A novel free piston expander-linear generator (FPE-LG) integrated unit was proposed to recover waste heat efficiently from vehicle engine. This integrated unit can be used in a small-scale Organic Rankine Cycle (ORC) system and can directly convert the thermodynamic energy of working fluid into electric energy. The conceptual design of the free piston expander (FPE) was introduced and discussed. A cam plate and the corresponding valve train were used to control the inlet and outlet valve timing of the FPE. The working principle of the FPE-LG was proven to be feasible using an air test rig. The indicated efficiency of the FPE was obtained from the p⁻V indicator diagram. The dynamic characteristics of the in-cylinder flow field during the intake and exhaust processes of the FPE were analyzed based on Fluent software and 3D numerical simulation models using a computation fluid dynamics method. Results show that the indicated efficiency of the FPE can reach 66.2% and the maximal electric p... [more]

Recently, the use of hybrid systems using multiple heat sources in buildings to ensure a stable energy supply and improve the system performance has gained attention. Among them, a heat pump system using both solar and ground heat was developed and various system configurations have been introduced. However, establishing a suitable design method for the solar-assisted ground heat pump (SAGHP) system including a thermal storage tank is complicated and there are few quantitative studies on the detailed system configurations. Therefore, this study developed three SAGHP system design methods considering the design factors focused on the thermal storage tank. Using dynamic energy simulation code (TRNSYS 17), individual performance analysis models were developed and long-term quantitative analysis was carried out to suggest optimum design and operation methods. As a result, it was found that SYSTEM 2 which is a hybrid system with heat storage tank for only a solar system showed the highest a... [more]

The thermal behavior of Li-ion cells is an important safety issue and has to be known under varying thermal conditions. The main objective of this work is to gain a better understanding of the temperature increase within the cell considering different heat sources under specified working conditions. With respect to the governing physical parameters, the major aim is to find out under which thermal conditions a so called Thermal Runaway occurs. Therefore, a mathematical electrochemical-thermal model based on the Newman model has been extended with a simple combustion model from reaction kinetics including various types of heat sources assumed to be based on an Arrhenius law. This model was realized in COMSOL Multiphysics modeling software. First simulations were performed for a cylindrical 18650 cell with a L i C o O 2 -cathode to calculate the temperature increase under two simple electric load profiles and to compute critical system parameters. It has been found that the crit... [more]

A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from th... [more]

Gas injection has already proven to be an efficient shale oil recovery method successfully tested all around the world. However, gas-enhanced oil recovery methods have never been implemented or tested for the greatest Siberian shale oil formation yet. This article proposes numerical simulation of a hydrocarbon gas injection process into a horizontal well with multiple hydraulic fractures perforating Bazhenov shale oil formation in Western Siberia characterized by ultra-low permeability. A complex field-scale numerical study of gas injection for such a formation has never been performed before and is presented for the first time in our work. The hydrodynamic compositional simulation was performed utilizing a commercial simulator. A sensitivity study for different operating parameters including cycle times, bottom-hole pressures for the production and injection period, and injected gas composition was performed after the model was history matched with the available production data. Some... [more]

Fault location on overhead power lines achieved with the highest possible accuracy can reduce the time to locate faults. This contributes to ensuring the stability of power systems, as well as the reliability of power supply to consumers. There are a number of known mathematical techniques based on different physical principles that are used in fault location on overhead power lines and whose errors vary. Fault location on overhead power lines uses techniques based on the estimation of emergency state parameters, which are referred to as distance-to-fault techniques and are widely used. They are employed in digital protection relay terminals and power-line fault locators. Factors that have a significant impact on the error of fault location on overhead power lines by emergency state parameters are design, manufacturing, and operation. The aim of this article is to analyze the existing techniques and to present a new analytical technique for estimating errors of fault location on overhe... [more]

