode with nickel-copper coated directly on the conductive PLA is 2.37 × 10-10 kg/s for the first 15 min and 1.35 × 10-10 kg/s after 30 min. Interestingly, the coated conductive PLA electrodes are shown to be more efficient despite slightly lower hydrogen production rates. Moreover, the unique approaches employed in this paper are expected to provide a potential cost-effective approach for improving the performance of the electrodes for electrolysis which can lead to the development of more efficient hydrogen production for industrial applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposed a low-carbon fuel ship power system configuration approach. • Ship demands from the perspective of water area, routes and ship types. • Constructing green low-carbon fuel ship power system from five dimensions. • Demand-configuration-integration-evaluation form a selection framework. The current global consensus to reduce greenhouse gas emissions has placed the maritime industry under increasing pressure to achieve carbon reduction and decarbonization. Low-carbon and zero-carbon power stand out as crucial means for ships to effectively cut emissions. However, implementing such power for ships is immensely intricate due to constraints like water conditions, routes, vessel types, demand, alternative fuels, and carbon reduction policies. Addressing these challenges, this paper establishes a high-level framework for low-carbon power systems in ships. Firstly, it focuses on low-carbon fuels like liquefied natural gas (LNG), liquefied petroleum gas (LPG), methanol, and biofuels, analyzing and summarizing the demands for low-carbon power in ships. Secondly, it offers a comprehensive overview of the configuration elements of low-carbon ship power systems across five dimensions: fuel, power module, propulsion method, aftertreatment module, and propulsion module. Based on this analysis, the paper proposes a configuration approach termed "demand-configuration-integration-evaluation" and presents six typical configurations for low-carbon ship power systems. Lastly, the paper outlines steps for evaluating low-carbon power systems. This framework and methodology cater to various water conditions (inland/offshore/offshore) and are adaptable to different vessel types (container ships, bulk carriers, cruise ships, and offshore engineering vessels). This systematic study offers crucial insights and references for ship designers and owners in the selection and design of new low-carbon power ships. [ABSTRACT FROM AUTHOR]

• Constructed the cross-scale dynamic model by standardized thermal resistance method. • Proposed a thermo-electric coupling analysis method to describe the dynamic response characteristics. • Developed multi-scenario parameter influence analysis on the response characteristics. • Obtained a better response characteristic with the fuel inlet temperature and excess oxygen coefficient. Analyzing the dynamic characteristics of pairs of polymer electrolyte membrane fuel cell (PEMFC) is crucial for enhancing the efficiency and flexibility of the system in which it is utilized. In order to accurately reflect its dynamic process and investigate the interaction between the heat and electricity, this paper adopted the standardized thermal resistance method to build a dynamic model of PEMFC. On this basis, a simulation comparison with the experimental data is performed to verify the accuracy of the proposed model. Moreover, we propsoed a two-stage comprehensive response analysis method of the PEMFC under thermo-electric coupling with corresponding key parameters, such as the fuel inlet temperature, gas excess coefficient and load variation amplitude in multiple scenarios. The results show that the comprehensive response characteristics are better when fuel gas inlet temperature is 40℃ or excess oxygen coefficient is 2.4 in the corresponding scenarios. The model, analysis method and findings in this paper provide an effective reference for the development of optimal control strategies for the PEMFC stack temperature as well as other parameters to improve its comprehensive dynamic response performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explores catalysts and reactors in vital methanol production. • Covers methanol synthesis comprehensively. • Includes cutting-edge research on catalysts and reactors. • Offers valuable guidance for efficient methanol production. • Essential for industry professionals, revealing innovations. • Guides future research in an evolving industry. This review paper extensively examines the significance of methanol as a vital commodity chemical with broad applications in chemical synthesis and as a fuel source. The production of methanol heavily relies on the utilization of catalysts and industrial reactors. The primary focus of this paper is to provide a comprehensive analysis of various catalysts and reactor companies that play a crucial role in methanol production. The review delves into the synthesis of methanol, encompassing the feedstock used, reaction conditions, and the diverse catalysts and reactors employed in the process. Among the discussed reactor types and catalysts are prominent names such as Lurgi, Casale, Linde, Toyo, Mitsubishi, and Haldor Topsøe. The advantages and disadvantages of each catalyst and reactor technology are carefully evaluated, considering their performance, selectivity, stability, and cost. Furthermore, this study incorporates the most recent advancements in research and development endeavors geared towards enhancing methanol production. This includes innovations in catalyst and reactor design, as well as the exploration of alternative feedstocks. By thoroughly reviewing the current state of the field, the paper concludes by offering valuable recommendations for future research, aimed at addressing the key challenges faced by the methanol production industry. In essence, this paper is a valuable resource for researchers, engineers, and industry professionals actively engaged in methanol production. It sheds light on the latest developments in catalyst and reactor technology and their potential impact on the industry, making it a comprehensive and informative source of knowledge. [ABSTRACT FROM AUTHOR]

