Hydrogen (H2) is an attractive energy carrier to move, store, and deliver energy in a form that can be easily used. Field proven technology for underground hydrogen storage (UHS) is essential for a successful hydrogen economy. Options for this are manmade caverns, salt domes/caverns, saline aquifers, and depleted oil/gas fields, where large quantities of gaseous hydrogen have been stored in caverns for many years. The key requirements intrinsic of a porous rock formation for seasonal storage of hydrogen are: adequate capacity, ability to contain H2, capability to inject/extract high volumes of H2, and a reliable caprock to prevent leakage. We have carefully evaluated a commercial non-isothermal compositional gas reservoir simulator and its suitability for hydrogen storage and withdrawal from saline aquifers and depleted oil/gas reservoirs. We have successfully calibrated the gas equation of state model against published laboratory H2 density and viscosity data as a function of pressure... [more]

In current power station boilers, fuel burns at a low temperature, which results in low exergy efficiency. This research combined the second law of t with the boiler structure to maximize the efficiency of a 350 MW power plant boiler. A three-dimensional simulation of the combustion process at the power plant boiler is performed. A one-dimensional simulation model of the boiler is then constructed to calculate the combustion exergy loss, heat transfer exergy loss, and boiler exergy efficiency. Under the principle of high-temperature air combustion technologies, this paper also proposes a new structure and improved operating parameters to improve the exergy efficiency of boilers by reducing the heat exchange area of the economizer and increasing the heat exchange area of the air preheater. Simulation results show that the exergy efficiency of the boiler increased from 47.29% to 48.35% through the modified model. The simulation outcomes can instruct future optimal boiler design and contr... [more]

Induction motors have gained a renewed interest due to this new shift from conventional power sources to electric power. These motors are known for their high commencing torque, adequate speed control and reasonable overload capacity. However, induction motors have an innate thermal issue wherein their lifespan and performance are strongly temperature dependent. Hence, it is highly essential to focus on the thermal management aspect of these motors to ensure reliability and enhance performance. Thus, the major purpose of the paper is to comprehensively review various approaches and methods for thermal analysis, including finite element analysis, lumped parameter thermal network and computational fluid dynamics tools. Moreover, it also presents various cooling strategies commonly adopted in induction motors. Furthermore, this study also suggests an integrated approach with two or more cooling strategies to be the need of the hour. These will combine the benefits of the individual system... [more]

The ammonia oxidation reaction on solid platinum−rhodium gauze is a critical step in nitric acid production. As the global demand for food and fertilisers keeps steadily growing, this remains an essential reaction in the chemical industry. However, harsh conditions inside ammonia burners lead to the degradation of catalytic meshes, severely hindering this process. This manuscript is focused on two issues. The first is the influence of catalyst gauze geometry and process parameters on the efficiency of ammonia oxidation on platinum−rhodium gauze. The second investigated problem is the influence of geometry on catalyst fibre degradation and the movement and deposition of entrained platinum particles. Computational Fluid Dynamics was utilised in this work for calculations. Different catalyst gauze geometries were chosen to examine the relationship between wire geometry and heat and mass transfer by analysing temperature and flow fields. Significantly, the analysis of the temperature gradi... [more]

Forecasting return and profit is a primary challenge for financial practitioners and an even more critical issue when it comes to forecasting energy market returns. This research attempts to propose an effective method to predict the Brent Crude Oil return, which results in remarkable performance compared with the well-known models in the return prediction. The proposed hybrid model is based on long short-term memory (LSTM) and convolutional neural network (CNN) networks where the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) outputs are used as features, along with return lags, price, and macroeconomic variables to train the models, resulting in significant improvement in the model’s performance. According to the obtained results, our proposed model performs better than other models, including artificial neural network (ANN), principal component analysis (PCA)-ANN, LSTM, and CNN. We show the efficiency of our pro... [more]

The continuous technical improvements involving electric motors, battery packs, and general powertrain equipment make it strictly necessary to predict or evaluate the energy consumption of electric vehicles (EVs) with reasonable accuracy. The significant improvements in computing power in the last decades have allowed the implementation of various simulation scenarios and the development of strategies for vehicle modelling, thus estimating energy consumption with higher accuracy. This paper gives a general overview of the strategies adopted to model EVs for evaluating or predicting energy consumption. The need to develop such solutions is due to the basis of each analysis, as well as the type of results that must be produced and delivered. This last point strongly influences the whole set-up process of the analysis, from the available and collected dataset to the choice of the algorithm itself.

