Showing papers in "Energy Sources Part A-recovery Utilization and Environmental Effects in 2022"
TL;DR: In this article , a hybrid AlexNet/Extreme Learning Machine (ELM) network was proposed to provide an optimal identification tool for the Proton-exchange membrane fuel cells (PEMFCs) by reducing the error amount between the empirical output voltage and the evaluated output voltage of the PEM fuel cell stack model.
Abstract: ABSTRACT A new optimized design of a hybrid AlexNet/Extreme Learning Machine (ELM) network to provide an optimal identification tool for the Proton-exchange membrane fuel cells (PEMFCs) is presented in this study. The major concept is to reduce the error amount between the empirical output voltage and the evaluated output voltage of the PEM fuel cell stack model using the proposed hybrid AlexNet/ELM. For enhancing the model formation of the AlexNet/ELM, a modified version of the African Vulture Optimization (MAVO) Algorithm, which is a new metaheuristic, is suggested. To analyze the efficiency of the suggested method, it is applied to a practical PEMFC benchmark case study for identification purposes. Then, the method is confirmed by comparison of the experimental data and standard AlexNet/ELM. The achievements indicated the better confirmation of the suggested AlexNet/ELM network with the experimental data. The results show that the highest relative error for training and test is 0.03% and 0.05342%, respectively, which shows a promising result for the study.
59 citations
TL;DR: In this paper , the authors proposed a new optimal hybrid renewable energy system (HRES) arrangement, including a photovoltaic system, wind turbine, and fuel cell, for electrifying a remote area in Turkey.
Abstract: ABSTRACT This study proposes a new optimal hybrid renewable energy system (HRES) arrangement, including a photovoltaic system, wind turbine, and fuel cell, for electrifying a remote area in Turkey. The study is based on considering system cost and reliability. To deliver an optimal configuration, system sizing has been designed based on an Amended version of the DragonFly optimizer. The achievements of the method have been then compared with some other published methods, including Particle Swarm Optimizer (PSO)-based algorithm and Firefly (FA)-based method. Simulation results show that the proposed method with 1,888,827.5 USD provides the minimum Net Present Cost value among the others. The main idea is to assess the objective function by lessening the Net Present Cost (NPC) by confirming based on the loss of power supply probability (LPSP). Final simulations indicated that the proposed approach provides lower NPC and LCOE toward the others.
50 citations
TL;DR: In this paper , the authors proposed an optimal design of combined cooling, heating, and power systems (CCHP) for a watersport complex, where the main purpose is to reduce the energy losses in economic, energy, and environmental terms of view.
Abstract: ABSTRACT The present study proposes an optimal design of combined cooling, heating, and power systems (CCHP) for a watersport complex. The main purpose is to reduce the energy losses in economic, energy, and environmental terms of view. The method has been established by optimizing the nominal capacity of the CCHP components for the watersport complex. The design parameters for proper configuration include the number of gas engines and their nominal capacity, boiler heating capacity, partial load, and the cooling capacity of the electric and absorption chillers, which should be selected optimally by the relation annual benefit based on two different scenarios of considering and not considering the capability of selling the extra electrical power to the network. To provide optimal results for the system, an improved metaheuristic technique called the developed African Vulture Optimization (dAVO) algorithm has been utilized. The results of the actual annual benefit for the suggested method are compared with different state-of-the-art methods to show the method’s superiority. The results show that without considering the CCHP system, the total cost of electricity purchasing equals 420959 $/year, while the cost function value is achieved positive, which shows the positive effect of the CCHP system in reducing the system costs.
48 citations
TL;DR: An effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO) based on the fitness function, and the performance was improved with a traffic density improved when many vehicles were considered.
Abstract: ABSTRACT Intelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers’ safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered.
32 citations
TL;DR: In this paper , a review of the potential of alcohol utilization in the form of blend or under dual-fuel combustion modes, as well as neat alcohol-fueled CI engine is presented.
Abstract: ABSTRACT Alcohol fuels, primarily ethanol and methanol, have emerged as one of the important alternatives for sustainable transportation and power generation applications, due to the overall lower carbon dioxide (CO2) emissions. The higher octane number of alcohol makes it suitable for spark ignition (SI) engines while lower blend ratios can be used for compression ignition (CI) engines as well. Since significant work and exploration have already been performed for the application of alcohol in SI engines, the present study is primarily focused on alcohol utilization in CI engines. This review majorly consists of three parts: first, a discussion on the physical and chemical properties of ethanol and methanol from the fuel perspective, second, combustion, and engine performance of CI engines fueled with alcohol and lastly, emissions characterization of alcohol as fuel. A summary of this review is provided which highlights the potential of alcohol utilization in the form of blend or under dual-fuel combustion modes, as well as neat alcohol fueled CI engine. Alcohol-fueled CI engine improves the soot-NOx trade-off characteristic in comparison to conventional diesel combustion, this fuel could be an enabler to meet future emissions regulations. Overall lower CO2 emissions (up to 15% lower compared to diesel) by utilizing alcohol as fuel make it suitable for sustainable transportation.
18 citations
16 citations
TL;DR: In this paper , the impact of injection pressure on the engine parameters amalgamated with copper (III) oxide composites as a nanofuel additive was examined, showing that the injection of nano-additives raises the injection pressure leads to enhanced engine combustion characteristics, including a maximum peak pressure and a faster heat release rate.
