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Abstract This study investigates the viability of hybrid photovoltaic (PV), wind, and fuel cell (FC) systems for on‐grid and off‐grid operations for the Ashrayan‐3 housing project in Bangladesh, with an increased focus on sustainable energy solutions. Motivated by the issue of the delivery of proper and sustainable energy services to remote locations, we conducted an extensive analysis of load demand and found that an average daily demand of 46,176.65 kWh exists, with a peak load of 4852.8 kW. In this research, the HOMER software has been used to make a simulation of five different hybrid system configurations with differing mixes of renewable technologies. From the analyses, the systems based 100% on renewable resources suffer more initial capital costs, with a total net present cost increase of up to 20%, in comparison to conventional systems. On the other hand, the systems give much lower operational costs and cost of energies (COEs) of a minimum of $0.0253/kWh, reported from the on‐grid PV‐based system. On the other hand, the off‐grid PV–FC–wind turbine system showed a COE of $0.286/kWh, along with a decrease in CO2 emissions by about 15,000 kg/year, showing a 30% decrease, compared with on‐grid systems. The results form a basis for the conclusion that such hybrid renewable energy systems are both economically and environmentally feasible. They can reduce COEs by up to 70% in off‐grid systems. This proves that the quality of life and energy security in developing regions will be highly increased, supporting the goals of sustainable development.

A solar air heater duct (SAH-duct) SAH-duct has low thermal performance due to the low heat transfer coefficient of air which is the working fluid. Proper mixing of relatively hot and cold air with respect to the hot absorber plate of the SAH-duct can enhance heat transfer. It can be achieved by placing suitable hindrances in the path of airflow like detached ribs. This study presents the numerical investigation of an asymmetrically heated 2D rectangular SAH-duct with in-line arranged circular detached ribs. In the geometrical configurations of the detached ribs; blockage ratio d/H, clearance ratio c/d, and relative longitudinal pitch ratio P/d are varied as 0.04–0.48, 0.1–1.2, and 05–50 respectively at Reynolds number Re 3000–15000 to study their effect on the thermal-hydraulic performance of the SAH-duct. Local and space-averaged thermal-fluid properties like heat transfer coefficient, and friction factor, Nusselt number, pressure coefficient, etc. are discussed through the graphical illustrations and contour plots. The various dimensions of the ribs are varied to find the optimum geometrical configuration of the detached ribs.

Water scarcity is a global challenge, underscoring the importance of efficient water resource management. Solar stills offer a cost-effective method to convert brackish water into potable water but face productivity limitations. This study aims to enhance solar still productivity through modifications using different fin materials and water depth. Computational Fluid Dynamics (CFD) simulations were employed to evaluate thermal performance across four scenarios: copper and aluminum fins at water depths of 20 mm and 40 mm. Key parameters including temperature distributions, friction volume, and fluid velocity were analyzed for each configuration (MSS-I to MSS-IV). Energy and exergy efficiencies were also assessed. MSS-III, utilizing copper fins at a 20 mm depth, demonstrated the highest daily productivity (8.33 liters) compared to MSS-IV (8.02 liters), MSS-I (7.81 liters), and MSS-II (6.71 liters). Energy efficiencies were highest for MSS-III (60.10%), followed by MSS-IV (57.41%), MSS-I (55.22%), and MSS-II (52.18%). MSS-III also exhibited the highest exergy efficiency (21.50%), with MSS-I (17.15%), MSS-IV (16.43%), and MSS-II (14.12%) following. The study underscores significant improvements in thermal and energy efficiency achieved through specific design modifications of solar stills. MSS-III’s higher performance, attributed to the use of copper fins and optimized depth, highlights the critical role of material selection and structural design in enhancing solar still productivity. These findings have important implications for sustainable water resource management, emphasizing the potential of optimized solar still designs to address water scarcity challenges.

The air conditioning and refrigeration applications use a significant portion of the electrical energy. This research analyses a performance of refrigeration system, which comprises on different configurations of solar based organic Rankine cycle (ORC) and vapor compression refrigeration (VCR) cycles requiring low evaporation temperatures. In this research, the dry natural hydrocarbons such as n-Decane, n-Dodecane and Toluene have been chosen to serve as the working fluids in ORC. Whereas in VCR cycle natural hydrocarbons such as Ethane, Propane, isobutane, isopentane and isohexane have been used because traditional working fluids have a negative environmental impact due to high values of ozone depletion and global warming potential. The simulated results showed that the facility can be operated efficiently with the use solar thermal energy resources within the temperature range 90 to 315ºC and decreasing the need for conventional fossil fuel resources. It was also revealed that the highest efficiency was achieved by n-Dodecane in regenerative ORC, which is 35.34 % at the evaporation temperature of 315ºC and the highest overall coefficient of performance (COPoverall) in regenerative ORCCRS facility was achieved by n-Dodecane in ORC and isopentane in refrigeration cycle which is 1.017 while in case of Simple ORC–VCR facility the highest COPoverall was achieved by Toluene in ORC and isopentane in refrigeration cycle, which is 0.7174 at the evaporation temperature 315ºC.

