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China’s renewable energy products have an important place in international trade, and the conclusion of the RCEP agreement can create favourable external conditions for China’s renewable energy product exports. This paper measures the export potential of China’s renewable energy products to other RCEP countries through a trade gravity model. The results show that (1) the GDP of the importing and exporting countries, the energy consumption of the importing countries, and the particulate emissions damage significantly enhance China’s renewable energy product exports; (2) among the RCEP countries, China has a greater potential to export renewable energy products to five countries in 2020, which are mainly located in East and Southeast Asia; and (3) from a dynamic perspective, China’s renewable energy trade potential is increasing, while in the Oceania region it is weakening, while trade links to Southeast Asian countries are developing in a more mature direction. Based on the above research, this paper puts forward corresponding policy recommendations.

The development of modern computational fluid dynamics (CFD) technology provides many preliminary references for experimental design. In addition, the CFD calculation results verified by experiments can display enormous microdata in areas that are difficult to measure through experiments, as an extension. This can make measurements more reasonable and effective, shorten measurement times, and save manpower and capital significantly. Therefore, it is vital to verify the accuracy of CFD calculation results, especially in cases of complex structures and multiphase flows. The results of model tests can be utilized in a prototype experiment by properly designing a test section and selecting a working fluid if the Reynolds similarity criterion is met. Optical measurement technology is a noninvasive measurement method, and the impact on the flow field can be almost negligible. It is advised to use transparent materials and prepare a refractive index-matching (RIM) fluid to obtain a good optical path. Polymethyl methacrylate (PMMA) is widely used in flow field visualization experiments because of its good light transmission and mechanical strength. This review is aimed at introducing the current status of different flow field measurement techniques; moreover, it is intended to help the readers to become more familiar with the principles of RIM, the characteristics, applications, and usage suggestions of various RIM fluid schemes of PMMA, providing references for researchers in the design, preparation, and conducting stages of flow field visualization measurement experiments. This review is divided into five sections. In the introduction section, Chapter 1, relevant research developments and related results of flow field measurements are presented, followed by the innovations and benefits of this paper. In Chapter 2, the flow field visualization measurements are presented and a derivation is shown. In Chapter 3, some RIM fluid schemes of PMMA and their applications are given, which are very valuable for peers. In Chapter 4, the measurement and analysis of some physical properties are described. In the RIM process, it is necessary to focus on the RI, density, dynamic viscosity, compatibility, stability, safety, and cost of RIM fluids. These factors greatly impact the accuracy of experimental results, experimental progress, and safety of the experimenters. Based on the analysis and our practical experience, some suggestions are given for preparing and using RIM fluids. In the conclusion section, Chapter 5, the results and practical implications of this paper are summarized.

Due to the extensive usage of fossil fuels such as coal, oil, and gas, the energy crisis and environmental pollution issues have garnered global attention, making the creation of clean, renewable energy an unavoidable option. Solar photovoltaic energy production is regarded as one of the most promising technologies owing to its safety, dependability, and lack of environmental impact. However, the adoption of photovoltaic systems comes with some risks that may affect their deployment. This paper examines the risks of sustainable photovoltaic power plants through a realistic case study. A comprehensive approach is presented through which consultants can use linguistic variables to express their opinions about risks, priorities, and strategies for dealing with them. Evaluating and prioritizing risk assessment is a complex task that requires consideration of multiple criteria. Therefore, this paper proposes a hybrid multicriteria decision-making (MCDM) approach to deal with risks and their priorities. The risks of five aspects of sustainability were considered: the economic, technical, institutional, social, and environmental aspects. The developed approach consists of two decision-making methods: the criteria importance through intercriteria correlation (CRITIC) method, which is used to determine the weights of the criteria, and the evaluation based on distance from average solution (EDAS) method, which is used to arrange risks according to their priorities. The developed approach is performed in a neutrosophic environment and uses type-2 neutrosophic numbers (T2NN) to address uncertainty. Also, the paper adopts a developed model to determine the consultants’ reputation weights instead of assuming them. In addition, the model was applied to classify the risks of each aspect of sustainability into three levels, which are high, medium, and normal risks. The results indicate that 52% of the risks in all aspects are high risks, 36% are medium risks, and 12% are usual risks. Also, sensitivity analysis was performed to prove the correctness and stability of the developed approach with differences in weights.

This paper designed a protection scheme for utility grid with high share of renewable energy (RE) generated from wind energy and solar energy plants. This is based on extraction of features from the current using Stockwell transform (ST), Hilbert transform (HT), and alienation coefficient. A Stockwell index (SI) is designed by extracting current features using ST, a Hilbert index (HI) is designed by extracting current features using HT, and an alienation index (AI) is designed by extracting current features using an alienation coefficient. A fault index (FI) is formulated by multiplying the SI, HI, AI, and WF (weight factor). This FI is implemented for fault detection. Fault classification is achieved considering number of fault phases and ground fault index (GFI). This GFI is designed by processing zero sequence currents applying ST. GFI effectively identifies the ground involved during event of a fault. A designed protection scheme is effective to identify faults in the scenario of high RE share and during various cases of study which includes the variations of fault impedance, different fault occurrence angles (FOA), fault incident at different nodes, and noisy condition. This protection scheme effectively discriminates the fault events from the operational events such as feeder operation, load, and capacitor switching. Performance of hybrid protection method formulated in this paper is better relative to alienation coefficient-based protection scheme (ACPS) reported in literature. The ACPS has maximum error and mean error of fault detection equal to 9.54% and 5.99%, respectively, which is relatively high compared to the respective values for the proposed method which are 1.89% and 0.978%, respectively. ACPS is effective for detecting the fault events in noise level of 30 dB SNR (signal-to-noise ratio) whereas the proposed method effectively identifies the faults in the high noise scenario of 20 dB SNR.

Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery’s performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery’s state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons.

Lithium-ion batteries (LIBs) require to be preheated under cold weather to restore performance, while DC pulse discharging is often considered a promising approach. Applying a larger pulse current can shorten the heating time, but it may lead to battery decay and demand additional cost. To keep the current lower in preheating, it is necessary to explore the effects of pulse current on the cell temperature rise and make full use of the battery’s heat-generating potential. This paper experimentally compared the effects of DC pulse, average (AVG) current, root mean square (RMS) current, pulse frequency, and state of charge (SOC) of the LIBs on its temperature rise rate, respectively. It is found that within the frequency of 1 kHz~10 kHz, the heat generation is affected by all factors above, among which the frequency has less impact while AVG current has the highest impact. During DC pulse discharging, it is shown that there is more entropy heat produced than the Joule heat in some SOC ranges. Therefore, entropy heat cannot be overlooked in the heat generation model. Moreover, as the thermal coefficient of entropy varies greatly with the SOC, the battery temperature rise rate will be inconsistent during the DC pulse discharging, which might cause an inconsistent warm-up time.

Optimal management of sources, storage, and responsive loads in the energy network leads to improvement of economic and technical situation of the network. However, note that single-layer energy management requires to provide the network operator with more amounts of information. This results in the increase in the processing speed for the network operator. Also, coordination of sources, storage, and responsive loads in the form of an aggregator such as an energy hub can play an important role for them in promoting the economic situation and energy efficiency. So, this paper presents the participation of grid-connected energy hubs (EHs) in the day-ahead (DA) and real-time (RT) energy markets. Scheme has a two-stage optimization framework. In the first stage, participation of EHs in the hourly DA energy market is executed based on a two-layer energy management system that is modeled as bilevel problem. EH model in DA market is formulated in upper-level problem aimed at maximizing their profit subject to meeting the linearized constraints of the EH’s operation. Lower-level problem deals with the operation of energy networks to minimize cost of energy purchase from the upstream network constrained to linearized optimal power flow equations. The second stage of participation of EHs in a 5 min RT market has an objective function to minimize the unbalance between the EHs’ profits in the mentioned two markets. This stage is subject to the upper-level constraints and the lower-level formulation in the first stage. Robust optimization models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage to achieve an optimal robust solution. Benders decomposition algorithm solves bilevel formulation in two-stage scheme. Numerical results confirm capabilities of the scheme in enhancing operation status of the networks and economic situation of EHs. Hence, the Benders decomposition algorithm is able to extract the optimal solution in shorter computation time. Two-layer energy management needs shorter computation time than the single-layer management. Simultaneous modeling of two real-time and day-ahead energy markets reduces the unbalance cost between these markets. The proposed scheme can enhance the operating and economic indicators of the network by up to around 18% and 26% compared to power flow studies of energy networks.

The marine industry must reduce emissions to comply with recent and future regulations. Solid oxide fuel cells (SOFCs) are seen as a promising option for efficient power generation on ships with reduced emissions. However, it is unclear how the devices can be integrated and how this affects the operation of the ship economically and environmentally. This paper reviews studies that consider SOFC for marine applications. First, this article discusses noteworthy developments in SOFC systems, including power plant options and fuel possibilities. Next, it presents the design drivers for a marine power plant and explores how an SOFC system performs. Hereafter, the possibilities for integrating the SOFC system with the ship are examined, also considering economic and environmental impact. The review shows unexplored potential to successfully integrate SOFC with thermal and electrical systems in marine vessels. Additionally, it is identified that there are still possibilities to improve marine SOFC systems, for which a holistic approach is needed for design at cell, stack, module, and system level. Nevertheless, it is expected that hybridisation is needed for a technically and economically feasible ship. Despite its high cost, SOFC systems could significantly reduce GHG, NOX, SOX, PM, and noise emissions in shipping.