Abstract: ABSTRACT Current scenario of crude oil exhaustion and price rise has motivated researchers to opt and explore other forms of energy which are renewable and sustainable in nature. Waste plant oils have significant potential to become a viable alternative to petro-diesel fuel for transportation and manufacturing purposes. Esterification of unrefined waste oils has significantly addressed the issues mainly occurring due to highly viscous nature of the oil. This analysis aims to conduct a controlled study to examine the impact of injection pressure on the engine parameters amalgamated with copper (III) oxide composites as a nanofuel additive. Biofuel obtained from waste plants (Eichhornia Crassipes) is amalgamated with plain diesel in a 30:70 ratio and copper (III) oxide (Cu2O3) as nano-additive. It is essential to operate the engine over a wide range of injection pressures (180, 200, and 220 bar) for furnishing maximum efficiency when mixed with 90 ppm nano-additive volume fraction. The current analysis shows that the injection of nano-additives raises the injection pressure leads to enhanced engine combustion characteristics, including a maximum peak pressure and a faster heat release rate. At 220 bar, injection pressure with a 90-ppm volumetric fraction of nano-additives yielded superior results in comparison with its counterpart blends. The inclusion of nano-additives for increased injection pressures decreases emissions of hydrocarbon, oxides of nitrogen, and soot particles. Thus, biofuels engines benefit by enhanced injection pressure and decreased emission levels by successfully amalgamating copper (III) oxide as nano-additives. Combined effect of high pressure and nano-additive fuel furnishes a maximal progression of 3.5% in combustion efficacy and a 14% drop in BSEC with reduction of 14% in HC, 15% in NOx, and 15% in smoke.
15 citations
TL;DR: In this article , a two-stage process for the placement of fast-charging stations (FCSs) is proposed, in which the first stage is to minimize the land cost and maximize the EVs flow for FCSs placement.
Abstract: ABSTRACT Recently, electric vehicles (EVs) gained tremendous attention from government agencies and the automotive industry due to lower CO2 emissions, low maintenance, and operating costs. However, due to increasing EV penetration, the EV’s load affects the distribution network parameters like power loss, voltage profile, and harmonic distortion. Therefore, the proper placement of EV fast-charging stations (FCSs) is required for the reliability of the distribution network. Further, this paper proposes two-stage processes for the placement of FCSs. In the first stage, the charging station owner decision index (CSODI) has been introduced considering the land cost index (LCI) and electric vehicle flow index (EVFI). The CSODI has been formulated to minimize the land cost and maximize the EVs flow for FCSs placement. In the next stage, an optimization problem is formulated for minimizing the total active power loss by considering the distribution system operator (DSO) constraints. In addition, the minimization problem has been solved using the hybrid gray wolf optimization-particle swarm optimization (GWOPSO) algorithm. Therefore, the best possible locations were obtained by the GWOPSO with 198.93 kW power loss. Furthermore, the average 2.02% power loss for the GWOPSO technique is lower when compared to the PSO technique.
15 citations
TL;DR: In this paper , a new electric propulsion system based on electric motor, proton exchange membrane fuel cell (PEMFC) and battery for a small UAV was introduced and investigated.
Abstract: ABSTRACT Most Unmanned Aerial Vehicles (UAV) currently use propulsion systems based on gas turbines or internal combustion engines. However, such systems cannot be used in all sizes of UAVs. To perform a high- endurance mission, a UAV must have an efficient propulsion system and aerodynamic. On the other hand, batteries have a low energy density, which therefore increases the UAV’s weight and imposes penalties on the system. Using fuel cells as the main source of power generation in the UAV propulsion system can increase flight endurance and have reasonable fuel consumption. The aim of the present work is introduce and investigate of a new electric propulsion system based on electric motor, proton exchange membrane fuel cell (PEMFC) and battery for a small UAV. Two different battery discharge strategies are compared for propulsion system design. In addition, static optimization is applied to determine the appropriate size of the various components of the propulsion system. It was found that, the required power at the theoretical endurance speed is about 42.13% lower than that at the actual endurance speed. In addition, the required hydrogen rate and PEMFC area to provide the required power in the proposed hybrid propulsion system are 55.7 g/h and 0.09 m2, respectively. Moreover, the payload weight (with lithium-ion polymer battery) for two battery discharge strategies was 0.71 and 1 kg, respectively. The sensitivity analysis is also used to determine the parameters affecting the final performance of the propulsion system.
14 citations
TL;DR: In this article , the effect of variation in open area ratio (β) in multi-V rib roughened single pass solar air heater (SPSAH) is computed.
Abstract: The effect of variation in open area ratio (β) in multi-V rib roughened single pass solar air heater (SPSAH) is computed in this study. Different perforated plastic ribs are used to develop variations in perforation. Four values of the open area ratio (β) ranged from 0 to 0.31, six values of relative roughness width (W/w) ranged from 2 to 10, and nine values of the Reynolds number (Re) ranged from 2000 to 18,000. These values were under consideration, along with fixed values of other parameters. Optimum values of performance parameters achieved at an open area ratio β = 0.27 with W/w = 6. The optimum range of improvement in Nusselt number ratio (Nu/Nus) and thermo-hydraulic performance parameter (THPP) was 6.33–8.19 and 3.78–5.41, while duct friction ratio (f/fs) was recorded within the range of 3.68–4.78 when compared to the flat channel, respectively. A correlation for Nusselt number and friction factor with varying parameters was also established with ±12% and ±7.5% accuracy, respectively.