Deep saline permeable sandstones have the potential to serve as sites for CO2 storage. However, unstable CO2 storage in pores can be costly and harmful to the environment. In this study, we used lattice Boltzmann (LB) simulations to investigate the factors that affect steady-state CO2−brine imbibition flow in sandstone pores, with a focus on improving CO2 storage efficiency in deep saline permeable sandstone aquifers. We extracted three representative element volumes (REVs) from a digital rock image of a sandstone core and selected a tighter REV in the upper subdomain so that its permeability would apparently be lower than that of the other two based on single-phase LB simulation for further analysis. The results of our steady-state LB simulations of CO2−brine imbibition processes in the tighter REV under four differential pressures showed that a threshold pressure gradient of around 0.5 MPa/m exists at a differential pressure of 200 Pa, and that higher differential pressures result in... [more]

Local over-temperature is one of the main reasons for boiler tube failures (BTF). By accurately monitoring and controlling tube wall temperature, local over-temperature can be avoided. Based on the measured flue gas parameters and numerical simulation, a method of thermal deviation calculation is proposed in this study for the on-line calculation of the tube wall temperature of boiler superheaters. The full-size three-dimensional numerical simulation was presented on the combustion in a pulverized coal-fired boiler of 1000 MW ultra-supercritical (USC) unit. A difference in the thermal deviation of the vertical direction was innovatively introduced into a segmented discrete model, and the thermal deviation condition conforming to reality was introduced into the calculation. An on-line calculation system developed based on the current calculation method was applied in a 1000 MW USC unit. The calculated local high-temperature zone was consistent with the actual over-temperature position a... [more]

Flame interaction with obstacles can affect significantly its behavior due to flame front wrinkling, changes in the flame front surface area, and momentum and heat losses. Experimental and theoretical studies in this area are primarily connected with flame acceleration and deflagration to detonation transition. This work is devoted to studying laminar flames propagating in narrow gaps between closely spaced parallel plates (Hele−Shaw cell) in the presence of internal obstacles separating the rectangular channel in two parts (closed and open to the atmosphere) connected by a small hole. The focus of the research is on the penetration of flames through the hole to the adjacent channel part. Experiments are performed for fuel-rich propane−air mixtures; combustion is initiated by spark ignition near the far end of the closed volume. Additionally, numerical simulations are carried out to demonstrate the details of flame behavior prior to and after penetration into the adjacent space. The re... [more]

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today’s environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energ... [more]

The need to evolve cleaner, decentralized, and digitalized energy distribution systems and services includes the electrification of means of transport as Electric Vehicles (EVs) achieve a greater market share. In this context, this work presents and applies, through a case study, the proposal of a new methodology for calculating the hosting capacity of EVs in low- and medium-voltage distribution systems. The proposal of a new methodology that combines deterministic and stochastic methods, while considering several operational criteria, as well as being applicable in both low and medium voltage, shows itself as a more germane and innovate approach. The results obtained demonstrated that the hosting capacity of EVs for the transformers pertinent to the distribution system under study is 100% for more than 50% of the simulations performed. The conductor overload criterion is the main limiting factor, representing 36.69% of violations for the 3.6 kW charger and 52.14% for the 7 kW charger.... [more]

In recent years, the integration of battery energy storage systems (BESSs) with multilevel modular converters (MMCs) has received interest in power system applications. In this work, this configuration is called a MMC-based BESS. The batteries are connected directly to the MMCs on submodules (SMs), called the single-stage approach. Several control strategies have been proposed to guarantee the proper operation of a MMC-based BESS. This system is complex due to the control strategy. Another challenge is in obtaining the controller gains for a MMC-based BESS converter. In this sense, there is a gap in the methodology used to calculate the controller gains. Thus, this work aimed to tune the analytical expressions of a MMC-based BESS by considering the single-stage approach. The methodology is validated through detailed simulation models of 10.9 MVA/5.76 MWh connected to a 13.8 kV power system. Finally, to validate the dynamics of the controllers, the simulation results in the PLECS softwa... [more]

Heat dissipation in high-heat flux micro-devices has become a pressing issue. One of the most effective methods for removing the high heat load of micro-devices is boiling heat transfer in microchannels. A novel approach to flow pattern and heat transfer recognition in microchannels is provided by the combination of image and machine learning techniques. The support vector machine method in texture characteristics successfully recognizes flow patterns. To determine the bubble dynamics behavior and flow pattern in the micro-device, image features are combined with machine learning algorithms and applied in the recognition of boiling flow patterns. As a result, the relationship between flow pattern evolution and boiling heat transfer is established, and the mechanism of boiling heat transfer is revealed.

Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly based on PV time series has some advantages over solar irradiance forecasting first and PV power modeling afterwards. In this paper, the power forecasting for BIPV systems in a vertical façade is studied using machine learning algorithms based on decision trees. The forecasting scheme employs the skforecast library from the Python environment, which facilitates the implementation of different schemes for both deterministic and probabilistic forecasting applications. Firstly, deterministic forecasting of hourly BIPV power was performed with XGBoost and Random Forest algorithms for different cases, showing an improvement in forecasting accuracy when some exogenous variables were used. Secondl... [more]

The main goal of this paper is to research and analyze the problem of image reconstruction performance using machine learning methods in 3D electrical capacitance tomography (ECT) and electrical impedance tomography (EIT) by comparing the areas inside the tank to determine the finite elements for which one of the method reconstructions is more effective. The research was conducted on 5000 simulated cases, which ranged from one to five inclusions generated for a cylindrical tank. The authors first used the elastic net learning method to perform the reconstruction and then proposed a method for testing the effectiveness of reconstruction. Based on this approach, the reconstructions obtained by each method were compared, and the areas within the object were identified. Finally, the results obtained from the simulation tests were verified on real measurements made with two types of tomographs. It was found that areas closer to the edge of the tank were more effectively reconstructed by EIT... [more]

Heated glass can be applied to improve windows’ condensation resistance and indoor thermal comfort in buildings. Although this applied technology has advantages, there are still some concerns in practical applications, such as additional energy consumption and control issues. This study evaluates the effectiveness of a heated window heating (HWH) system in terms of thermal comfort and heating energy performance (HEP). The simulation-based analysis is performed to evaluate the effectiveness of the HWH using a residential building model and to compare it with radiant floor heating (RFH) and hybrid heating (HH) systems (i.e., combined HWH and RFH). This study also investigates the peak and cumulative heating loads using HWH systems with various scenarios of control methods and setpoint temperature. The predicted mean vote (PMV) is used as an indoor thermal comfort index. The ratio of cumulative thermal comfort time to the entire heating period is calculated. The results show that HWH and... [more]

The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions of energy demand are crucial for ensuring the reliability, stability, and efficiency of SGs. LF techniques aid SGs in making decisions related to power operation and planning upgrades, and can help provide efficient and reliable power services at fair prices. Advances in artificial intelligence (AI), specifically in machine learning (ML) and deep learning (DL), have also played a significant role in improving the precision of demand forecasting. It is important to evaluate different LF techniques to identify the most accurate and appropriate one for use in SGs. This paper conducts a systematic review of state-of-the-art forecasting techniques, including traditional techniques, clustering-based techniques, AI-based techniques, and time series-based techniques, and provides an analysis of their performance and results. The aim of this paper is to determine which LF tech... [more]

The air-blown interrupting gap protection method is a self-energy interrupting method based on the concept of suppressing arc building by frequency continuation. In order to verify the effect of an air-blown interrupting gap to reduce the rate of line lightning tripping, in this paper, the protection principle of the air-blown interrupter gap is first described, and the action process simulated by COMSOL Multiphysics simulation software. Next, a frequency renewal test circuit is built for the test. Then, an arc-building rate calculation model and a lightning trip rate calculation model under the condition of an air-blown interrupting gap are established, and, finally, a 10 kV overhead line in Yunnan is selected for the verification of the calculation example. The results show the following: a gas blowing arc gap can be effectively extinguished in about 2.5 ms frequency arc and with no re-ignition phenomenon. Before and after the installation of the gas blowing arc gap line, the arc rat... [more]