In this paper, antimony sulfide (Sb2S3) nanobars were prepared via a facile poly[N-9 ′ -heptadecanyl-2,7-carbazole-alt-5,5-(4 ′ ,7 ′ -di-2-thienyl-2 ′ ,1 ′ ,3 ′ -benzothiadiazole) (PCDTBT) organic polymer-assisted hydrothermal method. The impact of PCDTBT polymer concentration on the crystal structure, shape, optical, and electrical characteristics of Sb2S3 nanobars and thin films was studied. Analysis by X-ray diffraction indicated that raising the PCDTBT concentration caused the low-crystalline structure of the Sb2S3 nanobars to transform into a polycrystalline structure. In addition, the sample had a unique Sb2S3 (orthorhombic) crystalline phase, and the crystal size increased from 32 nm to 42.6 nm. Analysis of the data revealed that the Sb2S3 nanobars had a relatively high absorption coefficient (∼105 cm−1) in the visible spectrum, with band gap values ranging from 1.71 to 1.89 eV. At a high polymer concentration, the surface morphology and grain development were enhanced, and the chemical composition analysis peaks indicate a 2 : 3 atomic ratio for Sb : S. Transmission electron microscopy (TEM) micrograph examinations demonstrate the appearance of the produced Sb2S3, which is bar-like and made of nanobars with a typical width of 200–300 nm and confirm the crystallinity of the nanobars. The optimized Sb2S3 device has a power conversion efficiency (PCE) of 5.11%, a short-circuit current density of 16.5 mA/cm2, an open-circuit voltage of 456 mV, and a fill factor of 66.62% under AM1.5G illumination. It was observed that PCDTBT has a significant role in the creation of Sb2S3 nanobars and thin films.

This paper presents a mathematical model of 255 kW grid-connected solar photovoltaic (SPV) system. To study the performance characteristics of the grid-connected SPV system, a new hybrid adaptive grasshopper optimization algorithm with the recurrent neural network (AGO-RNN) control technique was implemented. Furthermore, the power quality at the point of common coupling (PCC) has been studied using the conventional (PSO) and proposed AGO-RNN controllers. The characteristics of the PV system were analyzed under varying environmental (variable irradiance and temperature) conditions considering 3 different cases such as (i) standard test conditions (STC), (ii) variable radiation with constant temperature, and (iii) variable radiation with variable temperature. For each case, the total harmonic distortion (THD) has been calculated using the proposed AGO-RNN control technique, and the results were compared with particle swarm optimization (PSO) technique. The 255 kW PV model is initially developed and connected to a three-level NPC inverter, an MPPT-based perturbation and observation algorithm. Later, the PV model is controlled by an AGO-RNN pulse width modulation (PWM) controller and is then integrated to the main grid at PCC. The main advantage of this technique is exploiting the separate DC-DC converter between the SPV module and the inverter. Finally, the proposed grid-connected SPV system was simulated on MATLAB for analyzing the performance of the system based on its I-V and P-V characteristics, inverter voltage, grid power, gird voltage, grid current, power factor, and THD under different environmental conditions. The simulation results demonstrate that the current magnitude and THD of the SPVGC system are improved with the cutting-edge AGO-RNN controller compared to PSO in all three different scenarios, and this value is less than 1.6%, which is within the permitted limits of IEC 61727 standards.

Energy decision-makers have considered solutions to meet the demand for affordable and highly reliable energy due to population growth and technological advancement. One of these possibilities is the utilization of several energy carriers in one system as an energy hub. Instead of optimizing a single energy carrier, the energy hub optimizes a system with many energy carriers, including electricity, natural gas, and local heat, by use of its converters and storages. In this paper, a novel and new general framework is proposed to evaluate and compare both series and parallel connections of n hub to analyze cost and reliability aspects by considering coupling matrices. In order to compare the outcomes of the connections of several comparable hubs in both series and parallel modes, new indices are proposed and evaluated from the aspect of evaluating the amount of energy not supplied and the amount of energy input to each hub. Additionally, simulations are run for a variety of scenarios in order to better assess the proposed model and investigate each type of connection by evaluating the proposed performance indices. The results show that in all the examined scenarios, the total cost of the energy carriers in the series mode (link in the output) is lower than in the parallel mode (link in the input).

The emission crisis in the iron and steel sector prompted the search for modern systems that contribute to reducing the resulting emissions to alleviate the growing concerns about global warming. This research evaluates four flue gas treatment systems in the iron and steel sector through a case study in Egypt. A comprehensive approach is presented through which experts can use linguistic terms and their corresponding spherical fuzzy numbers (SFNs) to express their views on identifying aspects and indicators that affect the sustainability of flue gas treatment systems. Also, determining the optimal system for dealing with emissions is a necessary task, which requires consideration of many aspects of sustainability, including economic, environmental, and technological aspects and their subindicators. This paper presents a new hybrid approach to multicriteria decision-making (MCDM) under a spherical fuzzy (SF) environment that takes into account several incompatible indicators. The SF-CRiteria Importance through Intercriteria Correlation (SF-CRITIC) has been used to assess and prioritize the main aspects and subindicators. The SF-COmbinative Distance-based ASsessment (SF-CODAS) has been applied to evaluate and rank the selected systems. A sensitivity analysis has been implemented to confirm the effectiveness of the recommended hybrid approach and the stability of its results by changing the weights of the indicators used. Also, a comparative analysis has been fulfilled with MARCOS and WASPAS methods under an SF environment to validate the proposed approach. The findings show that the environmental aspect is the highest evaluated with a weight of 0.431, followed by the technological aspect with a weight of 0.326, and they are the basis for enhancing the sustainability of low-emission systems in the iron and steel sector. Furthermore, the conclusions advise optimizing low-emission systems for sintering flue gas in the iron and steel sector to improve sustainability.