• An improved model DWTimesNet based on dilated convolution and WN is proposed. • RF was used to reduce the effect of missing data on the proposed model. • To eliminate the effect of some variables, MIC is used for feature selection. • The PBT algorithm is used to efficiently parameterize the proposed model. • Two cases with different data quality are used to verify the proposed model. The share of wind power in global electricity generation is increasing year by year, and the prediction of wind power is a practical and necessary scientific research. In this paper, the TimesNet model is used for multistep prediction of wind power. It is observed that the prediction error of the TimesNet model could be improved by replacing the original convolutional structure with the dilated convolution and weight normalization (WN). And in addition, the population-based training (PBT) algorithm is introduced to guide the model tuning. Missing values in the raw data are imputed to enhance data integrity. And the maximal information coefficient (MIC) method is chosen to select features for the different influences on wind power, which reduces the computational effort of the model. In order to demonstrate the effectiveness of the proposed model MIC-PBT-DWTimesNet, nine models are arranged in this paper as a control group and compared with their predicted values with each other. It can be observed that the RMSE of the proposed model MIC-PBT-DWTimesNet is decreased by 17–33% compared to the base TimesNet method. [ABSTRACT FROM AUTHOR]

• A new DPV-5G BS-ES communication and power mutual coupling system is developed. • A BSSCP model is proposed to minimize the number of 5G base station deployments. • The ISOM model is used to optimize the microgrid connection lines in 3D space. • Economically optimal simulation experiments are conducted for different PV systems. The emergence of ultra-dense 5G networks and a large number of connected devices will bring with them significant increases in energy consumption, operating costs, and CO 2 emissions. At the same time, the deployment of distributed photovoltaic (DPV) in megacities plays an important role in promoting the integration of "building-photovoltaic", adjusting the city's clean energy supply system, and building a green, and low-carbon smart city. The development of a new "DPV-5G Base Station-Energy Storage (DPV-5G BS-ES)" coupled DC microgrid system and its pre-deployment investment costs are fundamental factors to be considered when the problem of large-scale DPV and BS deployment in cities has to be addressed. There are almost no published papers on this issue in the literature, which only consider the problem of maximizing the deployment of 5G BS service coverage or only perform optimization analyses for the performance of microgrid systems without considering the economic optimality and low-carbon green issues in the construction of urban utilities. In order to solve the problem of 5G BS deployment and economic optimization of topology in the "DPV-5G BS-ES" coupled DC microgrid system, a novel deployment strategy is proposed in this paper. The strategy is divided into: (a) The Geographic Information System (GIS) for extracting and analyzing 3D data from all buildings in the area. (b) A GIS-based formulation of a mixed integer quadratic constrained planning model (BSSCP) under discrete fixed radius coverage constraints is used to delineate building clusters within the area and determine the optimal deployment location of 5G BS under each sub-cluster. (c) An Improved Self-Organizing Mapping (ISOM) model combines GIS and microgrid performance parameters to address the economically optimal connection of grid lines for DPV in 3D space. (d) Simulation experiments were conducted for the power output and profitability of eight different PV panels and tracking systems to calculate the economically optimal type of DPV generation hardware. The experimental results show that the BSSCP model proposed in this study reduced the 5G BS deployment cost input by up to 35.8974 % compared to that of the control experimental model. Meanwhile, the ISOM model obtained an average reduction of 52.3741 % in the cost of connecting cables for the optimized 25 DPV planning and DC microgrid topology. [ABSTRACT FROM AUTHOR]

Partial shading (PS) is a prevalent fault during photovoltaic (PV) array operation, significantly impacting power generation performance. An effective approach of PS recognition and fault severity diagnosis for PV array is proposed in this paper. This paper primarily includes current–voltage (I–V) data preparation, PV array modeling, and PS fault diagnosis. Data preparation involves preprocessing and conversion procedures for I–V curves, providing a dependable data source for subsequent studies. The converted I–V curves are the basis of the PV array modeling, which can simulate normal and fault I–V characteristics under target ambient conditions. PS fault diagnosis comprises pre-diagnosis, PS recognition and fault severity diagnosis. Pre-diagnosis is realized by quantifying the power degradation of the PV array. PS recognition and fault severity diagnosis are based on the number and position of the steps on the I–V curve. The experiments validate the effectiveness of the PV array modeling method and the PS fault diagnosis method. The root mean square error (RMSE) of the measured and simulated currents is employed to assess the modeling accuracy, within 0.03A. PS recognition accuracy surpasses 96%, and fault severity diagnosis achieves over 80% accuracy. Therefore, the proposed methods for PV array exhibits strong reliability and practicality. • I–V data preparation provides a dependable data source for proposed methods. • A PV array modeling method (CCFSM) has high modeling accuracy and practicality. • A multiple-PS fault diagnosis method for PV arrays exhibits excellent performance. [ABSTRACT FROM AUTHOR]

