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The study reports on synthesis of aqueous extract mediated gold and silver nanoparticles of M. biflora (MBAuNPs and MBAgNPs) via hydrated chloroauric acid and silver nitrate salts. The nanoparticles (NPs) were produced in 1:15 (MBAuNPs) and 1:6 (MBAgNPs) ratios under sunlight displaying localized surface plasmon resonance (LSPR) peaks at 541 and 431 nm, respectively. The sizes characterized by transmission electron, scanning electron, and atomic force microscopic (SEM, TEM, AFM) techniques were respectively 26.73 nm and 53.81 nm. The subject NPs demonstrated application in the degradation of methylene blue, Congo red, Rhodamine B, methyl orange, ortho-nitrophenol, and para-nitrophenol ranging from 65 to 86 %. For detection of levofloxacin, amoxicillin, and azithromycin antibiotics, the MBAuNPs and MBAgNPs exhibited efficiency in real water and biological (blood plasma and urine) samples. Conclusively, the MBAuNPs and MBAgNPs applications for dyes degradation and antibiotics detection was found as simple and cost-effective analytical method.

Microgrid (MG) has extensive properties to overcome common problems of local distribution system. Some of those problems are generation and demand difference, blackout and brownout, environmental concerns due to burning of natural resources in power stations (indirectly), and reliability issues. Research on microgrid is being conducted to enhance its features to mitigate power quality (PQ) problems associated with distribution system. Voltage sag and swell have been major power quality problems for decades, loads in distribution system are heavily affected due to these power quality problems. In the distribution system, microgrid and power quality compensation strategy should be existed in order to ensure reliability and voltage sag/swell mitigation. Dynamic voltage restorer (DVR) is comprehensive power electronics based Flexible Alternating Current Transmission System (FACTS) device, it is third-generation FACTS device as its control scheme selection flexibility and power line coupling approach make it advance when compare to first and second-generation FACTS devices. In this paper, an Enhanced Dynamic Voltage Restorer (EDVR) is presented to efficiently mitigate voltage sag/swell in grid connected microgrid. On the one side, the presence of microgrid structure ensures reliability of distribution system for local loads on the other side, EDVR ensures voltage sag/swell free power supply for loads. The control strategy of EDVR is based on enhanced synchronous reference frame (ESRF) approach and fuzzy technique system. ESRF is specially design for fast and precise operation of EDVR whereas; fuzzy technique system is responsible for standardized voltage supply for local loads. DC link voltage of EDVR is effectively regulated with the help of proposed control scheme at the time of voltage sag/swell compensation. Stability analysis of ESRF control has been done using modeling of VSC and eigenvalue analysis system. Simulation results on MATLAB/Simulink verified the performance of EDVR under presented control approach hence the specific loads in distribution system are more secure under proposed microgrid system with EDVR.

Potential implementation of smart grid technologies has been given wide attention for modernization of electrical power systems. Existing power grid infrastructure of Pakistan is ill-suited to accommodate increased renewable energy sources and poses interoperability issues for seamless transition towards decentralization and digitalization of the power grid. Modernization of power grid through realization of smart grid technologies is much needed to meet the ever-increasing energy demand of the country. The China Pakistan Economic Corridor (CPEC) is a conglomerate of multibillion dollar infrastructural projects with a major focus on energy sector which offers a great opportunity for the country to invest appropriately in the power grid transformation. This research study is focused on devising a technical and policy framework for conversion of the existing power grid into smart grid under the umbrella of CPEC. Potential key drivers are identified for the deployment of smart technologies to upgrade the power grid of Pakistan. The presented research proposes a stage-wise implementation plan to upgrade the power infrastructure of the country. In particular, this research study presents cost benefit analysis for a case study with an aim to reduce the interruption index values. Obtained values of the cost benefit analysis suggest that the investment incurred for smart grid realization can be paid back within a period of twenty months and offers a positive net present value which establishes the financial feasibility of the project. Importantly, policies and recommendations are suggested which would enable the concerned stakeholders to ensure grid modernization as per the proposed smart grid implementation plan.

Electric mobility seems to bring a paradigm shift in the road transport sector worldwide. Huge consumption of fossil fuels and ever-increasing traffic congestion have caused concerns over future energy consumption, economy growth, and greenhouse gas emissions in the Gulf Cooperation Council region’s member countries. The introduction of electric vehicles (EVs) in the two most populous countries of the region, i.e., the Kingdom of Saudi Arabia and UAE is considered a promising option to address environmental pollution and future economy-related fears region. This paper presents key drivers for the countries to adopt electric transportation. This research study investigates the impact of EVs penetration on energy, economy, and environment of KSA and UAE through EV stockpile forecasting using linear regression analysis. The obtained results suggest that expected growth in KSA and UAE’s power sector will enable them to keep up 5% and 30% EVs penetration by 2030, respectively. In this regard, a set of policies are proposed, which will enable the countries to pace up their efforts to achieve the intended greenhouse gases (GHG) emission reduction goals. Though the presented research is focused on the case study of KSA and UAE, the research findings are generalized enough to be applied to all other regions of the region. The suggested set of policies will serve as guidelines for the relevant stakeholders about the necessary measures required for sustainable road transport electrification in KSA and UAE.