In this study, a newly developed zero-dimensional electrochemical model was used for modeling and controlling proton-exchange membrane fuel cell (PEMFC) performance. Calibration of the model was performed with measurements from the fuel cell stack. Subsequently, a compressor and a humidifier on the cathode side were sized and added to the existing model. The aim of this work was to model the PEMFC stack and balance of plant (BoP) components in detail to show the influence of operating parameters such as cathode pressure, stack temperature and cathode stoichiometric ratio on the performance and efficiency of the overall system compared to the original model using a newly developed real-time model. The model managed to predict the profile of essential parameters, such as temperature, pressure, power, voltage, etc. The most important conclusions from this particular case are: the cell power output is only slightly changed with the variations in stoichiometric ratio of the cathode side and... [more]

With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic behaviour of wind, accurate forecasting of wind power for different intervals of time becomes more challenging. Forecasting of wind power generation over different time spans is essential for different applications of wind energy. Recent development in this research field displays a wide spectrum of wind power prediction methods covering different prediction horizons. A detailed review of recent research achievements, performance, and information about possible future scope is presented in this article. This paper systematically reviews long term, short term and ultra short term wind power prediction methods. Each category of forecasting methods is further classified into four subclasses an... [more]

Chemical looping combustion (CLC) is one of the most advanced technologies allowing for the reduction in CO2 emissions during the combustion of solid fuels. The modified method combines chemical looping with oxygen uncoupling (CLOU) and in situ gasification chemical looping combustion (iG-CLC). As a result, an innovative hybrid chemical looping combustion came into existence, making the above two technologies complementary. Since the complexity of the CLC is still not sufficiently recognized, the study of this process is of a practical significance. The paper describes the experiences in the modelling of complex geometry CLC equipment. The experimental facility consists of two reactors: an air reactor and a fuel reactor. The paper introduces the fuzzy logic (FL) method as an artificial intelligence (AI) approach for the prediction of SO2 and NOx (i.e., NO + NO2) emissions from coal and biomass combustion carried out in air-firing; oxyfuel; iG-CLC; and CLOU conditions. The developed mod... [more]

A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in P... [more]

Integrating new renewable energy resources requires robust and reliable forecasts to ensure a stable electrical grid and avoid blackouts. Sophisticated representation learning techniques, such as autoencoders, play an essential role, as they allow for the extraction of latent features to forecast the expected generated wind and photovoltaic power for the next seconds up to days. Thereby, autoencoders reduce the required training time and the time spent in manual feature engineering and often improve the forecast error. However, most current renewable energy forecasting research on autoencoders focuses on smaller forecast horizons for the following seconds and hours based on meteorological measurements. At the same time, larger forecast horizons, such as day-ahead power forecasts based on numerical weather predictions, are crucial for planning loads and demands within the electrical grid to prevent power failures. There is little evidence on the ability of autoencoders and their respect... [more]

Accurate price forecasts on the EU ETS market are of interest to many production and investment entities. This paper describes the day-ahead carbon price prediction based on a wide range of fuel and energy indicators traded on the Intercontinental Exchange market. The indicators are analyzed in seven groups for individual products (power, natural gas, coal, crude, heating oil, unleaded gasoline, gasoil). In the proposed approach, by combining the Principal Component Analysis (PCA) method and various methods of supervised machine learning, the possibilities of prediction in the period of rapid price increases are shown. The PCA method made it possible to reduce the number of variables from 37 to 4, which were inputs for predictive models. In the paper, these models are compared: regression trees, ensembles of regression trees, Gaussian Process Regression (GPR) models, Support Vector Machines (SVM) models and Neural Network Regression (NNR) models. The research showed that the Gaussian P... [more]