• A stack-level multiphysics model with bubble and shunt current effects is proposed. • The proposed ER curve is applied to quantify various factors on stack performance. • The reduction of single cell area will be a trend in size design for on-grid stacks. • The practical scale-up AWE stack coupled with RES is investigated. Large-capacity hydrogen production systems are crucial for promoting green hydrogen development. However, the scaling-up of electrolysis stacks through larger electrode areas or additional electrodes may increase the bubble coverage effect and shunt current effect, leading to efficiency degradation. Therefore, a quantitative model that considers interrelated factors is needed to optimize the size design during the scaling-up process. This paper presents a stack-level multiphysics model that describes the electrochemical and two-phase flow processes within the stack. The model is validated experimentally, with a relative error of the current–voltage (IV) curve within 4%. Based on the model, the energy efficiency-reaction current density (ER) curves of different stacks are analyzed and used to provide performance optimization strategies for stacks coupled with renewable energy sources (RES). The findings demonstrate that in on-grid mode, the small cell design leads to a hydrogen production rate increase of over 6%, driving a trend towards miniaturization of cell areas. In addition, during off-grid operation, the hydrogen production variations among different designs can exceed 4% due to performance differences under heavy and light loads. Therefore, optimizing electrolyzer performance requires considering power source fluctuations and conducting specialized computational optimizations based on specific scenarios. In summary, this paper proposes optimization strategies of size design for scaling up electrolysis stacks to improve stack performance, with the goal of driving the advancement of green hydrogen. [ABSTRACT FROM AUTHOR]

[Display omitted] • Green hydrogen LCOH for fuel cell buses in Fiji was found to be $9.08 /kg for grid connected and $13.00 /kg for off grid systems incorporating wind and solar energy. • Grid connected hydrogen generation system has lower NPC, LCOH and LCOE compared to an off-grid system. • A detailed methodology for executing a techno-economic analysis for green hydrogen is provided along with simple equations to adjust cost inputs for historical inflation. Daily operational results of the system are also presented. • Neglecting hydrogen compression load and costs can underestimate the LCOH for fuel cell buses by as much as 6.92 %. • The grid connected best case scenario led to a CO2 emission reduction of 93.9 % while the off grid best case resulted in a 100 % emission reduction compared to the baseline case. The study examines the feasibility of producing hydrogen for fuel cell buses in Fiji. The paper focuses on sizing hybrid microgrids comprising solar panels and wind turbines as the primary power source for hydrogen production while considering both off-grid and grid-connected cases. As no fuel cell vehicles exist in Fiji at present, five long-distance buses with daily travel of 380 km are proposed as a pilot scale baseline system, which would operate in the daytime only. Detailed technical and economic modelling was done using the HOMER Pro software with cost inputs derived from literature. A total of three cases for the on-grid and four cases for the off-grid systems are analyzed based on the various combinations of components. HOMER Pro optimized the grid configurations to minimize the Net Present Cost (NPC) values. All seven cases were also modelled with and without the effects of hydrogen gas compressors to emulate the production of low-pressure gas without compression and compressed high-pressure hydrogen required for fuel cell buses. It was found that a grid-connected system with solar and wind hybrid was the most feasible configuration and yielded the lowest NPC of $6 million. The grid-connected case also yielded a Levelized Cost of Electricity (LCOE) of $0.10/kWh and a Levelized Cost of Hydrogen (LCOH) of $9.08/kg with compression to 400 bars. The LCOH stood at $8.73/kg for hydrogen generation without compression. The most feasible off-grid configuration using solar and wind power attained an LCOE of $1.15/kWh while giving a LCOH of $13.00/kg for compressed hydrogen. It was also found that neglecting hydrogen compression load and costs can underestimate the LCOH by as much as 6.92 %. The off-grid best case had no CO 2 produced during its operation, leading to a net zero process that offsets 929.9 tons of CO 2 compared to the baseline. The grid-connected system does have some emissions but leads to a 93.9 % reduction in CO 2 emissions, offsetting 873.3 tons of CO 2 compared to the baseline. The study updated the cost inputs to account for inflation, logistics, and hydrogen gas compression, which are not available directly in HOMER Pro software. At the time of writing this paper, no feasibility study existed for hydrogen production in Fiji using renewable energy. [ABSTRACT FROM AUTHOR]

