The increased penetration of renewable energy sources (RES) and electric vehicles (EVs) is resulting in significant challenges to the stability, reliability, and resiliency of the electrical grid due to the intermittency nature of RES and uncertainty of charging demands of EVs. There is a potential for significant economic returns to use vehicle-to-grid (V2G) technology for peak load reduction and frequency control. To verify the effectiveness of the V2G-based frequency control in a microgrid, modeling and simulations of single- and multi-vehicle-based primary and secondary frequency controls were conducted to utilize the integrated components at the Canadian Centre for Housing Technology (CCHT)-V2G testing facility by using MATLAB/Simulink. A single-vehicle-based model was validated by comparing empirical testing and simulations of primary and secondary frequency controls. The validated conceptual model was then applied for dynamic phasor simulations of multi-vehicle-based frequency c... [more]

While the production costs and logistical benefits of biomass pelleting have been widely discussed in the literature, the downstream economic and environmental benefits of processing pelleted biomass have been largely neglected. To investigate those benefits, we performed a comparative techno-economic analysis and life cycle assessment of producing ethanol using loose and pelleted forms of biomass. Analyses of a 2000 metric tons (dry)/d biorefinery showed that using pelleted biomass is more economical than using loose or baled biomass. The lowest minimum ethanol selling price (MESP) for pelleted biomass was USD 0.58/gal less than the lowest MESP for loose biomass. Among all processing conditions analyzed, MESP for ethanol produced with pelleted biomass was always lower than when produced with loose biomass. Shorter pretreatment and hydrolysis times, higher pretreatment solids loadings, lower ammonia requirements, and reduced enzyme loadings were the primary factors contributing to lowe... [more]

Due to the continuous diurnal, seasonal, and annual changes in the German power supply, prospective dynamic emission factors are needed to determine greenhouse gas (GHG) emissions from hybrid and flexible electrification measures. For the calculation of average emission factors (AEF) and marginal emission factors (MEF), detailed electricity market data are required to represent electricity trading, energy storage, and the partial load behavior of the power plant park on a unit-by-unit, hourly basis. Using two normative scenarios up to 2050, different emission factors of electricity supply with regard to the degree of decarbonization of power production were developed in a linear optimization model through different GHG emission caps (Business-As-Usual, BAU: −74%; Climate-Action-Plan, CAP: −95%). The mean hourly German AEF drops to 182 gCO2eq/kWhel (2018: 468 gCO2eq/kWhel) in the BAU scenario by the year 2050 and even to 29 gCO2eq/kWhel in the CAP scenario with 3700 almost emission-free... [more]

An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (SOC) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC independence and their corresponding static SOC. With battery discharging current and multi-frequency EIS results, an ANN model is built and trained to estimate SOC. The measurement data is obtained using the potentiostats/galvanostats device, and the ANN is trained using the neural network toolbox in MATLAB. According to the experimental results, the performance of the proposed ANN model is dependent on the number of neurons in the hidden layer. The proposed method is validated with a set of random discharging processes. The high accuracy of SOC estimation is able to be achieved with the average error reduced to 1.92% when the number of neurons in the hidden layer is 35. Therefore, the proposed... [more]

In the field of air navigation, there is a constant pursuit for new navigation solutions for precise GNSS (Global Navigation Satellite System) positioning of aircraft. This study aims to present the results of research on the development of a new method for improving the performance of PPP (Precise Point Positioning) positioning in the GPS (Global Positioning System) and GLONASS (Globalnaja Nawigacionnaja Sputnikovaya Sistema) systems for air navigation. The research method is based on a linear combination of individual position solutions from the GPS and GLONASS systems. The paper shows a computational scheme based on the linear combination for geocentric XYZ coordinates of an aircraft. The algorithm of the new research method uses the weighted mean method to determine the resultant aircraft position. The research method was tested on GPS and GLONASS kinematic data from an airborne experiment carried out with a Seneca Piper PA34-200T aircraft at the Mielec airport. A dual-frequency du... [more]

The prediction of time series data applied to the energy sector (prediction of renewable energy production, forecasting prosumers’ consumption/generation, forecast of country-level consumption, etc.) has numerous useful applications. Nevertheless, the complexity and non-linear behaviour associated with such kind of energy systems hinder the development of accurate algorithms. In such a context, this paper investigates the use of a state-of-art deep learning architecture in order to perform precise load demand forecasting 24-h-ahead in the whole country of France using RTE data. To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms. WaveNet uses dilated causal convolutions and skip-connection to utilise long-term information. This kind of novel ML architecture presents different advantages regarding other statistical algorithms. On the one hand, the proposed deep learnin... [more]