A turbulence model study was performed to analyze the flow around the Tubercle Leading Edge (TLE) wing. Five turbulence models were selected to evaluate aerodynamic force coefficients and flow mechanism by comparing with existing literature results. The selected models are realizable k-ε, k-ω Shear Stress Transport (SST), (γ−Reθ) SST model, Transition k-kl-ω model and Stress- ω Reynolds Stress Model (RSM). For that purpose, the TLE wing model was developed by using the NACA0021 airfoil profile. The wing model is designed with tubercle wavelength of 0.11c and amplitude of 0.03c. Numerical simulation was performed at chord-based Reynolds number of Rec = 120,000. The Computational Fluid Dynamic (CFD) simulation reveals that among the selected turbulence models, Stress- ω RSM estimated aerodynamic forces (i.e. lift and drag) coefficients closest to that of the experimental values followed by realizable k-ε, (γ−Reθ) SST model, k-ω SST model and k-kl-ω model. However, at a higher angle of attacks i.e. at 16° & 20° k-ω SST model predicted closest drag and lift coefficient to that of the experimental values. Additionally, the critical observation of pressure contour confirmed that at the lower angle of attack Stress- ω RSM predicted strong Leading Edge (LE) suction followed by realizable k-ε, (γ−Reθ)SST model, k-ω SST model and k-kl-ω model. Thus, the superiority of Stress- ω RSM in predicting the aerodynamic force coefficients is shown by the flow behavior. In addition to this pressure contours also confirmed that k-kl-ω model failed to predict tubercled wing aerodynamic performance. At higher angles of attacks k-ω SST model estimated aerodynamic force coefficients closest to that of the experimental values, thus k-ω SST model is used at 16° & 20° AoAs. The observed streamline behavior for different turbulence models showed that the Stress- ω RSM model and k-kl-ω model failed to model flow behavior at higher AoAs, whereas k-ω SST model is a better approach to model separated flows that experience strong flow recirculation zone.

Wind energy penetration has considerably increased in the recent past. However, wind turbines are often prone to various faults which may lead to failures causing huge production and economic losses with increased downtime. To reduce this production and economic loss. It is therefore clear that early detection of these failures can be achieved through an appropriate condition monitoring approach. Various approaches are reported for predicting the condition of wind turbines. However, deploying a costly condition monitoring system with additional data accusation devices poses a challenge for windfarm owners. To address this challenge this study employing Preferred Reporting Item for Systematic Literature Review and Meta Analysis (PRISMA) provides a detailed review of various approaches used for the wind turbine condition monitoring. The key objective of this study is to find out the most frequently used and reliable method of wind turbine condition monitoring, focusing particularly on the SCADA-based approach due to its practical advantages and widespread adoption in the industry. Additionally, this review considers the distinctive concept of machine learning model building which includes data input and its processing, feature selection, model building and its evaluation to analyze the research issues. The review findings concluded that amongst various condition monitoring techniques, SCADA based data driven approach is most popular as it does not require additional sensors, blade mount cameras, unmanned arial vehicles and a separate data accusation unit. Nevertheless, condition monitoring results based on SCADA approach to provide varying predications for differently located wind farms which is a pertinent knowledge gap. This review study provides some detailed insight into various condition monitoring approaches of wind turbines and recommendation to consider any of these based on available resources.

The challenging task of insulator monitoring through aerial images is addressed in high voltage transmission network and highlights the limitations of traditional human patrolling with emphasize on utilization of unmanned aerial vehicles UAVs utilizing machine learning algorithms. This research has been accomplished by creating indigenous dataset of 500kV transmission network of National Transmission and Despatch Center Limited (NTDCL). 608 original images were captured in diverse lighting and topographical conditions which were then augmented to 3618 images. To improve the detection accuracy of YOLOv8s algorithm in aerial images, HSV and CLAHE image enhancement techniques were employed to improve the visual feature of insulator with suppressed noise. YOLOv8s algorithm with image enhancement has improved detection accuracy from 88% to 95% demonstrating the effectiveness of integrating image enhancement technique for insulator monitoring, offering promising improvement in maintenance practices and operational reliability of transmission lines.

Energy drives the growth, transformation, and economic development of every nation. The vitality of human existence and progress hinges on the accessibility of both energy and water resources. As freshwater resources are diminishing, therefore, desalination needs have increased. In solar membrane distillation systems, the key challenge is maintaining the intake water temperature in the membrane distillation system with fluctuating solar radiation intensity which affects the distillate water quantity and quality. The objective of this study is to enhance and optimize a mathematical model for analyzing a cutting-edge solar-integrated PV/T-DCMD system. In this innovative integration, the direct contact membrane distillation intake water temperature is derived from the photovoltaic thermal output. The integration of direct contact membrane distillation with photovoltaic thermal systems represents a cost-effective and technologically advantageous concept. As the water temperature increases, there is a notable improvement in the evaporation efficiency of PV/T-DCMD systems, with an increase from 35.08% to 42.01%. Additionally, there is a reduction in specific thermal energy consumption, decreasing from 1,192 to 1,386 kWh/m3 as a consequence of the elevated feed water temperature.

A meteorological drought study is performed using monthly time scale data from three separate locations in southern Sindh, Pakistan. Rainfall and temperature have been used to identify the drought. These data were transformed into drought indices known as the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI), which were derived using (the Hargreaves equation). In this study, two indices are compared for three separate meteorological stations Chhor, Mithi, and Badin where most socioeconomic livelihoods depend heavily on water. The SPEI is produced through a simple water balance combining precipitation and temperature, in distinction to the SPI, it just considers precipitation. In conclusion, our study showed that both indices were capable of detecting droughts that fluctuated in time across the reference period of 2004–2021. SPI and SPEI's direction of change was similar, however the impact on the drought condition varied. SPEI discovered more droughts with longer durations and greater with 13 moderate droughts at SPEI-3 for Chhor and Badin Station while Mithi indicated 8 moderate droughts during 2004-2021 and SPI-3 indicated 4 moderates for Chhor, Mithi and Badin indicated 6 moderate drought. Conversely, SPEI discovered more moderate-level droughts than SPI, however they were of shorter length and less frequent occurrence than the severe to moderate droughts. The findings imply that drought characteristics are significantly influenced by temperature variability.