Irrigation systems to supply water to agricultural land are essential in remote and isolated areas. However, these areas often face challenges and obstacles in obtaining energy for use in irrigation since many depend on diesel generators (DGs) to produce electricity. A farm located in a remote area in Al-Jafr, Jordan, uses a 100 kW DG to supply its need for electric energy for irrigation purposes. Its energy consumption is 500 kWh/day at $0.29/kWh. This paper designs a new hybrid renewable energy system (HRES) for this farm by conducting simulations using the HOMER (Hybrid Optimization of Multiple Energy Resources) software. This new system consists of solar photovoltaics (PVs), batteries, an inverter, and a 100 kW DG. The results showed a clear difference between the baseline DG-only system and the hybrid system regarding energy cost and carbon emissions. The energy price for the HRES is $0.107/kWh, and carbon dioxide emissions are reduced to 27,378 kg/yr from 184,917 kg/yr for the DG-only system. In addition, simulations and comparisons for an alternative HRES with a 60 kW DG were conducted. Based on the simulation results, the energy price was $0.091 instead of $0.19, and carbon dioxide (CO2) emissions were 15,847 kg/yr instead of 115,090 kg/yr. It was concluded that using hybrid renewable energy systems to power the irrigation of remote areas successfully reduced the energy cost, fuel consumption, emissions, and overall cost. The HOMER program makes an accurate comparison over extended periods between the four strategies (load following, cycle charging, combined dispatch, and predictive dispatch) and selects the optimal system based on the cost, emissions, fuel consumption, and percentage of renewable energy from the system.

One of the essential challenges of tandem solar cells is designing and adjusting the current-matched tandem structures with high efficiency and stability. Nitride-based wide band gap semiconductors, owing to their high stability and high resistance against the cosmic rays, are appropriate elements to apply as the top cell of tandem solar cells. On the other hand, the organic-inorganic hybrid perovskites are emerging materials with exclusive electronic properties such as tunable band gap, low cost, simple manufacturing process, and efficient charge transport properties, making them capable candidates to be used in tandem layered structures. The aim of this paper is to adjust and optimize the performance of a two-terminal tandem solar cell consisting of InxGa1-xN as the top cell and a FAPbIyBr3-y as the bottom cell. We have studied the effect of different practical parameters such as the indium molar of the top cell, iodine molar of the bottom cell, the thickness of each layer, threading dislocation density of InxGa1-xN, and surface texturing effect on the performance of the two-terminal tandem structure. Because of the prominence of current matching problem in two-terminal tandem structures, we have determined the optimum situation for maximum light harvesting along with the minimum value of current matching factor. In the optimum situation, the current matching factor of 0.15 mA/cm2 leads to the power conversion efficiency of 25.17% for the device.

Higher-order neutron fluxes (i.e., higher-order harmonics) are widely applied in perturbation theory and modal kinetics, and they are important for research on the physical characteristics of accelerator-driven subcritical reactors (ADSRs). This paper presents a computational scheme for reconstructing the neutron flux in the steady state according to the biorthogonal properties of the forward and adjoint neutron fluxes, which can be used to analyze how higher-order harmonics affect the steady-state neutron flux under λ - and prompt α -modes. Simulation results indicated that a modal synthesis method based on λ - and prompt α -modes can effectively reconstruct the steady-state neutron flux and core power in an ADSR with a power reconstruction accuracy of within 5%. The higher-order harmonics can be classified into three types according to their physical characteristics: the first type contributes significantly to the steady-state neutron flux, the second type contributes almost nothing to the steady-state neutron flux, and the third type contributes nothing to the steady-state neutron flux. The external neutron source contributes only to specific harmonic expansion orders, which are characterized by significant axial and radial symmetry for both the λ - and prompt α -modes.

Metal–organic frameworks (MOFs), as a new generation of intrinsically porous extended crystalline materials formed by coordination bonding between the organic ligands and metal ions or clusters, have attracted considerable interest in many applications owing to their high porosity, diverse structures, and controllable chemical structure. Recently, 2D transition-metal- (TM-) based MOFs have become a hot topic in this field because of their high aspect ratio derived from their large lateral size and small thickness, as well as the advantages of MOFs. Moreover, 2D TM-based MOFs can act as good precursors to construct heterostructures with high electrical conductivity and abundant active sites for a range of applications. This review comprehensively introduces the widely adopted synthesis strategies of 2D TM-based MOFs and their composites/derivatives. In addition, this paper summarizes and highlights the recent advances in energy conversion and storage, including the hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, CO2 reduction reaction, urea oxidation reaction, batteries, and supercapacitors. Finally, the challenges in developing these intriguing 2D layered materials and their composites/derivatives are examined, and the possible proposals for future directions to enhance the energy conversion and storage performance are reviewed.