The United Nations established 17 Sustainable Development Goals (SDGs), and the fulfillment of the 7th, defined as “Ensure access to affordable, reliable, sustainable, and modern energy for all”, requires energy industry transitions and digital transformations, which implies that diverse stakeholders need to move fast to allow the growth of more flexible power systems. This paper contains the case report that addresses the commercial digital transformation process developed at AES Colombia, through the implementation of a modern platform based on specialized applications that use Industry 4.0 tools. The Chivor hydropower project, a 1000-MW powerplant that covers 6% of Colombia’s demand, which is owned by AES Colombia and constitutes its primary asset, is first described. Then, a description of Colombia’s complex market (energy matrix, trading and dispatch mechanisms, and future projects) is presented. Then, the methodology followed for the digital transformation process using modern to... [more]

To investigate the combustion modes and unsteady characteristics during the condition transition of a scramjet combustor, a series of experiments were carried out under the condition of Mach 2.52 supersonic incoming flow, the corresponding stagnation pressure and temperature of which were 1.6 MPa and 1486 K, respectively. A fuel supply system that could dynamically adjust the injection pressure was adopted to simulate the condition transition stage of a scramjet. Based on the advanced combustion diagnosis technique, the transient chemiluminescence image and the wall pressure were recorded during the whole combustion process. Three typical modes of turbulent combustion occurred when the injection pressure drop gradually increased. The jet flame was stable after the condition transition when the injection pressure drop was relatively low. An unstable combustion phenomenon accompanied by intermittent local extinction and reignition could be found near the blowout limits. With a further in... [more]

There are multiples conditions that lead to partial shading conditions (PSC) in photovoltaic systems (PV). Under these conditions, the harvested energy decreases in the PV system. The maximum power point tracking (MPPT) controller aims to harvest the greatest amount of energy even under partial shading conditions. The simplest available MPPT algorithms fail on PSC, whereas the complex ones are effective but require high computational resources and experience in this type of systems. This paper presents a new MPPT algorithm that is simple but effective in tracking the global maximum power point even in PSC. The simulation and experimental results show excellent performance of the proposed algorithm. Additionally, a comparison with a previously proposed algorithm is presented. The comparison shows that the proposal in this paper is faster in tracking the maximum power point than complex algorithms.

Environmental protection, and in particular air protection against pollution, is an extremely important element of the global policy of many countries. The problem of air pollution is particularly important in Poland, where the heating market is one of the largest in Europe and is based in 74% on the use of fossil fuels, in particular hard coal. One of the technological solutions for the implementation of cleaner fuels is the co-combustion of coal and biomass. This process enables the reduction of harmful pollutants such as CO2, SO2, and can be implemented in existing boilers. Heating boilers achieve the highest design efficiency during optimal load at the level of 85−95% of nominal power. Under such conditions, heat production is most efficient. During operation, boilers are often started, extinguished or run below rated output, resulting in increased emissions. This publication presents the results of measurements of efficiency and concentrations of pollutants in the WR water boiler... [more]

Climate changes are nowadays reality and affect all aspects of everyday life. One of the places where these changes influence the society the most is the Brazilian Ceará region and Jaguaribara basin that suffer long-lasting, devastating drought cycles. They have a dramatic negative impact on local economy, forcing change in business models. This work presents the valorisation of wastes and residues from local fish, prawns, and the vegetable-cultivation industry via biogas production forced to adapt to these new circumstances. Along a single year, as much as 189.74 tonnes of wastes and residues can be processed by the biogas production facility, producing as much as 94 GJ of cooling energy and 1 tonne of biofertiliser monthly. Even for such a small biogas production facility, the NPV is positive already after 11 years; its IRR is 6.2%, and accumulated ROI for 20 years of operation is as high as 77.8%. This work demonstrates that a valorisation of industrial wastes and residues via bioga... [more]