• Linear planning models of two types of compressed air energy storage are proposed. • A novel electricity-gas coupled energy hub for cascade utilization is structured. • Comparisons between energy-based and capacity-based energy storage are analyzed. Large-scale long-duration energy storage (LDES), like compressed air energy storage (CAES) and liquid air energy storage (LAES), is promising for high-penetration renewable energy consumption in the city-scale integrated energy system (IES). Due to high installation costs and challenges in decentralized control of distributed energy storage, this paper proposes an optimal planning model for an urban electricity-gas coordination hub (UEGCH) with centralized energy storage using large-scale LDES. Firstly, the UEGCH with energy cascade utilization is designed, where process-based linearized models of advanced adiabatic compressed air energy storage (AACAES) and LAES are established to make a trade-off between solving accuracy and speed. Then, a city-scale integrated electricity and gas system (IEGS) is constructed with multiple regions considering line-packing. Finally, a multi-scenario planning method is proposed based on X-means for scenario reduction, and the global optimum is obtained through linearization. Results show that compared with traditional gas storage tank (GST) in the UEGCH, the utilization of AACAES and LAES requires as little as 7 % and 0.7 % of the construction volume with lower carbon emissions and can be cost-competitive in terms of future advancement. As for different EESs, in contrast to the energy-based energy storage (EBES) (i.e., battery (BT)), the levelized cost of energy (LCOE) of the capacity-based energy storage (CBES), which includes AACAES and LAES, can be reduced by nearly 0.1 $/kWh, bringing an annual economic benefit of $ 3 million. Besides, the discharge duration of CBES can be increased to 13 h, demonstrating the application prospect in 100 % renewable consumption and islanding operation. Compared with AACAES, LAES can reach 11.67 times the energy density, with a drop of 6.11 % in operating cost. The abilities of EBES and CBES to locally consume renewables are respectively weaker and stronger than that of power-to-gas (P2G) combined with line-packing. Meanwhile, this paper further analyzes the impacts of temperature, discharge duration, renewable penetration, energy tariff, and demand on the operation and sizing of centralized energy storage. [ABSTRACT FROM AUTHOR]

• A hierarchical convex optimization framework for HEV-PCC is proposed. • A TSI-MPC approach is designed to address the speed planning problem. • An energy management strategy that combines regression model with PMP is designed. Predictive Cruise Control (PCC) achieves automated driving with improved vehicle economy by incorporating road gradient information from high-precision maps. However, existing research on PCC has primarily centered around conventional gasoline vehicles. Hybrid Electric Vehicle (HEV) offer superior fuel efficiency compared to their traditional counterparts. To further enhance vehicle economy by integrating the PCC system with HEVs, this paper proposes a hierarchical control architecture for HEV-PCC based on convex optimization. Firstly, considering spatial slope information from the road ahead, this paper introduces a Time-Space Integration Model Predictive Control (TSI-MPC) based on the principles of kinetic energy. Secondly, the paper combines a current regression model with the Pontryagin Minimum Principle (PMP) to present a Regression Analysis - Pontryagin Minimum Principle (RA-PMP) approach for the direct analytical solution of the energy management problem. Finally, the strategy is validated through simulation analysis and Hardware-in-Loop (HIL) testing on various real road scenarios. The result indicate that, in comparison to the Equivalent Consumption Minimization Strategy (ECMS) with constant speed cruising (CC), this method achieves an average fuel economy improvement of 9.45%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Optimal electrode configuration for industrial alkaline water electrolyzer. • Balance of Plant system design with full automatic running function. • Achieved 4.3kWh/m3H 2 @4000 A/m2 based on the optimized configuration. • Provided reference and guidance for electrode parameter selection and system design. • This study contributes to the deployment of more efficient industrial electrolyzer. The production of green hydrogen by water electrolysis is an effective way to realize the utilization of renewable energy and decarbonization, efficient water electrolysis equipment is of great significance to reduce energy consumption and accelerate the deployment of hydrogen production infrastructure. This paper reports the optimal electrode configuration adapted to actual industrial conditions. The aim of this paper is to study the electrode structure effect and system design of compactly-assembled industrial electrolyzer under the actual operating conditions, so as to find out more suitable electrolyzer configuration parameters. Firstly, the principle and structural parameter design of industrial electrolyzer are carried out, and compactly-assembled industrial alkaline water electrolyzer (22 cells) was manufactured. Secondly, a balance of plant (BOP) system was built and the fully automatic operation process was designed. Finally, a system-level alkaline water electrolysis (AWE) hydrogen production system (including compactly-assembled electrolyzer and BOP) was established. The effect of different expanded meshes and woven meshes on the energy consumption of AWE equipment was tested, and the system component cost was analyzed. Industrial system-level test results show that the electrode dominant parameters affecting AWE performance are different under varying current densities. From the perspective of industrial application, it is more suitable for the strand width and pore width of the woven mesh to be about 0.2 mm, it achieved 4.3kWh/m3H 2 @4000 A/m2 based on the optimized configuration parameters. This study is helpful to the selection of electrode parameters and optimal configuration for industrial AWE equipment, at the same time, it provides a reference for the design of electrolyzer, BOP and system control process. [ABSTRACT FROM AUTHOR]