13 citations
TL;DR: It has been reported that ANFIS tool can be successfully applied in wind output power estimations, as long as wind speed and turbine rotor rotational speed values are provided without the need for numerous experimental measurements, which induces additional time, labor, and measurement expenses.
Abstract: ABSTRACT Among renewable energy generation technologies, wind energy has become one of the most outstanding issues, especially in the last decade. Wind speed is the most critical parameter influencing the power obtained from a wind turbine. The unstable structure of the wind causes an impossibility to receive a direct theoretical relation between wind power and speed. Accordingly, obtaining a simulation of the generated turbine power concerning the approaching wind speed has become vitally essential, nowadays. In the current study, generated wind turbine power (P) has been predicted using three forecasting methods. The computer was trained using wind speed (V) and the turbine rotor rotational speed (ṅ) as the inputs of the forecasting methods. The methods used for this purpose were to include adaptive neuro-fuzzy inference system (ANFIS), Elmanneural network (ENN), and feed-forward neural network (FNN) approaches. In the training of the programs, among the cumulative of 43,800 wind speed, turbine rotor rotational speed, and wind power data, 80% and 20% of the total data were used for training and testing stages of the algorithms, respectively. The statistical results of the computations demonstrated that among three methods, ANFIS gave better outcomes when compared to ENN and FNN, in both the training and testing stages of the algorithms. The proposed models of the current study have revealed low and acceptable mean absolute error (MAE) and root mean square error (RMSE) statistical error results of ANFIS tool; corresponding to 52.448 kW and 87.204 kW error, respectively, were obtained at the training stage, whereas 48.675 kW and 78.453 kW error, respectively, were obtained at the testing stage. For the estimation of wind power, while the coefficient of determination (R 2) was detected as R2 = 0.9948 and 0.9961 in training and testing stages, respectively, with ANFIS model, it was found out as R2 = 0.9942 and 0.9957 with ENN model and R2 = 0.9943 and 0.9956 with FNN model. Namely, it has been reported that ANFIS tool can be successfully applied in wind output power estimations, as long as wind speed and turbine rotor rotational speed values are provided without the need for numerous experimental measurements, which induces additional time, labor, and measurement expenses.
TL;DR: In this article , two highly efficient Arnold's Cat Map and Henon Map-based chaotic approaches are employed to effectively disperse the shading by reconfiguring the modules without disturbing the electrical circuitry.
Abstract: ABSTRACT The partial shading phenomenon significantly limits the PV array output. To maximize the output power during shading, various reconfiguration techniques have been reported in the literature. However, many of these techniques are not scalable, ineffective, and inconsistent in uniform shade dispersion. Therefore, two highly efficient Arnold’s Cat Map and Henon Map-based chaotic approaches which are widely used in image encryption are employed to effectively disperse the shade by reconfiguring the modules without disturbing the electrical circuitry. The proposed approaches are evaluated in a MATLAB environment for symmetrical 8 × 8 PV array and unsymmetrical 5 × 7 PV array under different groups of progressive shading like left-to-right, triangular, top-to-bottom, and diagonal shading conditions. The efficacy of the proposed approaches is compared with conventional Series-Parallel, Total-Cross-Tied, existing Chaotic Baker-Map, Odd-Even, and Odd-Even-Prime-based reconfiguration techniques and extensively analyzed using different performance indices. A laboratory experimental setup of a 4 × 4 PV array reconfiguration system is developed and examined under distinct progressive shading cases. From the quantitative results obtained, it is noted that the proposed approaches offer consistently superior and reliable performance compared to existing state-of-art configurations under shading reinforcing their effectiveness.
TL;DR: In this article , microwave-assisted pyrolysis, supercritical water gasification, and plasma gasification are compared to highlight their advantages and limitations in process and techno-economic feasibility.
Abstract: ABSTRACT Growing non-biodegradable waste plastics pose a significant environmental challenge that cannot be addressed by conventional methods alone. Therefore, alternative waste management methods such as plastic-to-fuel methods that convert waste plastics into valuable biofuels via thermochemical degradation must be investigated. Waste plastic pyrolysis and gasification are popular plastic-to-fuel technologies that will be instrumental in circular economies. Therefore, they must be discussed and compared to highlight their advantages and limitations in-process and techno-economic feasibility. Thus, this paper tries to reach three technologies: microwave-assisted pyrolysis, supercritical water gasification, and plasma gasification, highlighting their strengths and weaknesses. It appears that the diesel-like pyrolysis oil from microwave pyrolysis can be used in internal combustion engines to mitigate fossil fuel dependence. Moreover, gasification technologies could help in the growth of integrated biorefineries that can extract hydrogen from syngas to produce value-added chemicals. It is anticipated that their industrial-scale implementation could be beneficial for landfill reclamation and mitigation of plastic-related environmental harm. However, these technologies are currently at low technology readiness levels. Therefore, more studies are required to spotlight their in-depth techno-economic feasibility and provide a research direction to economize these technologies further to maximize their economic rate of return. Graphical Abstract
TL;DR: In this paper , the impact of incorporating an oxygenated fuel additive (H2O2)) into a biodiesel/diesel fuel mix on diesel engine combustion efficiency and instability was investigated.