The currently implemented indiscriminate subsidy policy in China is difficult to provide effective incentives for a firm’s investment behavior because it does not take into account the differences in resource characteristics. Thus, this paper puts forward a type of differentiated subsidy that considers resource conditions, consisting of a basic guaranteed subsidy and a variable incentive subsidy. First, from the perspective of the economic efficiency of enterprises, the amount of basic subsidy required by enterprises is calculated by the discounted cash flow method. Then, from the government’s perspective, a variable incentive subsidy amount is calculated by establishing an expectation benefit maximization function using principal-agent theory. The case application shows that due to the superior resources and development conditions of the Fuling block, it requires a significantly smaller subsidy amount than the Weiyuan block which verifies the need to consider regional differences in the design of shale gas development subsidies.

Once-through molten salt reactors have the advantages of easy availability of fuel, nuclear nonproliferation, and low technical difficulty. It is expected to be the earliest commercial molten salt reactor fuel cycle mode. The current research on the neutronic performance of the once-through fuel cycle mainly focuses on the thermal spectrum range, and broader spectrum research needs to be carried out further. In particular, the fuel utilization characteristics of molten salt fast reactors in once-through fuel cycle are not yet clear. In addition, small modular design is one of the trends of molten salt reactor development, and small modular design under thermal and fast spectrum has different advantages and needs further comparative analysis. In this paper, the single lattice, bare reactor, and core with reflector models are used to compare the neutronic performance in once-through fuel cycle from thermal to fast spectrum, which includes burnup, variation of fuel salt composition and volume with burnup, temperature reactivity coefficient, neutron irradiation lifetime, and small modular size. The results show that the burnup increases first, then decreases, and then increases again with the increase of the average lethargy causing fission (EALF). For small-scale molten salt reactors, the difference in fuel utilization under thermal and fast spectra is small. However, for large-scale reactors, the burnup under fast spectrum is significantly higher than that under thermal spectrum. When the EALF is large enough, and the neutron loss ratio is small enough, the breed-and-burn (BNB) fuel cycle mode can be realized, and then, the fuel utilization will be significantly improved. In the fast spectrum reactors, the HN and volume of molten salt change violently because of the longer burnup period. Based on the infinite single lattice model, the temperature reactivity coefficient increases first, then decreases, and increases again with EALF. It is negative over the entire energy spectrum. In addition, the small modular designs are discussed by the bare reactor model, and the neutron irradiation lifetime of the materials is analyzed by the core model with a reflector.

The battery pack performance and expected lifespan are crucial in electric vehicle applications. Balancing the charge on a battery pack connected in series and parallel is crucial due to manufacturing discrepancies and distinct performance of each cell in a standard battery pack. In this paper, a switched-resistor passive balancing-based method is proposed for balancing cells in a battery management system (BMS). The value of the available voltage at the battery cell terminals is balanced using resistors in an electrical circuit, and the excess voltage is eliminated. The cell balancing outcome demonstrates that the electrical circuit can maintain an even voltage across each cell. The procedure of balancing involves individually adjusting each cell’s level of charge. Passive balancing releases energy as heat by draining charge from cells that have too much charge. A passive cell balancer is a cost-effective solution and easy to install, but due to thermal loss from a resistor, it has a low energy efficiency for cell balancing and necessitates a lengthy balancing process. This passive cell balancer is an effective and reliable method for low-power devices and portable applications such as electrical vehicles. The power limits during charging and discharging are estimated using the bisection method.

The distribution of hydrogen and how this can be widely achieved plays a significant role in the transition from fossil fuels to clean fuel. Using the existing natural gas pipeline network can be a suitable option for countries with extensive pipeline infrastructure. This study is aimed at investigating the compatibility of the existing polyethylene (PE) distribution pipe network with hydrogen. This paper utilises the cyclic pennsylvania edge notch test (CPENT) method together with material property measurements including melting point, crystallinity, and oxidative induction time (OIT) to understand the impact of exposing two different PE pipe resins to hydrogen at different exposure times. It was found that exposure to hydrogen has a noticeable impact time to failure, while the bulk crystallinity, OIT, and melting point showed little change. A change in crystal orientation was observed, and this likely accounts for the difference in time to failure when exposed to hydrogen.

In this paper, a new hybrid multiobjective algorithm, namely, the modified bald eagle search Algorithm (MBES), integrated with the grasshopper optimization algorithm, is proposed to solve the unit commitment (UC) problem. We consider a standard 10-unit power system with two wind farms, two photovoltaic farms, and flexible loads for optimization purposes. The UC problem is tackled under uncertainties related to demand and renewable generation capacities. To account for these uncertainties, probability density functions (PDFs) are assigned to the sources of uncertainty, and Monte Carlo simulation (MCS) is employed to select several scenarios with specific probability coefficients. Additionally, two innovative objective functions based on operation cost and emissions are introduced, with each scenario weighted based on its occurrence probability. To assess the performance of the proposed MOGOA-MBES algorithm, simulations are conducted across three scenarios with varying conditions, and the results are compared against those obtained from several multiobjective algorithms. Our findings, supported by optimization results and the S-metric index, demonstrate that the proposed MOGOA-MBES algorithm outperforms other algorithms in terms of reducing operation cost and emissions. Furthermore, the simulation results reveal that uncertainties lead to an increase in cost and emissions, whereas the inclusion of flexible loads and their participation in the UC program can effectively mitigate cost and emission levels.