Global energy trends are experiencing a profound transformation, and the future of transportation will boost sustainable development by controlling energy production and consumption while limiting vehicle emissions. Hence, Electric Vehicles (EVs) can substantially influence energy consumption trends by addressing potential environmental hazards. In the coming decades, Artificial Intelligence (AI) based systems will play a crucial role in future EVs' overall energy management systems. Advanced electric vehicle technology and intelligent modules will lead the automotive powertrain architecture. Numerous barriers, such as government support in certain regions, user compatibility, vehicle limitation, battery technology, and charging infrastructure, limit electric vehicle expansion. Therefore, the current state and emerging trends in this area are a matter of concern for increasing the expansion of electric vehicles. This study presents the existing charging technologies and compatible standards that may assist in its adaptability. It also examines the applications of artificial intelligence in electric vehicle development to render a collectively smarter package for EVs. The current level of development, in accordance with the paper's purpose, is to deduce the adaptability of EVs to certain obstacles. Hence, the categorization of methodologies and standards may be investigated and improved by scholars at a later time.

This review is inspired by the increasing shortage of fresh water in areas of the world, and is written in response to the expanding demand for sustainable technologies due to the prevailing crisis of depleting natural water resources. It focuses on comprehending different solar energy-based technologies. Since the increasing population has resulted in the rising demand for freshwater, desalination installation volume is rapidly increasing globally. Conventional ways of desalination technologies involve the use of fossil fuels to extract thermal energy which imparts adverse impacts on the environment. To lessen the carbon footprint left by energy-intensive desalination processes, the emphasis has shifted to using renewable energy sources to drive desalination systems. The growing interest in combining solar energy with desalination with an emphasis on increasing energy efficiency has been sparked by the rapid advancements in solar energy technology, particularly solar thermal. This review paper aims to reflect various developments in solar thermal desalination technologies and presents prospects of solar energy-based desalination techniques. This paper reviews direct and indirect desalination techniques coupled with solar energy, and goes on to explain recent trends in technologies. This review also summarizes the emerging trends in the field of solar thermal desalination technologies. The use of nanoparticles and photo-thermal materials for localized heating in solar desalination systems has decreased energy consumption and enhanced the efficiency of the system. Solar power combined with emerging processes like membrane distillation (MD) has also a recent resurgence.

This research investigates the hydro dynamical features of steady rectilinear torsional flow of pseudoplastic fluid between two infinite coaxial cylinders. Rotation of both cylinders about common axis yields the motion in the fluid. Three cases has been considered i.e. (i) when the inner cylinder rotates and the outer cylinder is at rest, (ii) when the outer cylinder is rotated, and inner is at rest and (iii) when the inner is rotated, and outer is removed. Governing equation describing the flow of pseudoplastic fluid has been derived and analytical solution are obtained by employing Langlois Recursive Approach (Langlois, 1963). Analytical solution for velocity profile of rotating cylinders and its special cases are calculated up to third order. The corresponding solution for viscous fluid is obtained as a limiting case. The impact of various evolving parameter on the flow, i.e. angular velocity (ω), radius ratio (R), gap width (r) and non-Newtonian parameter (β) are examined and illustrated graphically. The analytical approximate solutions acquired are in accordance with the existing literature if the non-Newtonian behaviors vanishes.

Abstract In Pakistan, around 58% of current electricity is generated from fossil fuels and only 2.4% is generated using renewable energy (RE) resources even though country is blessed with enormous RE potential. Among other RE resources, Pakistan's geographical location offers high solar energy potential, which implies that actual potential assessment should be undertaken. This study, as such, undertakes a comprehensive assessment of solar energy potential and prospects of solar photovoltaic (PV) systems for both off‐grid and grid‐connected systems. This study also estimates the future available capacity of rooftop and rural off‐grid solar PV capacity. Three different types of solar PV modules of the same size, that is, thin‐film, premium, and standard were modeled to compare energy outputs. NREL's System Advisor Model (SAM) is used to estimate the geographical and technical potential of solar PV considering updated data and geographical information. SAM results suggest that an average of 4.5 kWh/kWp/day is obtained from an installed capacity of 1 KWp. The logistic modeling equations are further used to forecast the solar PV penetration over a period until 2090. The research investigation concludes that 2.8 × 106 GWh of electricity can be generated annually in Pakistan. The estimated results prove that solar PV has the potential to meet the present as well as future energy needs of Pakistan.

As global warming is increasing due to conventional sources the government and the private sectors introduce policies to minimize it, renewable energy has been developed and deployed because of these strategies. Among the various renewable energy sources, wind energy is the fastest-growing and cleanest energy resource in the world. However, predicting wind power is not easy due to the nonlinearity in wind speed that eventually depends on weather conditions. To reduce these issues improved forecasting models have been used to get the correct results and improve the performance and stability of the power system and thereby its reliability and security.In this work, two models are used to predict the “Output of Wind Turbine” to improve the prediction accuracy of short-term wind power generation. The two models namely the Gated Recurrent Unit (GRU) from the deep learning model and Autoregressive Integrated Moving Average (ARIMA) from Statistical Learning. The data used in this research is collected from the wind power plant, Located in Jhimpir Pakistan. This study compares the accuracy metrics of deep learning models and statistical models to determine which model is the most effective for producing wind power.The results are obtained by using python programming in Jupyter Notebook software and the accuracy metrics of each algorithm are compared with each other as a result Gated recurrent unit (GRU) is the best model among others with the least possible errors and high accuracy. i.e., up to 0.047 root mean square error, 0.89 coefficient of metrics, and 0.03 mean absolute error. Hence, due to its advanced features, then other deep learning, and statistical models the Gated recurrent unit (GRU) Model is suitable for the prediction of wind turbine output power.