Abstract: ABSTRACT The current study intends to assess the impact of incorporating an oxygenated fuel additive (hydrogen peroxide (H2O2)) into a biodiesel/diesel fuel mix on diesel engine combustion efficiency and instability. The combustion, performance, and emission properties were evaluated using advanced test equipment to offer a full study. The combustion instabilities in terms of cyclic fluctuations were investigated to discover any unusual behavior when H2O2 was added to biodiesel/diesel fuel mixtures. Statistical and continuous wavelet transformations were used to investigate the cyclic fluctuations in peak cylinder pressure and indicated mean effective pressure (IMEP). Diesel, B20 (20%/80% of biodiesel/diesel fuel), B20H5 (20%/5%/75% of biodiesel/H2O2/diesel fuel), and B20H10 (20%/10%/70% of biodiesel/H2O2/diesel fuel) were tested in the study. Experiment results indicated that B20H10 produced the best results. In comparison to B20, B20H10 improved BTE by 6.56% and HRR by 8.5%, while decreasing BSFC by 7.98%, CO and HC emissions by 26.5%, and 6.67%, respectively. Furthermore, the peak pressure and IMEP cyclic changes were also well within permissible ranges. However, it was shown that adding H2O2 to B20 increased NOx emissions. The cyclic variations in the cylinder peak pressure and indicated mean effective pressure were analyzed using statistical methods and continuous wavelet transformation. No abnormality was detected in cyclic variation, thus ruling out any unsafe behavior in introducing test fuel additive to biodiesel blends. In principle, introducing H2O2 into a biodiesel/diesel fuel mix helped improve the engine performance and emissions while having no negative effects on engine life. Graphical abstract
TL;DR: In this paper , the authors proposed a virtual oscillator control (VOC) for parallel-connected single-phase inverters (SPIs) in an islanded microgrid (MG), where the non-linear dynamical equations of the oscillator are analyzed and its nonlinear current source (NCS) is made simpler in order to develop new VOC for SPIs.
Abstract: ABSTRACT This paper describes the concept of virtual oscillator control (VOC) for parallel-connected single-phase inverters (SPIs). The principal idea is to introduce a series of weakly coupled oscillators (Deadzone oscillator-based VOC and Van der Pol oscillator-based VOC) that can be used for the regulation of single-phase power inverters in an islanded microgrid (MG). Its dynamic equations are used to provide the frequency and amplitude references of the inverters. In these traditional methods, there is always the presence of a 3−order harmonic in the output voltage, which causes a significant amount of 3−order harmonic current in the system. The non-linear dynamical equations of the oscillator are analyzed and its non-linear current source (NCS) is made simpler in order to develop new VOC for SPIs that can effectively get rid of the 3−order harmonic component in the oscillator’s output voltage. Finally, an extensive comparison of Deadzone-based VOC (Dz-VOC), Van der pol-based VOC (VdP-VOC), and new VOC-based controllers with linear and nonlinear loads is presented. The new VOC-based controller minimizes the 3-order harmonic component in the output voltage and achieves a quicker response compared to the conventional VdP-VOC-based controller. Simulation results of the Dz-VOC, VdP-VOC, and proposed new VOC-based control methods with different loads (Resistive, Linear RLC, non-linear) were compared and analyzed in detail. The total harmonic distortion (THD) of the current in Dz-VOC, VdP-VOC, and new VOC are 1.98%, 1.11%, and 0.21%, respectively. The 3rd harmonic is dominant in both Dz and VdP VOCs, while in the new VOC the 3rd harmonic is very less and below 0.1%. The new VOC is also showing good current sharing and fast voltage synchronization (within 0.2 s). Hardware experimentation is also carried out to analyze the efficacy of the proposed new VOC controlled SPI in standalone MG. The results clearly depict that the new VOC control strategy is quite efficient in handling the output voltage harmonics and situations of different loadings in the standalone MG.
TL;DR: In this article , a comprehensive review was conducted to describe, evaluate, and compare most of the software (36 software were considered), models, and algorithms used to design PV systems in the past eight decades.
Abstract: ABSTRACT Solar energy and photovoltaic (PV) systems became an essential part of the global energy profile. The PV systems are designed using different configurations such as standalone, grid-connected, and tracking. However, PV systems could be added to other renewable energy systems or nonrenewable energy systems such as wind turbines and diesel generators, respectively. In this context, designers, researchers, and engineers working to find the optimum design fitting the electrical load in terms of technical, economic, environmental, and social aspects. Many software, models, and algorithms were invented and proposed to help to find the optimum PV sizing and design. In this paper, a comprehensive review was conducted to describe, evaluate, and compare most of the software (36 software were considered), models, and algorithms used to design PV systems in the past eight decades. It is found that PV system design optimization tools developed with time and needs. Different classifications are used for design software, sometimes as classical and artificial or single and hybrid algorithms. However, hybrid algorithms became the most used algorithm due to their flexibility and ability to deal with complex problems. Also, comparison and critical review are presented, and a case study is given in this paper. It is found that REPS.OM software results are closer to the case study experimental results compared with HOMER software with less than 7% variation between the two software. The review presented in this paper provides useful information to identify PV system design software suitable for the user application.