• Organic Rankine cycle-ejector heat pump hybrid system performance is investigated. • The proposed trigeneration system utilized low GWP zeotropic mixtures. • Thermodynamic simulations are performed utilizing five proposed zeotropic mixtures. • The effect of a wide range of parameters on system's performance is analysed. • The thermodynamic model is an effective tool for system design and optimisation. This paper presents a comprehensive study focused on improving the thermal efficiency and performance of an organic Rankine cycle (ORC) combined with an ejector heat pump (EHP) for trigeneration application. The utilization of zeotropic mixtures with low global warming potential (GWP) was proposed as a solution to address the inherent inefficiencies of the ORC at low-temperature heat sources and to enhance the coefficient of performance (COP) of the ejector refrigeration cycle. A thermodynamic model was developed to predict system performance, utilizing low-temperature heat sources such as solar or geothermal energy, along with zeotropic mixtures. The model was validated with the available literature data, demonstrating very good agreement. Five zeotropic fluid mixtures were preliminary studied, and the optimal mass fractions identified. Detailed investigations were then carried out for two of these mixtures, R1233zd(E)/propane and butane/propane, which exhibit low GWP and ozone depletion potential (ODP). The influence of generator temperature and entrainment ratio on system efficiency metrics, exergy, and COP were evaluated for these mixtures. Conclusions are drawn based on the two optimized zeotropic mixtures for maximum thermal efficiency. For R1233zd(E)/propane mixture with mass fractions 0.75/0.25 and butane/propane mixture with mass fractions 0.5/0.5 at generator temperature of 75 °C and entrainment ratio of 0.5, the systems achieved a heating capacity of 1607.9 kW and 2847.9 kW, a cooling capacity of 1037.5 kW and 1280.3 kW, and a net power output of 59.2 kW and 136.3 kW. Other related performance and efficiency parameters are included in the paper. These findings indicate the feasibility of utilizing these systems for trigeneration application. Additionally, a comparative analysis with a regenerative ORC highlighted the ORC-EHP hybrid system's advantages, including enhanced heating efficiency, cooling capacity, and overall COP. This research contributes valuable insights to advance the efficiency and sustainability of combined cooling, heat, and power systems, fostering the progression of innovative energy solutions. [ABSTRACT FROM AUTHOR]

• Analysis of 12 decarbonisation scenarios via the electrification of space heating. • Innovative modelling approach that combines two highly detailed models. • Building stock and upstream supply sector of whole EU are covered. • Electrification via heat pumps leads to 11% lower system costs compared to hydrogen. • Heat pumps are cost-efficient in decentralised and in district heating systems. This paper analyses different levels and means of the electrification of space and hot water heating using an explorative modelling approach. The analysis provides guidance to the ongoing discussion on favourable pathways for heating buildings and the role of secondary energy carriers such as hydrogen or synthetic fuels. In total, 12 different scenarios were modelled with decarbonisation pathways until 2050, which cover all 27 member states of the European Union. Two highly detailed optimisation models were combined to cover the building stock and the upstream energy supply sector. The analysis shows that decarbonisation pathways for space and water heating based on large shares of heat pumps have at least 11% lower system costs in 2050 than pathways with large shares of hydrogen or synthetic fuels. This translates into system cost savings of around €70 bn. Heat pumps are cost-efficient in decentralised systems and in centralised district heating systems. Hence, heat pumps should be the favoured option to achieve a cost-optimal solution for heating buildings. Accordingly, the paper makes a novel and significant contribution to understanding suitable and cost-efficient decarbonisation pathways for space and hot water heating via electrification. The results of the paper can provide robust guidance for policymakers. [ABSTRACT FROM AUTHOR]

With the increasing popularity of photovoltaic (PV) systems, both academia and industry have been paying growing attention to fault prediction and health management. Although deep learning has achieved remarkable results in this field, the high cost of labeled data acquisition has become a bottleneck for its application. While unsupervised methods can reduce this cost, they fail to effectively utilize the prior knowledge of anomalous samples. To address this issue, this paper innovatively proposes a weakly-supervised anomaly detection network based on feature map conversion and hypersphere transformation (FMC-HT). Firstly, PV electroluminescence (EL) images are input into the feature extraction layer based on feature map conversion, which can reduce redundancy, enhance information density, and achieve efficient feature extraction through feature map conversion. Subsequently, inverse squared norm loss is set to utilize the knowledge of unlabeled sample features and labeled anomalous sample features, and backpropagation is performed separately to train the hypersphere transformation network, ultimately achieving effective distinction between normal and anomalous samples. On real PV EL datasets, this model exhibits impressive performance and remains stable under different prior knowledge and anomaly rates. This study not only provides a new solution for anomaly detection in PV systems but also expands new directions for the application of deep learning in scenarios with limited labeled data. • A weakly supervised model for photovoltaic panels anomaly detection is proposed. • Information from both unlabeled and anomaly samples is comprehensively utilized. • A feature mapping transformation mechanism is employed for feature extraction. • A hypersphere transformation approach is introduced for anomaly detection. [ABSTRACT FROM AUTHOR]