Abstract Pakistan has faced an electricity shortfall for over two decades despite various efforts taken at different levels. Though electricity supply in recent times has crossed the demand, the supply‐side stresses and deciding optimal power generation pathways have always been a challenge for policymakers and researchers. In this study using a LEAP energy model, following the sectoral electricity demand forecast, four supply‐side scenarios have been developed and analyzed for the study period 2017‐2055. In each scenario, referred to as Business as Usual (BAU), Renewable Energy Technologies (RET), Coal Power Penetration (CPP), and High‐Efficiency Low‐Emission (HELE) scenario, electricity generation, installed generation capacity, cost of production, and GHG emissions are estimated and compared for seeking long‐term optimal energy pathways for Pakistan. The study results reveal that for the end year (2055), RET is an environmentally sustainable scenario with an estimated electricity generation of 2421 TWh, which is enough to meet the electricity demand of 2374TWh. The GHG emissions under the RET scenario are estimated to be 857 million metric Tons, which are around 50% less than CPP and 40% less than the BAU scenario. However, the cost of generation is higher than BAU and CPP scenarios. The CPP scenario emerges to be cost‐competitive, however with the highest GHG emissions. This study thus suggests that convergence of RET with the CPP scenario could be an appealing option for Pakistan to meet increasing demand with energy security and environmental sustainability.

In this research, biodiesel fuel is produced using potassium hydroxide (KOH) as a catalyst and methanol as a co-solvent through the transesterification process. The analysis of particulate matter emissions from a compression ignition (CI) engine with pure diesel fuel (D100), biodiesel-mixed (B30), and clove oil (CL3000 ppm) was carried out. The findings obtained revealed 5.27% reduction of particulate matter in biodiesel-blended fuel, and furthermore, when clove was added to the biodiesel-blended fuel, 11.61% reduction of particulate matter was observed as compared to pure diesel. It may be due to the higher oxygen ratio present in clove oil. The engine was run for 100 h on each fuel sample, and the data were taken and tested per 25 h for elemental analysis. The results manifest that the metal concentration found different elements in lubricant oil, i.e., lead (51.84), (24.89%), and (23.255%); copper (47.41%), (28.71%), and (23.86%); nickel (37.88%), (32.32), and (29.78%); and cadmium (46.12), (29.87%), and (24.01%) in diesel, biodiesel-blended fuel, and biodiesel blended with clove oil, respectively. This study found better results for engine life, which shows the lower metal concentration in biodiesel-blended fuel and clove oil than diesel. Furthermore, the fuels investigated in this research can reduce the wear of engine parts because biodiesel and clove oil contain a high oxygen ratio. Moreover, it has been found that biodiesel and clove oil as an antioxidant have a positive impact on noise emission levels for the CI engine.

To meet electricity requirements and provide a long-term, sustainable existence, remote areas need to promote renewable projects. Most of the time, wind and solar power sources are selected as renewable energy technologies to help satisfy some of the power requirements. Alternative approaches should be employed, considering the inconsistent characteristics among those resources, to offer efficient and long-lasting responses. Electricity production needs to be conducted with the help of a wide range of energy sources to be productive and efficient. As a result, the current research concentrates on feasible analyses of interconnected hybrid energy systems for such remote residential electricity supply. To help a remote area’s establishment decide whether to adopt renewable electricity technology, this paper evaluates the techno-economic effectiveness of grid-connected and standalone integrated hybrid energy systems. The electricity requirements for the entire selected remote area were determined first. Furthermore, the National Aeronautics and Space Administration, a national renewable energy laboratory, was used to evaluate the possibilities of green energy supplies. A thorough survey was performed to determine which parts were needed to simulate the interconnected hybrid energy systems. Employing the HOMER program, we conducted a simulation, optimizations, and economic research. Considering the net present cost, cost of energy, and compensation time, an economic comparison was made between the evaluated integrated hybrid systems. The assessment reveals that perhaps the grid-connected hybrid energy system is the best option for reliably satisfying remote areas’ energy needs.

There is increased focus to harness renewable energy resources in the 21st century to contain climate change and attain energy security. In this context, wind energy is attaining a significant marketplace to meet the ever-increasing electricity demand. This study, as such, undertakes wind energy assessment and forecasts wind power market penetration in Pakistan considering three different scenarios for the period 2020–2050. The modeling approach of this study is based on the Levenberg–Marquardt algorithm (LMA) optimization method, which is used to estimate the parameters of the logistic model to improve forecasting precision. It is revealed that around 55, 64, and 73% of wind potential could be technically exploited under each of the three scenarios, respectively. The Certified Emission Reductions (CERs) for each scenario are also estimated. The anticipated annual abatement of GHG emissions and CERs earnings at 30% capacity utilization factor is found to be 158 million CERs by the year 2050. These results suggest that wind energy offers great potential to attain energy security, environmental stability, and sustainable development in Pakistan. This study would assist energy professionals, government, and stakeholders to undertake wind energy market assessment and devise appropriate energy management plans.

The doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) suffers from voltage and frequency fluctuations due to the stochastic nature of wind speed as well as nonlinear loads. Moreover, the high penetration of wind energy into the power grid is a challenge for its smooth operation. Hence, symmetrical faults are most intense, inflicting the stator winding to low voltage, disturbing the low-voltage ride-through (LVRT) functionality of a DFIG. The vector control strategy with proportional–integral (PI) controllers was used to control rotor-side converter (RSC) and grid-side converter (GSC) parameters. During a symmetrical fault, however, a series grid-side converter (SGSC) with a shunt injection transformer on the stator side was used to keep the rotor current at an acceptable level in accordance with grid code requirements (GCRs). For the validation of results, the proposed scheme of PI + SGSC is compared with PI and a combination of PI with Dynamic Impedance Fault Current Limiter (DIFCL). The MATLAB simulation results demonstrate that the proposed scheme provides superior performance by providing 77.6% and 20.61% improved performance in rotor current compared to that of PI and PI + DIFCL control schemes for improving the LVRT performance of DFIG.

Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

The integration of distributed generation (DG) into a power distribution network allows the establishment of a microgrid (MG) system when the main grid experiences a malfunction or is undergoing maintenance. In this case, the power-generating capacity of distributed generators may be less than the load demand. This study presents a strategy for the effective utilization of deployed active and reactive power sources under power mismatch conditions in the islanded distribution networks. Initially, the DGs’ and capacitors’ optimal placement and capacity were identified using the Jaya algorithm (JA) with the aim to reduce power losses in the grid-connected mode. Later, the DG and capacitor combination’s optimal power factor was determined to withstand the islanded distribution network’s highest possible power demand in the event of a power mismatch. To assess the optimal value of the DG–capacitor pair’s operating power factor (pfsource) for the islanded operation, an analytical approach has been proposed that determines the best trade-off between power losses and the under-utilization of accessible generation. The test results on 33-bus and 69-bus IEEE distribution networks demonstrate that holding the islanded network’s load power factor (pfload) equal to pfsource during the power imbalance conditions allows the installed distributed sources to effectively operate at full capacity. As expected, the proposed strategy will assist the utility companies in designing efficient energy management or load shedding schemes to effectively cope with the power mismatch conditions.

In this study, modeling and thermodynamic analysis of the combined double flash geothermal cycle generation was conducted using zeotropic fluid as the working fluid in the Organic Rankine Cycle (ORC). The analysis was performed based on the first and second laws of thermodynamics. Hexane, cyclohexane, isohexane, R245fa, and R236ea exhibit good performance at higher temperatures. In this study, three fluids—hexane, cyclohexane, and isohexane—were used. First, the model results for the pure fluids were compared with those of previous studies. Then, the important parameters of the cycle, including the efficiency of the first law of thermodynamics, the efficiency of the second law of thermodynamics, net productive power, and the amount of exergy destruction caused by changing the mass fraction of the refrigerant for the zeotropic fluids (investigated for the whole cycle and ORC), were obtained and compared.

Solar photovoltaic (PV) technology has a lower adoption rate than expected because of different weather conditions (sunny, cloudy, windy, rainy, and stormy) and high material manufacturing costs. To overcome the barriers to adoption, many researchers are developing methods to increase its performance. A photovoltaic–thermal absorber hybrid system may shift its performance, but to become more efficient, the technology could improve with some strong thermal absorber materials. A phase change material (PCM) could be a suitable possibility to enhance the (electrical and thermal) PV performance. In this study, a solar PVT hybrid system is developed with a PCM and analyzed for comparative performance based on Malaysian weather conditions. The result shows PV performance (both electrical and thermal) was increased by utilizing PCMs. Electrical and thermal efficiency measurements for different collector configurations are compared, and PV performance and temperature readings are presented and discussed. The maximum electrical and thermal efficiency found for PVT and PVT-PCM are 14.57% and 15.32%, and 75.29% and 86.19%, respectively. However, the present work may provide extensive experimental methods for developing a PVT-PCM hybrid system to enhance electrical and thermal performance and use in different applications.

Energy storage is becoming a critical issue due to the diminishing availability of fossil fuels and the intermittent nature of current renewable energy sources. As a result, thermal management (TM) and thermal energy systems have gained significant attention due to their crucial roles in various industries. Among the different TM materials, MXenes, a member of the transition metal carbide/nitride family, have emerged as a promising material due to their unique 2D nanostructure, changeable surface chemistry, high electrical/thermal conductivity, light absorptivity, and low infrared emissivity. This review outlines the synthesis methods of MXenes and their various features and applications in thermal management. These 2D materials exhibit outstanding optical and thermal properties, making them suitable for thermal energy generation and storage. The study also covers the potential applications of MXene in the desalination industry, hybrid photovoltaic thermal systems, solar energy storage, electronics, and other thermal management related industries. The findings suggest that MXene-based TM materials have remarkable features that significantly influence thermal energy storage and conversion and present opportunities for further research in efficiently using these materials.

In this article, the concept of an electric vehicle (EV) as a sustainable development (SD) is discussed, and the viability of the development of electric vehicles is assessed. This study broadens the conventional definition of sustainable development by incorporating and prioritizing crucial areas of technology, environment, and policy performance. The proposed review studies have summarized the elements that can promote the integration of electric vehicle technology. The innovation of the EV has just become a modern innovation. At the same time, some obstacles, such as policy and lower adoption, are resisting its goals. To overcome this situation, electric cars have to adopt some innovative approaches that can be another path to success. The review result shows that the proposal discusses the technological advancements of electric vehicles worldwide and paves the way for further improvements. The results also mentioned technological development to reduce emissions and help us understand the impact on the environment and health benefits. However, the summary would be advantageous to both scholars and policymakers, as there is a lack of integrative reviews that assess the global demand and development of EVs simultaneously and collectively. This review would provide insight for investors and policymakers to envisage electric mobility.

The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.

The ever-increasing water stress and availability of fresh drinking water are becoming a major challenge in rural and urban communities. The current high-end and large-scale technologies are becoming way more expensive and not friendly to the environment. In this regard, solar still is becoming a prominent and promising future technology due to its environment-friendly nature, less maintenance and operational costs, and simple design. The technological challenge regarding solar still is its low distillate yield. In this study, an attempt has been made to investigate the effect of tin oxide (SnO2) on the absorption surface of solar still towards improvement in sunlight absorption, which would lead to high distillate production rates. Various concentrations of SnO2, i.e., 0.5wt%, 1 wt%, 3 wt%, 5 wt%, 7 wt%, 10 wt%, 15 wt%, and 20 wt%, have been mixed in black and applied on the absorber plate to further optimize the suitable concentration. The experiments have been performed in both indoor (simulated) and outdoor conditions. An increase in surface temperature of absorber plate has been observed with increasing concentration of SnO2 under both the indoor and outdoor conditions, which is due to high solar spectrum absorption properties of SnO2 in the ultraviolet (UV) and near to far-infrared (IR) regions. The highest surface temperature of 101.61°C has been observed for specimens containing 15 wt% SnO2 in black paint under indoor conditions at 1000W/m2 irradiation levels, which is 53.67% higher compared to bare aluminum plate and 16.91% higher compared to only black paint coated aluminum plate. On the other hand, the maximum temperature of 74.96°C has been recorded for the identical specimens containing 15 wt% SnO2 under uncontrolled outdoor conditions. The recorded temperature is 47.96% higher than the bare aluminum plate and 14.88% higher than the black paint-coated aluminum plate. The difference of maximum temperatures under indoor and outdoor conditions is due to uncontrolled outdoor conditions and convective losses.