TL;DR: In this paper , a desalination system integrated with solar energy in Larak Island, Iran, to determine the best size with the least cost, environmental damage, and maximum reliability using the division algorithm is presented.
Abstract: ABSTRACT The present study aims to design, model, and optimize a desalination system integrated with solar energy in Larak Island, Iran, to determine the best size with the least cost, environmental damage, and maximum reliability using the division algorithm. The environmental impacts of designed configurationsare evaluated using the EcoInvent database and the IMPACT 2002 + method available in SimaPro software. This study shows that when the loss of power supply probability (LPSP) is 10%, the system has the least cost and environmental impact. Increasing water significantly increases costs and environmental impacts. A decrease in LPSP (%) leads to a significant cost increase and environmental impacts. The environmental assessment results of freshwater production by desalination integrated with the photovoltaic panel show that 1 m3 of freshwater leads, on average, to 4.83E-7 DALY (disability-adjusted life years) damage to human health, 0.157 PDF*m2*yr (potentially disappeared fraction of species per m2 per year) damage to ecosystem quality, 0.280 kg CO2 eq damage to climate change, and 4.614 MJ damage to resources (for LPSP from 2 to 10%). According to the results, the attachment of a diesel generator to desalination integrated with photovoltaic panels increases the damage to human health, climate change, resources, and ecosystem quality by 562%, 440%, 389%, and 144%, respectively.
TL;DR: In this article , an improvised moth swarm algorithm (i-MSA) is proposed to resolve the load frequency control issue of microgrid (MG) systems, which is a cluster of distributed generation (DG) power sources like photovoltaic (PV), wind power systems along with electric storage devices such as electrochemical batteries, flywheel systems and electric vehicles.
Abstract: This research work introduces an improvised moth swarm algorithm (i-MSA) to resolve the load frequency control issue of microgrid (MG) systems. A microgrid system is a cluster of distributed generation (DG) power sources like photovoltaic (PV), wind power systems along with electric storage devices such as electrochemical batteries, flywheel systems, and electric vehicles. The unpredictable responses of DG sources influence a large frequency deviation, which creates frequency regulation problems taken as the problem objective for this research work. In the preliminary stage, the effectiveness of the i-MSA has been established by performing a comparative analysis considering single/multidimensional benchmark test functions. To validate the superiority of the i-MSA method, the technical viability has been measured by comparing the outcomes of the proposed approach with those of some newly recommended optimization methods. Furthermore, an advanced hybrid fractional order type-2 fuzzy PID (FO-T2F-PID) controller is projected for frequency control of the MG system. The uncertainties due to DG source penetration in the MG system and the impact of storage devices including the EV system to regulate the frequency have been studied using the proposed approach. The effect of operational parameter variations has been studied through a sensitivity test. From the data analysis, it is observed that the i-MSA-scaled FO-T2F-PID approach presents advancement in the fitness function of 29.11%, 33.53%, 47.16%, and 55.37% as compared to h-DE-PS: 2DOF/PID, I-JAYA: fuzzy PD/PI-PD, hybrid DFPS: TID, and Kriging: FOPID approaches, respectively. Similarly, the improvement in the settling interval for the proposed approach is observed to be 33.83%, 35.82%, 38.12%, and 42.18% as related to h-DE-PS: 2DOF/PID, I-JAYA: fuzzy PD/PI-PD, hybrid DFPS: TID, and Kriging: FOPID approaches, respectively. To indorse the practicability of the i-MSA-based FO-T2F-PID method, real-time simulation testing has been carried out in the MATLAB-interfaced OPAL-RT experimental setup. Finally, the optimum results obtained from the proposed approaches are confirmed by relating the same to some recently published frequency regulation outcomes in a benchmark power system model. It is observed that the i-MSA-based FO-T2F-PID method delivers advancement in microgrid frequency control compared to recent research outcomes.
TL;DR: In this article , a hybrid meta-heuristic algorithm, hybrid Chimp-Sine cosine algorithm (HCSCA), was proposed for PV panel equivalent circuit parameter extraction, which provided satisfactory performance with the proposed algorithm and recommended for its practical implementation.
Abstract: The research on deriving accurate equivalent circuit of solar photovoltaic (PV) modules is increasing due to the necessity of constructing efficient energy conversion devices. The PV panel manufacturers provide data on three essential points on current–voltage (I–V) characteristics for standard temperature conditions (STC). Hence, the research on PV modules for different environmental/operational conditions with equivalent mathematical models are quite complex. Therefore, there is a necessity to derive accurate PV equivalent circuit parameters using novel AI-based approaches. This work proposes a novel hybrid meta-heuristic algorithm, hybrid Chimp-Sine cosine algorithm (HCSCA), for PV panel equivalent circuit parameter extraction. A well-known single- and double-diode PV models have been investigated with the proposed algorithm for different categories of PV modules, namely monocrystalline, polycrystalline, and thin film. The parameters derived from the proposed approach result in minimum error over different executions in the order of less than 10−10, which recommends better implementation in the present scenario. The nature of extracted parameters and I–V characteristics of considered PV panels are examined over different runs, which provided satisfactory performance characteristics with the proposed algorithm and recommended for its practical implementation.