Cellulosic biorefineries have attracted interest due to the growing energy security and environmental concerns related to fossil fuel-based energy and chemicals. Using pelleted biomass as a biorefinery feedstock can reduce their processing inputs while improving biomass handling and transportation. However, it is still questionable whether energy and emission savings from feedstock transportation and processing can justify pelletization. A life cycle assessment approach was used to compare energy consumption and greenhouse gas (GHG) emissions from pelleted and non-pelleted corn stover as a biorefinery feedstock. Operations considered were pelleting, transportation, and soaking in aqueous ammonia (SAA) pretreatment. Despite greater GHG emissions (up to 25 times higher than the transportation) generated from the pelleting process, the model showed a significant opportunity to offset and even reduce overall GHG emissions considering the pretreatment process benefits. Our process energy an... [more]

This paper reports the results of a study concerned with use of electroadhesion for investigation of the attraction force of paper (weight of 50 g/m2). For this purpose, a measuring system was developed and built. Next, a dedicated electroadhesive pad coated with copper and copper mesh on the other side was designed. A new supply control algorithm was also developed and a dedicated charger was built for the purpose of this study. The measurements were carried out with a tensiometer to measure the force with which the electroadhesive pad attracted paper. The research was carried out in a series of several experiments and involved shifting the polarity of the pad and depolarizing the entire system.

This paper deals with photovoltaic (PV) power plant modeling and its integration into the medium-voltage distribution network. Apart from solar cells, a simulation model includes a boost converter, voltage-oriented controller and LCL filter. The main emphasis is given to the comparison of two optimization methods—particle swarm optimization (PSO) and the Ziegler−Nichols (ZN) tuning method, approaches that are used for the parameters of Proportional-Integral (PI) controllers determination. A PI controller is commonly used because of its performance, but it is limited in its effectiveness if there is a change in the parameters of the system. In our case, the aforementioned change is caused by switching the feeders of the distribution network from an open-loop to a closed-loop arrangement. The simulation results have claimed the superiority of the PSO algorithm, while the classical ZN tuning method is acceptable in a limited area of operation.

Resource use in point-to-point and point-to-multipoint communication emerges with the tremendous growth in wireless communication technologies. One of the technologies is wireless power transfer which may be used to provide sufficient resources for energy-constrained networks. With the implication of cooperative communication in 5G/B5G and the Internet of Things (IoT), simultaneous wireless information and power transfer (SWIPT)-assisted energy efficiency and appropriate resource use become challenging tasks. In this paper, multiple IoT-enabled devices are deployed to cooperate with the source node through intermediate/relay nodes powered by radio-frequency (RF) energy. The relay forwards the desired information generated by the source node to the IoT devices with the fusion of decode/amplify processes and charges itself at the same time through energy harvesting technology. In this regard, a problem with throughput, energy efficiency, and joint throughput with user admission maximizat... [more]

Vegetable fibers have inspired studies in academia and industry, because of their good characteristics appropriated for many technological applications. Sisal fibers (Agave sisalana variety), when extracted from the leaf, are wet and must be dried to reduce moisture content, minimizing deterioration and degradation for long time. The control of the drying process plays an important role to guarantee maximum quality of the fibers related to mechanical strength and color. In this sense, this research aims to evaluate the drying of sisal fibers in an oven with mechanical air circulation. For this purpose, a transient and 3D mathematical model has been developed to predict moisture removal and heating of a fiber porous bed, and drying experiments were carried out at different drying conditions. The advanced model considers bed porosity, fiber and bed moisture, simultaneous heat and mass transfer, and heat transport due to conduction, convection and evaporation. Simulated drying and heating... [more]

Attaining a good level of internal comfort is possible by controlling various parameters. Among all, the thermo-hygrometric comfort and the indoor air quality are of fundamental importance. This research is developed with the aim of verifying the indoor air quality following the installation of a passive cooling device in a historic building located in the province of L’Aquila in the municipality of Poggio Picenze in climatic zone E. This research aims to verify the functioning of a ventilation duct installed between the hypogeal and the second level of the structure that was installed to obtain air recirculation by exploiting the inertial potential of the hypogeal room. The first phase of the research was aimed at thermo-hygrometric monitoring using sensors installed on-site and controlled remotely in order to verify the operation of the device. The second-phase object of this text was useful in investigating the acquired indoor air quality level.