Greenhouse gas (GHG) emissions are one of the major problems that the world is facing nowadays. The transportation sector, where vehicles run on oil, contributes a large amount of GHG. The development of electric vehicles to meet the allowed GHG limits has recently been the main focus of research worldwide. Research in electric vehicles (EVs) has observed a tremendous upsurge in recent years. However, reviews that analyze and present the demand and development of EVs comprehensively are still inadequate, and this integrative review is an effort to fill that gap. This study has revealed many thought-provoking understandings related to specific developments, specifically global demand and growth of EVs along with electricity and battery demand, current technological developments in EVs, energy storage technologies, and charging strategies. It also details the next generation of EVs and their technological advancements, such as wireless power transfer. The development of a smart city concept by EV implementation added a new aspect to this review. The summary would be advantageous to both scholars and policymakers, as there has been a lack of integrative reviews that assessed EVs’ global demand and development simultaneously and collectively. This review concludes the intuitions for investors and policymakers to envisage electric mobility.

Despite being a limited and scarce resource, the necessity and exploitation of fossil fuels are unstoppable in serving human demands. In order to supply energy demand without causing environmental damage, it is crucial to utilize a variety of renewable feedstock resources. Biochar, made up mostly of carbon, oxygen, and hydrogen, is the product of the thermochemical processes of pyrolysis, hydrothermal carbonization, torrefaction, and hydrothermal liquefaction. Biochar, once activated, has the potential to act as a catalyst in a variety of energy generation processes, including transesterification and fermentation. Transesterification is the process that is used to produce biodiesel from a variety of oils, both edible and non-edible, as well as animal fats in the presence of either a homogeneous or a heterogeneous catalyst. When selecting a catalyst, the amount of free fatty acid (FFA) content in the oil is considered. Homogeneous catalysts are superior to heterogeneous catalysts because they are unaffected by the concentration of free fatty acids in the oil. Homogeneous catalysts are extremely hazardous, as they are poisonous, combustible, and corrosive. In addition, the production of soaps as a byproduct and a large volume of wastewater from the use of homogeneous catalysts necessitates additional pretreatment procedures and costs for adequate disposal. This article examines the biochar-based fuel-generation catalyst in detail. At first, a wide variety of thermochemical methods were provided for manufacturing biochar and its production. Biochar’s chemical nature was analyzed, and the case for using it as a catalyst in the production of biofuels was also scrutinized. An explanation of how the biochar catalyst can improve fuel synthesis is provided for readers. Biodiesel’s transesterification and esterification processes, biomass hydrolysis, and biohydrogen generation with the help of a biochar catalyst are all reviewed in detail.

Pakistan has been facing energy crises for more than a decade as a result of its reliance on imported fossil fuels, circular debt, political instability, and absurd energy policies. However, the country has abundant renewable energy resources which, if harnessed, may help to effectively cope with ever-increasing energy demand. This review study investigates the country’s economic and energy situations, energy crises, and energy sector performance. A critical analysis of studies conducted on Pakistan’s energy planning since its independence in 1947 is, and policies announced to date are assessed. This review reveals that the economic situation of the country has remained severely stressed, and energy sector performance has been compromised over the years for various underlying reasons. The energy policy narrative in the early decades of the post-independence period focused on water resource management, whereas energy concerns were only realized in the late 1960s as demand grew. The first-ever energy and power planning study in Pakistan was conducted in 1967, and since then, various studies have been conducted to support the medium-term development plans of the government. These planning studies inspired further development, and in 1994, the first-ever electricity-focused power policy was announced by the government in response to industrial growth and subsequent electricity demand. However, this and subsequent policies were fossil-fuel-centric until 2006, when the government announced the first-ever renewable energy policy. This 2006 policy focused on increasing renewable energy penetration in the overall energy mix by setting specific targets. However, these targets have rarely been accomplished as a result of a lack of an effective planning paradigm, as most of studies have been conducted without sound demand forecasting and without considering renewable energy’s potential to meet growing demand. As such, planning efforts based on proven methodologies/modeling tools and the undertaking of demand forecasts and renewable energy assessments are inevitable for countries such as Pakistan. Therefore, we suggest that sectoral energy demand forecasting, estimation of renewable energy potential with end use, and modeling of optimal penetration of renewable energy using energy modeling tools will be helpful to develop sustainable energy policies in Pakistan to eradicate the energy crisis.

Geothermal is a renewable energy source, but this is not as often seen as other renewable energy sources, such as solar, wind, hydro, etc. Several applications could be implemented through geothermal energy, and heating & cooling systems are one of them. Because of the limits of technology, it is hard to improve cooling systems as an application. To address long-term sustainable space heating and cooling, it is imperative to develop geothermal technology. It is known as the oldest, most flexible, most adaptable, and most prevalent approach toward using renewable energy. Therefore, this review has reviewed the global development and challenges of geothermal energy for cooling systems. There are large reserves of geothermal energy available around the world, and numerous scholars have emphasized its importance, but due to a lack of knowledge, no operational work has been done in using these systems for cooling up to this point. This review paper examines globally available geothermal energy sources and technologies for environmentally friendly and sustainable cooling supplies. Finally, the benefits and challenges of geothermal systems for cooling are outlined to promote local, regional, and global investment in utilizing these resources for cooling.