TL;DR: In this article , LiNO3 + NaCl/expanded graphite (EG) composite phase change material was used as supporting material to enhance the eutectic PCM samples thermal conductivity.
Abstract: The main objective of this present research work is to investigate the feasibility of LiNO3 + NaCl/expanded graphite (EG) composite phase change material for medium-temperature thermal energy storage systems. EG was used as supporting material to enhance the eutectic PCM samples thermal conductivity. The XRD, FTIR and SEM results reveal that EG particles are uniformly dispersed to the PCM material and show better chemical stability. The phase transition temperature and latent heat values of pure eutectic PCM and composite eutectic PCMs are experimentally measured with the help of differential scanning calorimetry (DSC). The calculated thermal conductivity intensification of composite PCM with 9% EG composition is 5.75, significantly more than pure PCM salt. The composite PCM showed good thermal reliability performance even after 500 thermal cycles. The charging time of the PCM significantly decreases with EG loading. The corrosion rate of five metal specimens is determined when embedded in pure PCM and composite PCM samples at more than phase transition temperature for 1440 h. The metal specimens embedded in composite PCM show good corrosion stability. Among all the selected metal specimens, stainless steel 316 L showed better corrosion resistivity in both PCM samples. Furthermore, an artificial neural network model is developed to predict the DSC output parameters such as temperature and heat flow at various EG loading (%), heating rate, and conversion points.
TL;DR: In this paper , a new cascade fuzzy-noninteger (fractional order) proportional derivative with filter-proportional integral (CFPDμF-PI) control policy is proposed to cope with the frequency abnormality that occurs due to the presence of renewable generating units in the existing power system.
Abstract: ABSTRACT Current power system has taken a paradigm shift from the conventional structure to the hybrid consisting of thermal and renewable energy sources (RESs), such as hydro, tidal, geothermal, etc. power generating units. However, RESs are intermittent and extremely unpredictable, thus may cause huge frequency deviations. For the smooth operation of the power system in the wake of RESs intermittency and continuously varying load demands, an robust and ameliorated load frequency control (LFC) strategy is requisite. Therefore, in this paper, a new cascade fuzzy-noninteger (fractional order) proportional derivative with filter-proportional integral (CFPDμF-PI) control policy is proposed to cope with the frequency abnormality that occurs due to the presence of renewable generating units in the existing power system. The CFPDμF-PI controller adopts FPDμF as a master and integer order PI as a slave controller. The recently introduced slime mold algorithm (SMA) is employed as a stochastic optimizer to tune the controller parameters. Two case studies have been presented to investigate the performance of the proposed controller. Case-1 simply focuses on the implementation of the proposed technique on a two-area non-reheat thermal power system while case-2 involves the two-area thermal-hydro power system. In both cases, the efficiency of the control method is validated in the presence of tidal, geothermal power plants, and solid oxide fuel cells. To affirm the contribution of our proposal, comparative studies with the existing state-of-the-art techniques have been conducted under identical conditions. From the data analysis, it is witnessed that the proposed control approach with the integration of RESs presents an advancement in the four performance indices by 75.29%, 77.79%, 90.48%, 94.68%, and 36.19%, 40.87%, 49.41%, 50% for case-1 and case-2, respectively. Different scenarios for the robustness analysis validate the capability of the proposed approach in LFC and suitability for other real-world applications.
TL;DR: In this article , a wireless data acquisition system and a method of self-cleaning the PV panels are developed and tested and the proposed cleaning system not only cleans the PV system but also protects it from hailstorms.
Abstract: ABSTRACT Solar photovoltaic (PV) technology can be considered a suitable option for fossil fuels because of its free availability and ease of use. The deprivation of power generation from PV systems due to environmental factors shows a major flaw in solar PV systems. As a result, they are unreliable in deserts or remote locations. The accumulation of dust in solar PV systems is a major problem. Solar PV energy prediction is a critical factor in future ecological and reliable energy sources for system stability. Real-time observing systems are essential in a remote PV system for collecting all the parameters needed to evaluate and optimize system performance. Many existing studies use costly and difficult-to-use wired data acquisition systems that run on LABVIEW licensed software. PV panels must be cleaned on a regular basis to achieve maximum efficiency. Most existing cleaning methods require water for cleaning the PV system. In this study, a wireless data acquisition system and a method of self-cleaning the PV panels are developed and tested. The proposed cleaning system not only cleans the PV system but also protects it from hailstorms. We investigate the performance of a 106 W PV system under Jaipur weather conditions over a one-year period using a proposed wireless data acquisition and monitoring system. The results revealed that the exposure of 12 months of 106 W PV panels under different seasons in Jaipur reduced the PV system’s efficiency by 24.5% in summer, by 15.6% in winter, by 5.14% in post-monsoon and by 1.95% in monsoon. The PV panels’ maximum efficiency is reached at a panel temperature of 41°C in the summer and 48°C in the winter. We observed that the proposed data acquisition system is applicable, durable, efficient, and appropriate for severe outdoor conditions for observing and collecting operational information about the PV system. The efficiency of a fixed PV system with daily manual cleaning was compared to that of a proposed cleaning PV system for a month and the proposed cleaning PV system’s efficiency was only 1.13% lower. The result shows that the proposed cleaning PV system performs well even in semi-arid environments.