This paper presents an energy management peer-to-peer (P2P) and peer-to-grid (P2G) trading strategy for power sharing between prosumers with grid-connected photovoltaic/wind turbine/battery storage systems. The optimal results are obtained using the “Fmincon” solver due to its ability to solve large-scale optimization problems. Two prosumers sectors are considered in this study that is the residential and commercial. The results show that for the residential consumer side, the load is satisfied by the energy from the grid, its own renewable energy system (RES), and the energy from the commercial consumer side RES during the first off-peak period (00:00 AM to 05:00 AM). During the peak pricing period (17:00 PM to 22:00 PM), the load is satisfied by the residential consumer’s battery and the commercial consumer’s battery, and no energy is bought from the grid due to the high price. Furthermore, 37.69% and 39.40% cost savings for residential and commercial, respectively, and a reduction of the bill by $42.26/day and $42.97154/day, respectively, are recorded. Significant grid energy savings for the developed P2P/P2G trading for prosumers and maximum daily cost savings are achieved compared to existing cases such as P2G only and P2P only. Finally, the residential consumer can achieve a greenhouse gases (GHG) emissions reduction of 141.88 kgCO2-eq and 176.99 kgCO2-eq net savings, while the commercial consumer can achieve 122.34 kgCO2-eq GHG emissions mitigation and 196.53 kgCO2-eq net savings.

Nowadays, integration of renewable sources into the local distribution system and the nonlinear behavior of advanced power electronic equipment have made a large impact on the power quality (PQ). The unified power quality conditioner (UPQC) is a multifunctional FACTS device, which is a combination of both shunt active filter and series active filters via a common DC link. Presently, the artificial intelligence is playing a vital role in the development of the intelligent control methods. Traditional training methods of artificial neural network (ANN) like back propagation and Levenberg-Marquardt may get stuck in local optimal solution which leads to the invention of ANN trained optimally by metaheuristic algorithms. This paper develops a firefly algorithm-trained ANN (FF-ANNC) controller for the shunt active filter and proportional integral controller (PI-C) for the series active filter of the UPQC integrated with the solar energy system and battery energy storage via boost converter (B-C) and buck boost converters (B-B-C). The main aim of the proposed FF-ANNC is to reduce the mean square error (MSE) thereby achieving the constant DC link capacitor voltage (DLCV) during load and irradiation variations, reduction of imperfections in current waveforms, improvement in power factor (PF), and mitigation of sag, swell, disturbances, and unbalances in the grid voltage. The working of developed FF-ANNC was tested on five test studies with different types of loads and source voltage balancing/unbalancing conditions. However, to demonstrate supremacy of the suggested FF-ANNC, a comparative study with the training methods like genetic algorithm (GA) and ant colony optimization (AC-O) and also with other methods that exist in literature like PI-C, fuzzy logic controller (FL-C), and artificial neuro fuzzy interface system (ANFI-S) was conducted. The proposed method reduces the total harmonic distortion to 2.39%, 2.32%, 2.27%, 2.45%, and 2.66% which are lower than the existing methods that are available in literature. The FF-ANNC shows an excellent performance in reducing voltage fluctuations and total harmonic distortion (THD) successfully and thereby improving PF.

Accurate determination of photovoltaic (PV) parameters holds immense significance for ensuring the reliability of solar system operations, uninterrupted power supply for load consumption, and efficient control and management of energy sources. I-V curves transform parameter extraction into a nonlinear optimization problem supported by the I-V data points in the PV model to characterize the PV model macroscopically. Therefore, this paper proposes a novel parameter extraction model using the Q-learning-based multistrategy improved shuffled frog leading algorithm (CRNSFLA). During the evaluation process of the proposed algorithm, the colony predation algorithm (CPA) is utilized to expand the search range of the worst individual, which is no longer confined to the line segment range between the current and best values. In the later stage of evaluation, the optimal individual serves as the starting point and is applied to the Nelder-Mead simplex (NMS) for forming a simplex to mine higher-quality solutions. Besides, the simplest reinforcement Q-learning allows for a reasonable switch between these two mechanisms. A reasonable balance between exploration and exploitation trends is ensured while making full use of the advantages of both according to the reward and punishment mechanisms. The comprehensive test results under various optimization functions, different PV models, and environmental conditions demonstrate that the proposed algorithm is more advantageous than existing algorithms for parameter extraction problems. Specifically, CRNSFLA had RMSEs of 9.8602E-04, 9.8248E-04, and 9.8248E-04 in the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM), respectively. Moreover, compared with the original shuffled frog leading algorithm, the CRNSFLA showed significant improvements in 62% of the optimization functions. Therefore, CRNSFLA can be considered an effective tool for solar cell parameter extractions.

The supercritical carbon dioxide (S-CO2) power cycle is widely applicable to the utilization of multigrade heat sources due to its high efficiency and compact structure, in which the turbine is critical to the performance of the system. In this study, the operation pattern and output performance of an S-CO2 axial turbine with a design power scale of 10 kW power are investigated by experiments and numerical simulations. The loss mechanisms and distributions are analyzed using numerical simulations and loss models. The results show that the turbine output power and efficiency are at 9.66 kW and 36.6% at the designed condition and the output performance depends on the pressure ratio and rational speed. In addition, the flow field distribution reveals that the flow separation phenomenon occurs leading to loss, with the tip clearance loss being the most significant. Furthermore, the loss model validated in this paper is proven to be competent enough to fulfill the prediction of the loss distribution under different operational conditions, which could be very promising in the design of an S-CO2 axial turbine.