To mitigate the consequences of climate change, there is an increasing need to minimize the usage of fossil fuels, especially in the industrial sector because the majority of the industrial sector primarily rely on fossil fuels to meet their needs for heat energy, and a practical strategy to reduce reliance on fossil fuels is to use energy from the sun. Due to their environmental advantages, energy security, and viability as a potential substitute for fossil fuels, solar thermal collectors are acknowledged as promising technology to harness solar thermal energy fir process heating applications. This review is a thorough compendium and evaluation of contemporary literature on solar thermal collectors and their applications in industry. Apart from applications, this review paper also assesses the challenges and limitations currently hindering the global acceptance of this technology in the industrial sector.

With an expected annual increase of 1.2%, the industrial sector already consumes over 54% of all the energy generated globally. The majority of industrial sectors presently relies on fossil fuels to fulfil their needs for heat energy, but renewable sources, especially solar energy, can be substituted for them. For an underdeveloped country such as Pakistan, its industrial sector is important for the country’s economic development and long-term growth. The use of solar thermal energy potentially offers a significant and cheap alternative to fossil fuels. The current study focuses on a process heating system based on flat-plate solar collectors, developed to provide low to moderate temperature process heat. The innovative model’s thermal efficiency and economic feasibility have undergone a thorough investigation and analysis through TRNSYS simulations. The system portrayed a 79% thermal energy efficiency and 4.31% exergy efficiency during peak hours. The optimized system for three different temperatures of 60 °C, 70 °C, and 80 °C was designed and evaluated. The system presented a total of 82 tons of CO2 prevention annually. The economic analysis consisting of three parameters, NPV, IRR and PBP, also deemed the FPC-based solar thermal system economically profitable.

This paper reviews the optimization strategies for the optimal simultaneous allocation of distributed generation (DG) and shunts capacitor banks (SCBs) in electrical distribution networks. These optimization strategies aim to determine the optimal size, location, and combination of DGs and SCBs to constitute a techno-economic system while satisfying the constraints and energy demand of the load. The optimization strategies explicitly reviewed include the problem formulations, optimization techniques, restrictions posed for optimization problems, decision variables, and network operating modes typically assumed while allocating the DGs and SCBs. In addition, there is an attempt to highlight the limitations of the existing literature and future research directions. This study undertakes a comprehensive review of the literature that systematically considers the simultaneous application of DGs and SCBs to advance the existing literature, which lacks such a review. Expectedly, this review will serve as a principle platform for researchers intending to explore the subject area for further improvement.

Solar light absorber surface is probably one of the most important components in solar still that dictates the distillate yield. In this work, a systematic study is conducted to investigate the effect of particle size and concentration of titanium oxide (TiO2) in black paint in increasing the solar still absorber surface temperature. The various available particle sizes, i.e., 20, 150, and 400 nm, are mixed in black paint with varying concentrations and are applied on the absorber plate. XRD is used for phase identification of as-received powders. UV-Vis spectroscopy is used to examine light absorption properties. Finally, extensive indoor testing (using an improvised solar simulator) and outdoor testing are conducted to optimize the concentration. An increase in surface temperature is observed with the introduction of TiO2 nanoparticles in black paint. Furthermore, the increase in particle size leads to an increase in temperature. The highest surface temperatures of 104.86°C, 105.42°C, and 106.32°C are recorded for specimens with particles sizes 20 nm (at 15 wt% concentration), 150 nm (at 10 wt% concentration), and 400 nm (at 7 wt% concentration), respectively. Furthermore, the highest temperature of 69.69°C is recorded for TiO2-400 nm specimens under outdoor conditions, which is 15.97% higher than that of the bare aluminum plate. The increase in surface temperature may be due to high UV absorption. Moreover, an increase in particle size leads to high light-scattering ability, further improving the light-harvesting ability.

For over a decade, distributed generations (DGs) have sufficiently convinced the researchers that they are the economic and environment-friendly solution that can be integrated with the centralized generations. The optimal planning of distributed generations requires the appropriate location and sizing and their corresponding control with various power network types to obtain the best of the technical, economical, commercial, and regulatory objectives. Most of these objectives are conflicting in nature and require multi-objective solutions. Therefore, this paper brings a comprehensive literature review and a critical analysis of the state of the art of the optimal multi-objective planning of DG installation in the power network with different objective functions and their constraints. The paper considers the adoption of optimization techniques for distributed generation planning in radial distribution systems from different power system performance viewpoints; it considers the use of different DG types, distribution models, DG variables, and mathematical formulations; and it considers the participation of different countries in the stated DG placement and sizing problem. Moreover, the summary of the literature review and critical analysis of this article helps the researchers and engineers to explore the research gap and to find the future recommendations for the robust optimal planning of the DGs working with various objectives and algorithms. The paper considers the adoption of uncertainties on the load and generation side, the introduction of DGs with energy storage backups, and the testing of DG placement and sizing on large and complex distribution networks.

Solid fuel combustion in a chamber does not necessarily occur at a constant rate and may show fluctuations due to variables such as varying burning rates, chamber pressure, and residual combustion. These variables can cause the fuel to burn disproportionately. The acoustic environment of obstacle vortex-driven flow due to transient combustion with pressure oscillations in a solid fuel chamber is numerically investigated in the present study. Solid fuel combustion is considered transient, and flow characteristics of the present problem are governed by large eddies shed from an obstacle. Since unsteady Reynolds-averaged Navier-Stokes (URANS) simulations are not appropriate to compute the present flow phenomenon, therefore, a detached eddy simulation (DES) is performed to precisely predict the flow behavior. Simulation of steady-state combustion is carried out to validate the numerical results with available experimental data from the literature. The simulation of transient combustion shows that if the combustion frequency is close to the chamber’s modal frequency of the chamber, its amplitude increases greatly and creates an acute acoustic environment. This will result in fuel savings. The amplitude of pressure oscillation up to 18% and 5% of mean pressure are evident at the first and second mode of forced oscillation frequencies respectively. Interestingly, it is also found that pressure oscillation always occurs at inlet mass flux disturbance frequency and not between the disturbance and natural frequency of the chamber. As a result, it is evident that the combustion process or chamber configuration could be modified to ensure that both frequencies are far away enough to interact and create both a harsh acoustic environment and sufficient fuel to burn disproportionately.