TL;DR: In this paper , a rule-based intrusion detection technique is added to enhance the detection of intruders using Deep Belief Network (DBN), which is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation.
Abstract: The convergence from the electric grid to the smart microgrid motivates the incorporation of the intrusion detection system to identify intruders and mitigate the resultant damages to ensure system stability. It is planned to employ the Deep Belief Network (DBN), which is one of the deep learning techniques with some improvement to detect the attacks in a microgrid. To improve the accuracy of the detection, a rule-based detection technique is added to enhance the detection of intruders using DBN. The proposed technique is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation. The proposed Enhanced DBN (EDBN) performance is measured in different bus representations for identifying the higher hit rate and rejection rate, lesser miss rate and false-positive rate. Two attacks, such as False Data Injection and Denial of Service attacks, are generated by Greedy Algorithm and are detected by the proposed technique. Compared to the existing detection and control system, the proposed EDBN technique provides accuracy higher than 92%, false alarm rate less than 1%. Thus, the experimental results show that the proposed technique accuracy is higher than the existing intrusion detection techniques in a microgrid.
TL;DR: In this paper , the authors used the jackfruit peel (JP) waste for the synthesis of an inexpensive and eco-friendly activated carbon (AC) as a sustainable energy material for the development of supercapacitor devices.
Abstract: ABSTRACT To address current energy consumption and chemical production, biomass is a promising alternative that can be used as an inexpensive and sustainable energy material for the development of supercapacitor devices. In this study, the jackfruit peel (JP) waste is used for the synthesis of an inexpensive and eco-friendly activated carbon (AC) as a sustainable energy material for the development of supercapacitor devices. JP wastes were synthesized by three-step approach consisting of KOH activation via nitrogen carbonization at 600°C and CO2 activation at different temperatures (850, 900, and 950°C). The specific capacitances of supercapacitor cells are 85 F g−1, 191 F g−1, and 131 F g−1 for the JP-850, JP-900, and JP-950, respectively. Based on the results, the JP samples are suitable to be developed as an inexpensive and eco-friendly AC for electrode material in supercapacitor applications due to their good physicochemical properties and electrochemical performance.
TL;DR: In this article , an innovative maximum power point tracking (MPPT) approach for photovoltaic system under temperature varying was proposed to improve tracking performance under difficult scenarios of temperature change.
Abstract: ABSTRACT This paper offers an innovative maximum power point tracking (MPPT) approach for photovoltaic system under temperature varying. Basically, the innovative approach is introduced to improve tracking performance under difficult scenarios of temperature change. By far, it can be used to avoid the main shortcomings of the conventional MPPT strategies, e.g., ripple around the MPP at steady-state regime, sluggishness velocity converging, and loss of tracking direction under fast change of temperature. Also, it can be used to improve the tracking performance under low irradiance level and rapid load change. With respect to its direct control strategy based on the photovoltaic current control, it provides a quick tracking of the real MPP without steady-state fluctuations. To show the advantages and accuracy of the innovative MPPT approach, a comparison with other traditional strategies, e.g., P&O and INC techniques is investigated using simulation in MATLAB/Simulink® software under different scenarios of temperature, load, and insolation levels. In the light of the results collected, the innovative MPPT approach minimized the convergence time by five times, reduced the steady-state fluctuations to zero and improved the average tracking efficiency by 8.51% and 9.04% compared to the P&O and INC MPPT schemes, respectively.
TL;DR: In this article , varying concentrations of HCl and H2SO4 were used to examine and maximize the sugar extraction from rice paddy straw, and a temperature-variation study was also performed to evaluate the variation in sugar yielding with 2.0% HCl (v/v).
Abstract: ABSTRACT Over the past decades, bioethanol has emerged as an important alternative to fossil fuels. Moreover, bioethanol reduces the overall CO2 emission (greenhouse gas emission) compared to petroleum fuels as these are plant derived. Bioethanol will also provide energy security in the regions/countries where fossil fuels are in scarcity. Bioethanol is produced widely using edible resources like sugarcane, rice, and corn grains, etc. It has been a concern that the edible sources may create problem in food security. With these concerns, non-edible feedstock such as rice paddy straw and corn straw-based lignocellulose biomass has drawn tremendous attention toward the second generation (2G) ethanol production as a sustainable bioenergy source for internal combustion (IC) engine. Cellulose and hemicellulose contents are higher in biomass, which can be used as a source of reducing sugar to produce ethanol. Higher concentration of lignin fibers in the non-edible raw materials makes the sugar extraction challenging. The use of acidic medium such as hydrochloric acid (HCl) and sulfuric acid (H2SO4) makes it easier to break down the lignin fibers and enhances the sugar extraction. In this work, varying concentrations of HCl and H2SO4 were used to examine and maximize the sugar extraction from rice paddy straw. HCl was observed to provide higher yielding of sugar compared to H2SO4. Furthermore, a temperature-variation study (during the hydrolysis process) was also performed to evaluate the variation in sugar yielding with 2.0% HCl (v/v). GRAPHICAL ABSTRACT
TL;DR: Simulation results showed high efficiency for the proposed IEPO-based transportation energy demand forecasting based on all of the employed models for decision-making in ROC.