Solar air heating devices have been employed in a wide range of industrial and home applications for solar energy conversion and recovery. It is a useful technique for increasing the rate of heat transfer by artificially creating repetitive roughness on the absorbing surface in the form of semicircular ribs. A thermo-hydraulic performance analysis for a fully developed turbulent flow through rib-roughened solar air heater (SAH) is presented in this article by employing computational fluid dynamics. Both 2-dimensional geometrical modeling and numerical solutions were performed in the finite volume package ANSYS FLUENT. The renormalization-group (RNG) k-ε turbulence model was used, as it is suitable for low Reynolds number (Re) turbulent flows. A thermo-hydraulic performance analysis of an SAH was carried out for a ranging Re, 3800−18,000 (6 sets); relative roughness pitch (RRP), 5−25 (12 sets); relative roughness height (RRH), 0.03−0.06 (3 sets); and heat flux, 1000 W/m2. The numerical... [more]

The Francis hydro-turbine is a typical nonlinear system with coupled hydraulic, mechanical, and electrical subsystems. It is difficult to understand the reasons for its operational failures, since the main cause of failures is due to the complex interaction of the three subsystems. This paper presents an improved dynamic model of the Francis hydro-turbine. This study involves the development of a nonlinear dynamic model of a hydraulic unit, given start-up and emergency processes, and the consideration of the effect of water hammer during transients. To accomplish the objectives set, existing models used to model hydroelectric units are analyzed and a mathematical model is proposed, which takes into account the dynamics during abrupt changes in the conditions. Based on these mathematical models, a computer model was developed, and numerical simulation was carried out with an assessment of the results obtained. The mathematical model built was verified on an experimental model. As a resu... [more]

By operating over a large bandwidth, the terahertz (THz) frequency band (0.3−3 THz) promises to deliver extremely high data rates. While the use of this band in cellular communications systems is not expected to happen within the next decade, various other use-cases such as wireless backhauling and point-to-point wireless access are on the immediate horizon. In this study, we develop an analytical propagation model for the case of ground-to-aircraft communications by explicitly accounting for THz-specific propagation phenomena including path loss, attenuation by different types of clouds, and atmospheric absorption at different altitudes. To this aim, we first exhaustively characterize the geometric, molecular, and structural properties of clouds for different weather conditions and Earth regions. Then, by applying the tools of stochastic geometry, we present the closed-form expression for received power at the aircraft. Our numerical results show that the type of weather forming diffe... [more]

Enhancing the accuracy of the battery state of charge (SOC) estimation is essential in developing more effective, dependable, and convenient electric vehicles. In this paper, a hybrid CNN and gated recurrent unit-long short-term memory (CNN-GRU-LSTM) approach, which is a recurrent neural network (RNN) based model with an explainable artificial intelligence (EAI) was used for the battery SOC estimation, where the cell parameters were explicitly synchronized to the SOC. The complexed link between the monitoring signals related to current, voltage, and temperature, and the battery SOC, was established using the deep learning (DL) technique. A LG 18650HG2 li-ion battery dataset was used for training the model so that the battery was subjected to a dynamic process. Moreover, the data recorded at ambient temperatures of −10 °C, 0 °C, 10 °C, and 25 °C are fed into the method during training. The trained model was subsequently used to estimate the SOC instantaneously on the testing datasets. A... [more]

This article presents selected methods of limiting the bearing voltages of synchronous machines with permanent magnets supplied from power electronic converters. The authors analyzed methods based on the use of various shielding windings placed in slot wedges and mounted in the stator end-winding region. The values of the parasitic capacitances of the machine, on which the levels of bearing voltages depend, were determined using the finite element method. Three-dimensional simulation models were used for the calculations. The analysis of the influence of the shielding windings on the bearing voltage waveforms was conducted on the basis of circuit models with two- and three-level converters. The obtained calculation results indicate a high potential in limiting bearing voltages.

The need for a verification method for aggregation techniques for passive electrical systems is necessary as power systems increase in complexity. Model reduction is crucial to increase the number of simulations necessary to ensure a stable and reliable design of power systems. This paper presents a novel modal domain-based technique to identify the best aggregation technique for a given system and to indicate the validity of the aggregation. This is done by benchmarking different aggregation techniques and using the dominant contribution factor ratio as a validity parameter. The different aggregation techniques are compared via time-domain simulations against the full detailed model. It is found that (1) the power loss aggregation technique is the most precise when it weighs the equivalent impedances of the parallel feeders, (2) unequal current generation does not impact the aggregation accuracy, (3) individual string aggregation provides the best results for dynamic simulations, and... [more]