Solar photovoltaic (PV) converts sunlight into electricity and is an appropriate alternative to overcome the depletion of conventional fuels and global warming issues. The performance of a PV panel may vary with respect to PV cell technology, fabrication methods, and operating conditions. This research aims at performing an experimental study to investigate the electrical performance of novel tempered glass-based PV panels using two different types of solar cells: monocrystalline and polycrystalline. Tempered glass-based panels are modified forms of commercial PV panels, in which ethylene-vinyl acetate (EVA) and Tedlar are not utilized. This new fabrication method was carried out in this research. Real-time data recordings regarding the PV electrical characteristics (I-V curve) and solar irradiance were conducted under Malaysian weather conditions on clear sunny days. Results indicated that, at solar irradiance of 900 W/m2, the outputs from the fabricated polycrystalline and monocrystalline PV panels were 67.4 W and 75.67 W, respectively. However, at the highest average solar irradiance (634.61 W/m2), which was obtained at 12:30 PM, the outputs from both panels were 47.87 W and 54.89 W. An I-V curve was obtained for the real-time weather. The electrical efficiencies of the two PV panels were analyzed to be 10.54% and 12.23%.

Solar energy has increasingly been employed for domestic and industrial water heating. Both conventional solar water heater (SWH) and photovoltaic thermal (PVT) systems suffer from the drawback of poor energy conversion efficiency. In this article, a unique parallel serpentine-flow thermal collector has been designed and developed that has been employed as an isolated SWH and also integrated with a 32-cell monocrystalline photovoltaic (PV) module. Simulation models of both SWH and PVT systems have been built in TRNSYS to study their thermal performance numerically. Thereafter, outdoor experimental investigations have been conducted under the composite climates of Malaysia. Experimental results show very good agreement with the simulation outcomes with disparity less than 2%. At the optimum flow rate, the maximum thermal efficiencies of SWH and PVT are 82.5% and 74.62%, respectively. Superior water outlet temperature was obtained with SWH. Although SWH exhibits superior thermal performance, PVT’s additional electrical output might make it preferable for several applications.

The availability of fresh drinkable water and water security is becoming a global challenge for sustainable development. In this regard, solar stills, due to their ease in operation, installation, and utilization of direct sunlight (as thermal energy), promise a better and sustainable future technology for water security in urban and remote areas. The major issue is its low distillate productivity, which limits its widespread commercialization. In this study, the effect of silicon (Si) particles is examined to improve the absorber surface temperature of the solar still absorber plate, which is the major component for increased distillate yield. Various weight percentages of Si particles were introduced in paint and coated on the aluminum absorber surface. Extensive indoor (using a self-made halogen light-based solar simulator) and outdoor testing were conducted to optimize the concentration. The coatings with 15 wt % Si in the paint exhibited the highest increase in temperature, namely, 98.5 °C under indoor controlled conditions at 1000 W/m2 irradiation, which is 65.81% higher than a bare aluminum plate and 37.09% higher compared to a black paint-coated aluminum plate. On the other hand, coatings with 10 wt % Si reached up to 73.2 °C under uncontrolled outdoor conditions compared to 68.8 °C for the black paint-coated aluminum plate. A further increase in concentration did not improve the surface temperature, which was due to an excessive increase in thermal conductivity and high convective heat losses.

Demand for water resources has increased dramatically due to the global increase in consumption of water, which has resulted in water depletion. Additionally, global climate change has further resulted as an impediment to human survival. Moreover, Pakistan is among the countries that have already crossed the water scarcity line, experiencing drought in the water-stressed Thar desert. Drought mitigation actions can be effectively achieved by forecasting techniques. This research describes the application of a linear stochastic model, i.e., Autoregressive Integrated Moving Average (ARIMA), to predict the drought pattern. The Standardized Precipitation Evapotranspiration Index (SPEI) is calculated to develop ARIMA models to forecast drought in a hyper-arid environment. In this study, drought forecast is demonstrated by results achieved from ARIMA models for various time periods. Result shows that the values of p, d, and q (non-seasonal model parameter) and P, D, and Q (seasonal model parameter) for the same SPEI period in the proposed models are analogous where “p” is the order of autoregressive lags, q is the order of moving average lags and d is the order of integration. Additionally, these parameters show the strong likeness for Moving Average (M.A) and Autoregressive (A.R) parameter values. From the various developed models for the Thar region, it has been concluded that the model (0,1,0)(1,0,2) is the best ARIMA model at 24 SPEI and could be considered as a generalized model. In the (0,1,0) model, the A.R term is 0, the difference/order of integration is 1 and the moving average is 0, and in the model (1,0,2) whose A.R has the 1st lag, the difference/order of integration is 0 and the moving average has 2 lags. Larger values for R2 greater than 0.9 and smaller values of Mean Error (ME), Mean Absolute Error (MAE), Mean Percentile Error (MPE), Mean Absolute Percentile Error (MAPE), and Mean Absolute Square Error (MASE) provide the acceptance of the generalized model. Consequently, this research suggests that drought forecasting can be effectively fulfilled by using ARIMA models, which can be assist policy planners of water resources to place safeguards keeping in view the future severity of the drought.