This paper is an investigation into the high inrush current effect that emerges during the energizing of drivers of different circuit types loaded by light-emitting-diode (LED) strips. Two different driver circuit types were analyzed—a voltage stabilization (VS) circuit utilizing a Zener diode, and a current stabilization (CS) circuit type in the form of an integrated circuit (IC). Inrush current waveforms were calculated for drivers loaded by various numbers of LEDs. In the frame of the study, analysis executed for drivers energized by diverse input voltage RMS values at different switching-on phases was performed. The analysis is comprised of experimental verification of LT SPICE simulation results of current waveforms and calculations of current peak values appearing while switching on the supply voltage. The developed models for simulation needs were elaborated concerning precise reflection of the real current and voltage waveforms obtained during oscilloscopic registration of the... [more]

Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algo... [more]

Nuclear plant modeling and control is an important subject in nuclear power engineering, giving the dynamic model from process mechanics and/or operational data as well as guaranteeing satisfactory transient and steady-state operational performance by well-designed plant control laws. With the fast development of small modular reactors (SMRs) and in the context of massive integration of intermittent renewables, it is required to operate the nuclear plants more reliably, efficiently, flexibly and smartly, motivating the recent exciting progress in nuclear plant modeling and control. In this paper, the main progress during the last several years in dynamical modeling and control of nuclear plants is reviewed. The requirement of nuclear plant operation to the subject of modeling and control is first given. By categorizing the results to the aspects of mechanism-based, data-based and hybrid modeling methods, the advances in dynamical modeling are then given, where the modeling of SMR plant... [more]

An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dat... [more]

An energy supply and demand forecasting system can help decision-makers grasp more comprehensive information, make accurate decisions and even plan a carbon-neutral future when adjusting energy structure, developing alternative energy resources and so on. This paper presents a hierarchical design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model called GM(1,1) which covers all the process of data collection, data analysis and data prediction. It mainly consists of three services, namely Crawler Service (CS), Algorithm Service (AS), Data Service (DS). The architecture of multiple loose coupling services makes the system flexible in more data, and more advanced prediction algorithms for future energy forecasting works. In order to make higher prediction accuracy based on GM(1,1), this paper illustrates some basic enhanced methods and their combinations with adaptable variable weights. An implementation for testing the system was applied, wher... [more]

As the proportions of variable renewable sources (VRSs) such as solar and wind energy increase rapidly in the power system, their uncertainties inevitably undermine power supply reliability and increase the amount of operating reserve resources required to manage the system. However, because operating reserves have the characteristics of a public good and their value is related to the social cost of blackouts, it is difficult to determine their market price efficiently, which leads to inefficiencies in procuring operating reserves. This study estimates the operating reserve demand curve (ORDC) of the Korean power system to provide an effective basis for measuring the proper value and quantity of operating reserves needed to meet the reliability standard. A stochastic dynamic optimization model is applied to incorporate the probabilistic characteristics of VRS and the inter-hour constraint, which is necessary for analyzing load-following reserves. An econometric model and the Monte Carl... [more]

Ensuring a sustainable supply for humankind with mineral raw materials and preventing fuel and energy crises, minimizing human-made accidents and the negative impact of industry on the environment, the inflow of funds and innovations into the mining sector should be expanding in time and space. To do this, new mining platforms should have not only innovative and technological, but also social-and-economic coverage of the latest competencies, which Mining 4.0 fully corresponds to. The achievements of the Fourth Industrial Revolution, embodied in “end-to-end” digital and convergent technologies, are able to ensure the stable development of the mineral resource sector in the face of fluctuations in raw material demand and the profitability of mining enterprises, strengthening environmental safety legislation. Mining 4.0 is also a response to the technological shocks associated with the accelerated digital modernization of the manufacturing and infrastructure industries. This article attem... [more]