• Dual heat source high efficiency Organic Rankine cycle system. • Energy management strategies for complex power source vehicle systems. • Strategy parameter optimization and tuning for road condition adaptation. • Results of hybrid and integrated system selection for different road conditions. Developing hybrid electric vehicle architectures and Organic Rankine Cycle-based waste heat recovery for heavy commercial vehicles can improve engine efficiency and reduce energy consumption. Previous studies have focused on a single type of hybrid integrated with a simple Organic Rankine Cycle system, with engine efficiency to be further improved. In order to investigate the applicability of different hybrid configurations in different road conditions, this paper establishes series, parallel and 48 V mild hybrid simulation models based on SIMULINK software, as well as a model integrated with an efficient Organic Rankine cycle system, and operates them on highway and suburban road conditions respectively. The optimal selection of hybrid and integrated configurations under different road conditions is derived: the parallel hybrid and integrated system is selected for highway conditions. For suburban road conditions, hybrid only is selected in series, and 48 V mild hybrid configuration is selected after integrating the Organic Rankine Cycle system. The modelling of the waste heat recovery system and the derived hybrid selection basis provide a reference for energy saving and emission reduction of heavy-duty vehicles. [ABSTRACT FROM AUTHOR]

• A scalable energy management strategy for multi-stack fuel cell systems is developed. • The method serves as a benchmark for energy management in fuel cell systems. • The method is applicable to various heavy-duty vehicle applications. • The strategy considers the health condition of fuel cell stacks during operation. • Designed filters enhance the time efficiency of the methodology. This paper puts forward an offline optimal energy management strategy (EMS) for a multi-stack fuel cell (FC) hybrid electric vehicle (HEV) aimed at reducing both fuel consumption and degradation of FC stacks. This method is anticipated to serve as a benchmark for evaluating the performance of online EMSs in future studies. To achieve this goal, a constrained exploration method (CEM) is proposed for power allocation among different energy/power sources. Exploration methods often suffer from time-consuming processes and the curse of dimensionality, particularly when the number of control and state variables increases. To address these issues, the proposed CEM incorporates various filters for the effective calculation of battery power, battery state of charge (SOC) range, and SOC interpolation. The proposed method also utilizes a health-conscious multi-objective cost function intended for minimizing operational costs, including hydrogen consumption and degradation. CEM is introduced as a novel scalable, health-conscious and time-efficient EMS benchmark for hybrid multi-stack FC systems, efficiently decreasing operational expenses and notably shortening simulation duration. The effectiveness of the proposed method is validated through tests conducted on three heavy-duty vehicles as case studies. The first case involves an aircraft, the NASA X-57 Maxwell, fitted with two 50 kW stacks and a battery pack. The validation of the CEM is established by comparing it to level-set dynamic programming (DP) and sequential quadratic programming (SQP) in terms of accuracy and required calculation time. In the second case study, a tramway containing two 125 kW FC stacks is investigated. The validity of the CEM is approved by level-set DP and SQP algorithms. In the third case study, a truck is considered, equipped with four 75 kW FC stacks and a battery system. Level-set DP fails to do the power distribution in this case owing to the numerous state and control variables, whereas the proposed CEM successfully distributes the power among the power sources. Also, results from a comparison of the CEM and SQP methods demonstrate the superiority of CEM. This case study highlights the potential for scalability of the proposed methodology compared to the conventional DP approach found in the literature. [ABSTRACT FROM AUTHOR]

• Novel State-of-the-Art, standardized framework proposed for MGT techno economic assessment. • Comprehensive technical and economical modelling approach illustrated. • Universally adoptable off-design TEA method proposed and validated using MGTs part-load operations. • Ranking of MGTs based on TEA performance using MCDM-TOPSIS method. • Innovative strategies for stakeholders in making informed investment decisions. The urgent need for sustainable energy solutions in the face of escalating climate change and global warming has propelled the transition towards net-zero emissions. Central to this transition is the evolution of energy generation from fossil fuel-based centralized grids to decentralized microgrids, with a focus on renewable energy sources (RES) like solar PV and wind. However, the inherent intermittency of RES presents challenges that necessitate cost-effective and mature solutions. Micro gas turbines (MGTs) have emerged as promising alternatives, offering fuel flexibility, quick startup times, and low emissions. Despite their potential, the lack of a standardized techno-economic assessment (TEA) framework hampers the widespread adoption and commercial viability of MGTs. This paper addresses this gap by proposing a generalized TEA framework for MGTs, tailored to current energy market dynamics and global business models. The framework integrates technical and economic concepts, facilitating ease of programming and logical progression. Novel methodologies, including part-load fuel flow estimation and MCDM-TOPSIS ranking techniques, enhance the accuracy and applicability of MGT assessments. A case study involving 65 kW, 100 kW, and 200 kW MGT units demonstrates the framework's effectiveness in evaluating MGT performance and profitability. By providing insights into both technical feasibility and economic viability, the proposed framework serves as a valuable tool for decision-making in energy sector investments. Moreover, it fosters standardized assessments, enabling comparative analysis and informed decision-making amidst evolving energy landscapes. Ultimately, the development of a comprehensive TEA framework for MGTs represents a significant step towards achieving sustainable energy solutions and advancing towards a net-zero future. [ABSTRACT FROM AUTHOR]