Abstract: ABSTRACT A new methodology is suggested in this study to provide optimum forecasting of the future transportation energy demand in Taiwan. The paper introduces a new improved version of Emperor Penguin Optimizer (IEPO) to provide an optimal and suitable forecasting model. The forecasting was based on three different models including linear, exponential, and quadratic where their coefficients have been optimized using the suggested IEPO algorithm which is based on considering the population, the GDP growth rate, and the total annual vehicle-km. The study considers two different scenarios based on curve fitting and projection data. The results indicate that the RMS value for the TED forecasting based on the proposed IEPO algorithm applied to the linear, exponential, and Quadratic for training are 0.0452, 0.0461, and 0.0492, respectively and for testing are 0.0456, 0.0596, and 0.0642, respectively. This shows better results of the optimized exponential method’s efficiency. Simulation results showed high efficiency for the proposed IEPO-based transportation energy demand forecasting based on all of the employed models for decision-making in ROC.
TL;DR: In this paper , the experimental investigation of thermal and catalytic cracking using various types of plastics including HDPE, low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS) in a stainless steel semi-batch reactor under nitrogen atmosphere at the temperatures ranging from 350 to 500°C and by varying the residence times (60, 90, and 120 min).
Abstract: ABSTRACT Plastic waste has become a serious issue that causes environmental contamination because of not degrading naturally and neglecting in the landfills over the years. Therefore, this paper deals with the experimental investigation of thermal and catalytic cracking using various types of plastics including high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET) in a stainless-steel semi-batch reactor under nitrogen atmosphere at the temperatures ranging from 350 to 500°C and by varying the residence times (60, 90, and 120 min). In the catalytic cracking, the experiments were run by varying the catalyst to polymer ratios (5, 10, and 15 wt%), as well as a catalyst bed has been designed for catalytic cracking experiments. The produced liquid was analyzed using Gas Chromatography-Mass Spectroscopy (GC-MS) which revealed that the liquid produced contained mainly aromatic and paraffinic hydrocarbons that can be used as fuel. According to the experimental results, the highest liquid yield obtained from thermal cracking was 92.4 wt% using PS as feedstock at 500°C and 120 min, while the highest liquid yield obtained from catalytic cracking was 90.45 wt% using LDPE as feedstock and 5 wt% of the Ketjenfine PR.9 catalyst at 500°C and 90 min. Therefore, Ketjenfine PR.9 was found to be appropriate as a catalyst for the degradation of plastics.
TL;DR: In this article , the authors present a comprehensive analysis of various biomass used for biogas production considering the effects that co-digestion of these materials has on biOGas yield, as well as the technology involved.
Abstract: ABSTRACT Energy is an essential bedrock, which plays a high impact role in the running of domestic and industrial activities. Most energy used for these activities is majorly from conventional sources, which after combustion result in ecological imbalance, climatic affray, health hazards, and degradation of natural resources. Therefore, the quest for eco-friendly energy has made researchers to investigate on alternative energy, such as biogas. This review study presents a comprehensive analysis of various biomass used for biogas production considering the effects that co-digestion of these materials has on biogas yield, as well as the technology involved. It further evaluated the applicability of artificial intelligence for modeling and optimization of the anaerobic digestion process including the blend ratios, process parameters and so on. These indices determine the percentage methane yield from biomaterial. The review effort revealed that methane content of biomaterials digested without pre-treatment varies from 3.6 ± 0.7 to 443.55 ± 13.68 while the yield from biomaterials pre-treated using various methods varies from 301.38 mL /g to 0.73–5.87 L/week. Anaerobic digestion of the blends of cow dung, mango pulp, and Chromolaena odorata was reportedly necessary, as information is scantily available on it. The modeling of the resulting experimental data using different machine learning techniques such as an adaptive-neuro-fuzzy inference system and ANFIS for predicting biogas yield is a major information gathered in this study. The AI models reviewed have high correlation factors ranging from 0.8700 to 0.9998. This information gathered in this paper will motivate the production of useful fuel to complement the existing energy sources while offering a near-term and practical means for reduction of environmental pollution.
TL;DR: In this paper , a low-cost solution is discussed for solar air heating, which deals with integration of modified models of air heaters with graphite powder, brick powder, and desert sand.
Abstract: ABSTRACT Among various applications of solar energy, solar air heater is a common method to fulfill the demand of hot air for space heating. In this experimental work, a low-cost solution is discussed for solar air heating. This solution deals with integration of modified models of air heaters with graphite powder, brick powder, and desert sand. These sensible heat storage materials have been filled inside a small cylindrical-shaped container to be placed over the absorber of modified heaters for performance enhancement. Total three different configurations have been developed for comparative analysis such as, configuration 1 for graphite powder testing, configuration 2 for brick powder testing, and configuration 3 for desert sand testing. Results are compared to the same design conventional heater to find out the best configuration, which shows that the configuration 1 is the best among all tested configurations. Under the configuration 1, enhanced heat transfer is observed about 541.2 W/m2K, thermal efficiency about 37.62%, overall heat loss about 6.71 W/m2K, and maximum exhaust air temperature about 41.2°C. For a better comparative analysis, configuration 1 is further compared to some other designs of solar heaters purposely for the plate temperature and thermal efficiency. Total cost of the best configuration (model) is about $59.5.