On the basis of experimental studies, the operational power of four borehole heat exchangers (BHE) under real conditions was determined. The research was carried out in 2018−2019. The theoretical power of the BHE was verified with its operating power. The amount of thermal energy absorbed from the ground by individual BHEs, the operating temperatures obtained at the inlet and outlet of the exchanger, the annual brine flow rate, and the average operating power of the tested wells in two heating seasons were compared and analyzed. Both in 2018 and 2019, none of the examined exchangers achieved an average unit capacity of a well. The aim of the work is to verify the specific ground thermal efficiency indicators adopted for the design of the lower heat source, determined using the computational method and the TRT test with data obtained on the basis of experimental tests. The differences between the results of the tests of the operating parameters of the analyzed BHEs were shown. The data... [more]

Electrolytes are involved in the thermal runaway (TR) process of cells, which is a potential hazard in lithium-ion batteries (LIBs). Therefore, the effects of different mass ratio of carbonate solvents (ethylene carbonate (EC)/propylene carbonate (PC)/ethyl methyl carbonate (EMC)) with LiBF4 and different environmental pressure on the combustion characteristics of electrolyte such as flame centerline temperature, mass loss rate (MLR) and heat release rate (HRR) were analyzed. The combustion process could be divided into four stages: ignition, stable combustion stage, stable combustion with flame color change stage and extinguishing; with the decrease of pressure, the MLR of electrolyte declined and the combustion time prolonged, while the temperature of flame centerline increased.

The article proposes a proprietary approach to the diagnosis of induction motors allowing increasing the reliability of electric vehicles. This approach makes it possible to detect damage in the form of an inter-turn short-circuit at an early stage of its occurrence. The authors of the article describe an effective diagnostic method using the extraction of diagnostic signal features using an Enhanced Empirical Wavelet Transform and an algorithm based on the method of Ensemble Bagged Trees. The article describes in detail the methodology of the carried out research, presents the method of extracting features from the diagnostic signal and describes the conclusions resulting from the research. Phase current waveforms obtained from a real object as well as simulation results based on the field-circuit model of an induction motor were used as a diagnostic signal in the research. In order to determine the accuracy of the damage classification, simple metrics such as accuracy, sensitivity, s... [more]

Electroluminescence (EL) measurements allow one to detect damages and/or defective parts in photovoltaic systems. In principle, it seems possible to predict the complete current/voltage curve from such pictures even automatically. However, such a precise analysis requires image corrections and calibrations, because vignetting and lens distortion cause signal and spatial distortions. Earlier works on crystalline silicon modules used the cell gap joints (CGJ) as calibration pattern. Unfortunately, this procedure fails if the detection of the gaps is not accurate or if the contrast in the images is low. Here, we enhance the automated camera calibration algorithm with a reliable pattern detection and analyze quantitatively the quality of the process. Our method uses an iterative Hough transform to detect line structures and uses three key figures (KF) to separate detected busbars from cell gaps. This method allows a reliable identification of all cell gaps, even in noisy images or if disco... [more]

Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sou... [more]

The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters throu... [more]

This research presents a validation methodology for computational fluid dynamics (CFD) assessments of rooftop wind regime in urban environments. A case study is carried out at the Donadeo Innovation Centre for Engineering building at the University of Alberta campus. A numerical assessment of rooftop wind regime around buildings of the University of Alberta North campus has been performed by using 3D steady Reynolds-averaged Navier−Stokes equations, on a large-scale high-resolution grid using the ANSYS CFX code. Two methods of standard deviation (SDM) and average (AM) were introduced to compare the numerical results with the corresponding measurements. The standard deviation method showed slightly better agreements between the numerical results and measurements compared to the average method, by showing the average wind speed errors of 10.8% and 17.7%, and wind direction deviation of 8.4° and 12.3°, for incident winds from East and South, respectively. However, the average error betwee... [more]

Reticulate porous ceramic reactors use foam-type absorbers in their operation which must fulfill two essential functions: favoring the volumetric effect and increasing the mass and heat transfer by acting as a support for the reactive materials. Heating these absorbers with highly inhomogeneous concentrate irradiation induces high thermal gradients that affect their thermal performance. Owing to the critical function of these component in the reactor, it is necessary to define a selection criterion for the foam-type absorbers. In this work, we performed an experimental and numerical thermal analysis of three partially stabilized zirconia (PSZ) foam-type absorbers with pore density of 10, 20, and 30 PPI (pores per inch) used as a volumetric absorber. A numerical model and an analytical approximation were developed to reproduce experimental results, and calculate the thermal conductivity, as well as volumetric heat transfer coefficient. The results show that an increase in pore density l... [more]