• Novel study enhances distribution network reliability via multi-investment. • Optimal storage deployment and cost minimization are important objectives. • Framework combines distributed generation remuneration, emissions, and uncertainty. • Impact of multi vs. single-period investment and risk levels studied in detail. • Economic analysis includes Net Present Value, Payback, and Internal Rate of Return. Changes are currently unfolding within electric distribution networks as renewable energy integration grows. This paper introduces a novel stochastic approach for expansion planning in distribution networks with high renewable energy penetration. The model addresses uncertainties, daily and seasonal impacts, distributed generation remuneration, carbon emissions, and optimal energy storage deployment. Risk aversion techniques such as value-at-risk and conditional value-at-risk are employed to prepare networks for impactful events. Emphasizing multi-period investment over single-period, the analysis considers confidence levels and risk aversion's effects on reliability indexes, energy storage placement, generation patterns, network topology, and costs. Using a realistic 180-bus distribution network in Portugal spanning 30 years, the method's applicability and robustness are demonstrated. Results illustrate the benefits of multi-period investment, with cost increases of 6.09 % and 2.97 % for single- and multi-period investments at a 90 % confidence level, respectively, when both are compared to a situation with no risk aversion. Higher confidence levels amplify the trade-off between extreme scenario cost reduction and total cost increase, with a 99 % confidence level reducing scenario costs by 19.01 % while increasing total costs by 5.64 %. [ABSTRACT FROM AUTHOR]

• Novel energy management for battery & fuel cell bus charging stations is proposed. • Comparative grid vs. stand-alone electric bus charging analysis is demonstrated. • Achieving optimal energy management with reliable scheduling for buses charging. • Novel priority selection for bus charging based on trip energy consumption. • Grid-connected charging station gives lower energy cost than standalone. The global shift towards sustainable transportation has led to a surge in the adoption of electric and fuel cell vehicles. This paper introduces a novel energy management approach for the charging stations that serve both battery electric buses and fuel cell buses. Addressing the challenge of guaranteeing enough energy for all buses in the system, a novel priority charging approach is adopted. This approach involves a sophisticated control system that predicts which buses will receive the required energy to complete their designated routes. Minimizing the cost of the charging energy while ensuring adequate charging for buses across their entire routes without energy interruption is pursued. Six scenarios have been studied and compared, comprising a standalone and grid-connected photovoltaic-based charging station, considering all buses are either battery electric buses, fuel cell buses or a hybrid combination of both. The obtained results show that the grid-connected with battery electric buses gives the lowest energy cost of $0.0162/kWh. Furthermore, the study highlights the potential of grid-connected charging station accommodating both electric and fuel cell buses striking a balance between cost-effectiveness and fleet diversity. [ABSTRACT FROM AUTHOR]

• Patents prospection of deoxygenation routes for renewable hydrocarbons synthesis is addressed; • Simulation to the process of UOP/ENI technology for different feeds is presented; • Feeding with 75/25 (wt.%, soybean oil/animal tallow) returned high selectivity for kerosene; • Accordingly, on-spec kerosene and diesel were obtained (395.30 and 274.45 kg h−1, respectively); • Process did not produce enough C 3 H 8 to make the H 2 demand independent of external supply. Motivated by the scarcity of research compiling data from technological patents and simulations covering the integral process of renewable hydrocarbon production (reaction, separation, and purification sections), this paper presents a comprehensive technological prospection to subsequent process simulation for the hydrotreatment of fatty materials. Firstly, patents were selected from the main processes of synthesizing renewable hydrocarbons through deoxygenation reactions. Based on the data availability, technology from the company Universal Oil Products was selected. Using the UniSim Design R491 software, it was simulated the impact of the feedstock composition on the distribution and properties of hydrocarbons as well as hydrogen consumption encompassing fatty materials usually considered in biofuel productions (soybean oil and animal tallow). Data indicated that 75 wt% of soybean oil and 25 wt% of animal tallow return higher selectivity for kerosene production with lower hydrogen consumption. The reaction model selected for the cracking and isomerization reactor impacted this result, given the differences in conversion and selectivity between alkanes of different chain sizes. Contextually, on-spec kerosene and diesel were obtained. Considering a feed of 1,000 kg h−1 of fatty mixture, 274.45 kg h−1 of diesel and 395.30 kg h−1 of kerosene were obtained. The hydrogen consumption was also estimated: 50.36 kg h−1 for the simulation with pure soybean oil and 50.04 kg h−1 for the animal-tallow blend. For both situations, the process did not produce enough propane to sustain hydrogen demand to make the facility independent of external supply. [ABSTRACT FROM AUTHOR]