With the coming of the 2022 Beijing Winter Olympic Games, China’s artificial ice rink construction will be in rapid development. A parametric evaluation of the cooling pipe in a direct evaporation rink was performed by numerical simulation. The results showed that the influence of the temperature of the antifreeze pipe on the ice surface temperature can be ignored. The evaporation temperature of the working medium in the cooling pipe is between −32 °C and −22.4 °C to ensure the ice surface temperature is between −5 °C and −3 °C. With the increase in the cooling pipe spacing, the required evaporation temperature of the working medium in the cooling pipe and the uniformity of the ice surface temperature decreased. The required evaporation temperature of the working medium in the cooling pipe decreases by 1.2−1.5 °C for every 10 mm increment of spacing. With the increase in the cooling pipe diameter, the required evaporation temperature of the working medium in the cooling pipe and the un... [more]

Scarcity has made fresh water too economically and socially too valuable to be used by the processing industry without restriction. Wet evaporative cooling cycles offer competitive advantages in terms of CoP compared to other cooling cycles with relatively low cost but requiring extensive quantities of water. Dry cooling, on the other hand, requires large heat-transfer areas, in addition to high power requirements. In this study, a hybrid cycle is proposed for high-end cooling loads of 215 MW. The proposed cycle combines the benefits of phase change to make dry cycles competitive. Furthermore, the proposed cycle also diminishes the extensive use of various chemicals used in wet cooling cycles. The applicable dry bulb temperature range is 25−50 °C. Variations in cooling fluid cold temperature due to ambient conditions are curtailed to a maximum of 2 °C by the proposed cycle. A technoeconomic comparison of the proposed solution to wet evaporative cooling is presented, and the effects are... [more]

Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. The control systems are responsible for stopping the relevant unit safely in case of any malfunction while ensuring the desired operating point. Conventional control systems detect anomalies at certain limits or predefined threshold values by evaluating analog signals regardless of differences caused by operating conditions. In this study, using real data from a large hydro unit (>150 MW), a normal behavior model of a hydraulic governor’s oil circulation in an operational HEPP is created using several machine learning methods and historical data obtained from the HEPP’s SCADA system. Model outputs resulted in up to 96.45% success of prediction with less than 1% absolute deviation from actual measurements and an R2 score of 0.985 with the random forest regression method. This novel approach makes the model outputs far more appropriate to use as an active threshold value ch... [more]

Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

A low-performance fuel cell significantly hinders the application and commercialization of fuel cell technology. Computational fluid dynamics modeling could predict and evaluate the performance of a proton exchange membrane fuel cell (PEMFC) with less time consumption and cost-effectiveness. PEMFC performance is influenced by the distribution of reactants, water, heat, and current density. An uneven distribution of reactants leads to the localization of current density that produces heat and water, which are the by-products of the reaction to be concentrated at the location. The simplification of model geometry can affect performance prediction. Numerical investigations are commonly validated with experimental results to validate the method’s accuracy. Poor prediction of PEMFC results has not been discussed. Thus, this study aims to predict the effect of geometry modeling on fuel cell performance. Two contrasting 3D model dimensions, particularly single-channel and small-scale seven-ch... [more]

In this paper, a new carrier phase-shift (CPS) modulation method based on switching the displacement angle (SDA) is proposed to compromise the harmonic content of the output voltage and the circulating current. It can be used in medium- and low-voltage applications where the AC-side voltage and DC-side current of the modular multilevel converter (MMC) are required to have low harmonic content simultaneously. In this proposed SDA-based CPS modulation, the carrier displacement angle of the MMC with N submodules in each arm is periodically switched between the values of 0 and π/N degrees, so that the harmonic content of the output voltage and the harmonic content of the circulating current will not be in extreme conditions, which occurs when the displacement angle is set to 0 or π/N degrees. The effectiveness of this method has been verified by simulation and experimental results.

Magnetic properties of hard magnets are currently attracting a great deal of attention. In the paper, the modified Harrison model was used to describe the saturating hysteresis loops of three praseodymium−dysprosium ribbons that differed in their chemical composition and processing conditions. Microstructural studies (TEM and diffraction patterns) were performed for the ribbons under consideration. The Harrison model incorporates a number of physically tangible concepts such as the anhysteretic curve, bifurcations, and bi-stability. The modification of the original approach consisted of adding an additional degree of freedom in the modeling by freeing the restraints present in the original version, in which both coercivity and remanence are functions of temperature only.