Microgrids with distributed generation (DG) are rapidly coming into distribution networks to supply load demand in order to preserve ecological balance and reduce greenhouse gas emissions. Power electronic advancements are making renewables dispatchable to loads while turning passive networks to active with bidirectional power flow. The IEEE-1547-2018 regulations enforced certain standards on microgrids, including the ability to detect unintended failures, island the microgrid in less than 2 seconds, and feed connected loads while maintaining voltage, frequency, and power quality. There are numerous islanding techniques accessible, including passive, active, hybrid, and communication techniques. The active techniques degrade power quality due to injection of deviations, passive type leaves larger nondetection zone (NDZ), and communication type are costly. The hybrid type combines both passive and active methods. To get away from all these issues, a passive technique is formulated in this paper, which measures the differential phase angle of voltage and current at DG output, to detect islanding. This approach reliably identifies islanding in 20 ms at nearly zero NDZ. This approach is also stable during transient conditions such as load switching and throwing off. There is also no power quality issue because there are no injections during testing. This method is tested in MATLAB/Simulink and evaluated using the differential frequency technique to get performance indices in accordance with UL-1741 testing standards.

Wind turbines (WTs) are a desirable alternative to traditional nonrenewable power resources as a result of recent environmental concerns. Some of them are fixed speed wind generator (FSWG) and have been integrated to power system by squirrel cage induction generator (SCIG). Induction machine absorbs reactive power during all operating conditions, especially at fault condition may result in severe voltage drop which can lead to generator outage. This outage disconnects a significant amount of active power and consequently leads to frequency instability. In order to prevent induction generator (IG) outages in short circuit failures, this paper investigates thyristor-controlled series capacitor (TCSC) device as a candidate solution. TCSC compensates the IG terminal voltage drop by adjusting transmission line impedance at fault condition. In the proposed method, a metaheuristic technique means that shuffled frog leaping algorithm (SFLA) has been utilized to optimize the TCSC controller gains. The proposed scheme can be applied for both SCIGs and wound rotor induction generators (WRIGs) which is another advantage of this method. Single-machine infinite bus system is considered as case study, and various operating conditions and disturbances have been considered to verify the effectiveness of the proposed method.

Nowadays, consumption of different energy carriers is increasing due to the division of community activities in various sectors such as residential, commercial, and industrial. The energy hub concept is used to meet the demands of different energy carriers in these sections. In this paper, a new method for energy management of a microgrid is presented in an intelligent network based on three types of commercial, residential, and industrial energy hubs. In the energy hubs, a wide range of components, including renewable and nonrenewable generation units, converters, storage devices, intelligent parking lots consisting of electric vehicles, P2G units, and cogeneration units, are used to supply electrical, heating, cooling, and natural gas energies. Some parameters like renewable generations, energy demands, and arriving and departure time of electric vehicles are considered to be uncertain, and a relevant method is applied to provide a near-realistic profile for them. In addition, a demand response paradigm has also been proposed for three types of electrical, heating, and cooling demands at the hub output side. Therefore, in this method, a mixed integer linear programming model is proposed based on benefit and reduction of emission caused by the activity of gas-burning units for short-term planning and obtaining an optimal solution for generation and sending loads in a distribution network containing energy hubs. To evaluate the performance of the proposed modeling and structure, the presented approach is applied on a modified 33-bus IEEE test network. According to the results of the energy management model, it is possible to significantly increase benefits and also obtain a smoother consumption pattern in consumption with financial incentives.

While energy production is highly dependent on fossil fuels, which consider the main source of global warming, biofuels would play a significant impact in diminishing such warming. In this paper, biooils were extracted from inedible seeds (Jatropha and Castor) using different continuous devices (solvents, screw presses, and hydraulic press-machines), aiming to achieve the highest oil’s yield of improved extraction properties at reduced time and energy. A wide range of engine speeds of 35, 60, 85, 110, and 135 rpm and preheating temperatures of 100, 125, 150, 175, 200, and 250°C were extensively studied to find their impact on the extraction properties. Results proved the ability of the screw press machine to extract the highest biooil yields from Jatropha and castor seeds. The optimum yield of Jatropha and castor were achieved at an extraction temperature range of 150-175°C at a motor speed of 135 rpm and a temperature range of 200-250°C at a motor speed of 35 rpm, respectively. Noteworthy, the yield of extracted castor oil is potentially solidified at low temperatures

The flowing electrolyte in a zinc-air fuel cell (ZAFC) can reduce the problem of incomplete reaction caused by the concentration gradient of the electrolyte. In this study, we propose a self-developed ZAFC with a flowing electrolyte system and optimize control parameters. The anode of the ZAFC used Zn particles through which the electrolyte penetrates and combined with a negative pressure pump to allow the potassium hydroxide (KOH) electrolyte to have an effective chemical reaction with the Zn particles during the discharge process. However, the flow rate of the electrolyte is required to match other control parameters, such as operating temperature, KOH concentration, and cell size, to effectively improve the efficiency of ZAFC. Therefore, the Taguchi method is adopted to obtain the optimal reaction parameters and the key factors affecting the discharge efficiency of the cell. The best operation condition is the temperature at 50°C, the KOH concentration of 30% wt, and the electrolyte flow rate of 150ml/min; then, the maximum power obtained from the experimental results is 13.8 W, the maximum current density reaches 699.721 mA/cm2, and the maximum power density is 395.037 mW/cm2. This paper provides valuable insights for the upgrade of ZAFC for future practical applications.