• A novel Stirling engine-based combined heat and power system model is developed. • Polytropic endothermic/exothermic processes are considered in Stirling submodels. • Constructal theory is employed to optimize the key components within the system. • A new comprehensive performance evaluation index for the system is constructed. • Effects of different types of regenerators on system performance are analyzed. Renewable energy-driven micro combined heat and power systems offer a clean and efficient solution for energy supply in residential and commercial buildings. This paper presents a novel model for a biogas-fired Stirling engine-based micro combined heat and power system, and establishes the mathematical relationships between geometric and operational parameters of components and the system's thermal-electric performance. To improve the accuracy of system performance prediction, the model meticulously considers the intricate heat-to-work conversion and thermal energy transfer processes, incorporating the finite heat transfer capacity and polytropic endothermic/exothermic processes in the heat exchangers of the prime mover. The reliability of the system model is verified, and constructal theory is introduced to optimize the critical geometric variables of thermal energy transfer components within the system based on the new model. The results demonstrate that compared to the performance before optimization, the electrical power output of the system increases by 142.8 %, while heat output efficiency and overall efficiency increase by 20.5 % and 10.5 %, respectively. This work provides a new approach and perspective for the mathematical modeling, performance assessment, and optimization design of Stirling engine-based micro combined heat and power systems. [ABSTRACT FROM AUTHOR]

• Bidirectional scale effects of energy conversion in ultra-low-head PHES units are evaluated. • A comprehensive understanding of the technical and economic aspects is presented. • Pump mode is key to restricting the limit size for safe and stable bidirectional operation. • Controlling partial flow separation is the most fundamental way to improve technicality. • Optimal generalized economic scheme is recommended for ultra-low-head PHES units. Ultra-low-head pumped hydro energy storage (PHES) is an attractive solution to the intermittency of sustainable energy in lowland countries and regions. For the development of large-scale ultra-low-head PHES units, tubular pump-turbine is the core equipment, but a comprehensive understanding of the technical and economic aspects of its scale effects of energy conversion is still lacking. In this paper, taking the East Route of the South-to-North Water Diversion Project in China as a prospective case, a comprehensive technical assessment of scale effects of energy conversion in ultra-low-head PHES units is conducted, and then a generalized economic analysis considering both benefits and stability is performed. The following valuable results are obtained: (1) For energy performance, special scale effects are observed in the head, power and hydraulic efficiency in both pump and turbine modes, suggesting more attention should be paid to the energy conversion similarity failure induced by the variations of geometric scale. (2) For hydraulic stability, the rotor force moment and pressure fluctuations increase with the geometric scale in both pump and turbine modes, while the pump mode plays a crucial role in restricting the limit size for safe bidirectional operation. (3) For internal flows, partial flow separation is observed in the guide-vane region in pump mode and in the draft tube in turbine mode, respectively. The fundamental path to improving project technicality lies in controlling the energetic vortices. (4) For generalized economy, the adverse effects caused by the deterioration of stability with the increase of geometric scale must be considered, and the 7 ∼ 9 m runner size is recommended for ultra-low-head PHES units from the perspective of optimal generalized revenue. These findings can provide a comprehensive guidance for the selection, optimization and development of ultra-low-head PHES units with different capacities, and can help the decision-makers effectively avoid technical and economic problems caused by the scale effects during the project design phase. [ABSTRACT FROM AUTHOR]

• Novel CO 2 capture technology using three-phase gas-solid-liquid fluidization. • Process intensification of CO 2 capture through mass transfer enhancement. • Reduced capital cost for absorption unit by 40 % compared to packed bed systems. • Lower capital cost of CO 2 capture unit by 15 % compared to traditional technologies. • Reduced power production cost in a 1000 MW plant with integrated CO 2 capture. This study addresses the pressing need to mitigate anthropogenic carbon dioxide emissions from energy-intensive industrial sectors to achieve climate neutrality goals. The focus of this paper is on evaluating the industrial feasibility of a novel three-phase gas-solid-liquid fluidized bed absorption column using mono-ethanol-amine for carbon capture and comparing it with conventional packed-bed technology. The findings show that the fluidized bed configuration reduces capital costs of the absorber by 40 % and of the carbon capture unit by 15 % compared to the packed-bed technology, leading to decreased electricity costs when used as additional unit for a 1000 MW power plant. The proposed mathematical model incorporates key factors such as hydrodynamics, mass transfer, and chemical reactions, accurately describing the intensified carbon dioxide absorption process. This intensified system enhances the transferred carbon dioxide flowrate between phases, increasing overall decarbonization capacity while reducing capital and operational costs, offering a promising solution for industrial carbon capture. [ABSTRACT FROM AUTHOR]