In the passive direct methanol fuel cell (DMFC) operation, the catalyst plays an important role in electrical energy production. Efficient catalyst support increases the power output and simultaneously decreases irreversible losses in the fuel cell (FC). Effective utilization of methanol at the anode end reduces methanol crossover by selecting good anode catalyst supports. In the present study, two types of anode catalyst support of carbon and carbon black are used. The two types of anode catalysts are Pt-Ru/C+ Pt-Ru/black and Pt-Ru/C along with a 2 mm thick liquid electrolyte (LE) layer placed in the middle of the two half MEAs. The LE layer is made of piled hydrophilic filter papers, which are soaked in a solution of 1 M H2SO4 concentration. The methanol concentration is varied from 1 M to 12 M and from 1 M to 5 M with and without LE-inserted FC, respectively. The passive DMFC with Pt-Ru/C+ Pt-Ru/black anode catalyst and the LE layer exhibit the best performance, producing an MPD of 5.328 mWcm-2 at a 5 M methanol concentration. The MPD produced by the anode catalyst of Pt-Ru/C+ Pt-Ru/black and Pt-Ru/C along with the 2 mm thickness LE layer incorporated fuel cell is 75.26% higher than conventional-based FC of anode catalyst of single-layer catalyst of Pt-Ru/C.

FACTS tools in modern power systems provide a vital solution to the problems of voltage deviation and fault ride-through (FRT) capability in electrical power systems especially during the integration of wind power. Many distinct journals highlight that the wind-driven squirrel cage generator (SCIG) still accounts for around 15% of operating wind generators so far. To enhance voltage stability and FRT capacity, this paper recommends a cost-effective static VAR compensator (SVC) which has a size rating of six MVAR, and this improves the efficiency of the electrical power system. Different events are considered in this study such as high turbulent wind speed, low turbulent wind speed, unsymmetrical faults, and symmetrical faults to validate the suggested option. Moreover, the suggested solution is compared with the static synchronous compensator (STATCOM) and a fixed capacitor to ensure that during the studied wind speed profiles and faults, voltage stability, reactive power consumption, and FRT capability are realized. An overall comparison among them is performed under all studied scenarios to summarize their benefits and impacts. The simulated results show the effectiveness and superiority of SVC in improving the operation of an integrated wind system based on a grid-linked SCIG and the performance of the power system. The modeling of SCIG, SVC, and STATCOM is designed by MATLAB/Simulink toolbox.

As the temperature range of the cooling machine affects the cooling performance, parametric optimization of moderate temperature Stirling refrigerator has valuable concept. In this paper, a nonideal modified simple numerical model was used to investigate the effects of parameters and to propose optimized Stirling refrigerator design for moderate temperature applications. The analysis was conducted through simulating the numerical model using MATLAB code for beta configuration FEMTO-60 prototype operating as a refrigerator. The effects of shuttle heat loss and mass leakage on the cooling performance of domestic Stirling refrigerator have been studied. Moreover, the effects of operating parameters (operating frequency and charging pressure) and design parameters (phase angle, regenerator length and porosity, displacer height, swept volume ratio, and piston diameter to stroke ratio) have been investigated. A cooling power of 625 W and COP of 1.3 have been found at a charging pressure of 22.5 bar and an operating frequency of 6 Hz using the optimized design parameters. The optimized parameters (operating and specified design parameters) could deliver from 48% to 60% more cold production and from 30% to 40% more COP than the existing design with a safe working condition.

This paper estimates the maximum integration level of residential rooftop solar photovoltaic (PV) capacity within the power network of the Duke Energy Progress (DEP) and Duke Energy Carolinas (DEC) under two scenarios embodying different assumptions about the flexibility of nuclear power plant (NPP) operations. A mixed-integer optimization model was constructed and simulated to find out the maximum solar penetration level under each scenario and to calculate the expected total system’s electricity generation costs, energy mix, atmospheric emission reductions, and emission abatement costs. Analysis reveals that improving NPP operation maneuverability would increase the maximum solar PV penetration level in the DEP and DEC power networks by 39%, from 8.9% to 12.4% of the total system’s electricity generation. Consequently, it would further improve the electricity generations’ unit costs and CO2 emission reductions by 3% and 8% points, respectively. On the other hand, increasing the solar PV penetration limit under high flexible NPP operation scenario leads to increase in the CO2 emission abatement costs by 8% points. The results of the study indicate that the flexibility of existing power system resources may present a barrier for a large uptake of solar energy.