Investigation of power output from PV arrays under different fault conditions is an essential task to enhance performance of a photovoltaic system under all operating conditions. Significant reduction in power output can occur during various PV faults such as module disconnection, bypass diode failure, bridge fault, and short circuit fault under non-uniform shading conditions. These PV faults may cause several peaks in the characteristics curve of PV arrays, which can lead to failure of the MPPT control strategy. In fact, impact of a fault can differ depending on the type of PV array, and it can make the control of the system more complex. Therefore, consideration of suitable PV arrays with an effective control design is necessary for maximum power output from a PV system. For this purpose, the proposed study presents a comparative study of two intelligent control schemes, i.e., fuzzy logic (FL) and particle swarm optimization (PSO), with a conventional control scheme known as perturb and observe (P&O) for power extraction from a PV system. The comparative analysis is based on the performance of the control strategies under several faults and the types of PV modules, i.e., monocrystalline and thin-film PV arrays. In this study, numerical analysis for complex fault scenarios like multiple faults under partial shading have also been performed. Different from the previous literature, this study will reveal the performance of FL-, PSO-, and P&O-based MPPT strategies to track maximum peak power during multiple severe fault conditions while considering the accuracy and fast-tracking efficiencies of the control techniques. A thorough analysis along with in-depth quantitative data are presented, confirming the superiority of intelligent control techniques under multiple faults and different PV types.

Photovoltaic (PV) faults such as partial shading, bypass-diode defects, degradation of PV modules, and wiring issues greatly affect the power output and cause various peaks in P-V curves of a PV system. Although, commonly used Total-Cross-Tied (TCT) scheme in PV arrays is considered instrumental for reducing power losses there lies a great scope to evaluate power extraction through reconfiguration of modules with different PV materials. This paper presents detailed investigation of power extraction using number placement reconfiguration method under numerous faults. PV power extraction is carried out and compared with three different interconnections of PV modules, including series-parallel (SP), bridge-link (BL) and TCT. In order to conduct a thorough investigation and better evaluate the performance of PV arrays, we have studied reconfiguration of PV modules with polycrystalline and copper indium gallium selenide (CIGS) PV technologies. In addition, this paper contains detailed quantification of the impact of the studied PV faults on power grid. The results obtained in MATLAB/Simulink demonstrate that CIGS PV technology performs better than polycrystalline in terms of power output during different faulty conditions. It becomes evident from the presented results that optimal reconfiguration of PV arrays can increase the power extraction from PV system with reduced number of P-V peaks. Hence, leading to improved performance of the PV system.

In the South Asian countries, including Bangladesh, India, and Pakistan, the current energy scenario is considered non-sustainable due to diverse issues such as economic, environmental, geopolitical, technological options for energy exploitation, and negligible volume of regional energy trade. Though, within the region, India is leading a phase of energy transition and economic transformation through renewable energy development. The countries need to exhibit well in the development of their renewable sources following the rapid pace of renewable energies worldwide. This article offers an overview of the energy scenario, growth of renewable energies, evolution, and approach for energy policy by highlighting key challenges and barriers for the ecological energy mix of the countries. Importantly, the paper assesses the current energy mix in South Asia, highlighting the anomaly of its fossil fuel-based future outlook, its ambitions to move towards less environmental pollution, and sustainable energy mix through a strategic tool SWOT analysis; strengths, weaknesses, opportunities, and threats (SWOT). In particular, this study examines the government policies to expand the implementation of renewable sources with an insight into the existing regulatory structure of the energy sector. The presented research findings suggest that to achieve the ambitious target to reduce emission discharge by up to 30% by the year 2030 under Intended Nationally Determined Contributions (INDCs), the Governments of the three countries must take preemptive measures. It includes the stage-wise reduction of subsidies on fossil fuels, market integration within the region, and swift realization of the existing initiatives through strong political will, good governance, adoption of the latest technologies, and a pragmatic action plan, and energy cooperation across the region.

A huge body of research is concentrated on developing advance load control strategies for reducing energy consumption and ensuring smooth operation of power system. Air conditioning loads are considered major energy consuming electrical loads in residential and business buildings, and that may cause significant rise in energy consumption. This paper presents an implementation of fuzzy controller with different number and shapes of membership functions for smoothing energy consumption of an air conditioning load while dealing with thermal disturbances. Simulations are conducted in MATLAB Simulink and the results demonstrate that the fuzzy controller with triangular membership function gives the desired performance with an error of less than 1% and saves 25% energy consumption. The obtained results are found effective in terms of smoothing energy consumption pattern of an air conditioning load without compromising consumer' s comfort. Performance of the designed controller is also compared with different controllers including self-tuning adaptive fuzzy controller, linear quadratic regulator (LQR) and nonlinear controllers. The presented fuzzy controller stands-out in terms of desired performance, simplicity and implementation.

Triboelectrification is a novel technology to harvest electricity from unexploited environmental energy. Its efficiency depends on the area, morphology and charges on the in-contact surfaces. Many studies are carried out in order to increase the efficiency of the triboelectric generators by increasing the surface area or varying the morphology but the charge density is not properly focused until date. Herein, we will discuss the effect of the artificial charges induced on the in-contact surface. For that purpose, plasma treatment was used to increase the surface charge density of the in-contact surface. The increment in the charge density actually helped us to increase the efficiency of triboelectric generator even without changing the dimensions of the device. The output power was increased up to two folds as compared to the untreated device. At the end, we also charged the capacitor in order to compare the charge storing efficiencies of both devices. We found that the untreated device and the plasma treated device (15 min and 30 min) stored voltages up to 6 V, 9 V and 12 V respectively. This type of study will surely support the researchers to enhance the triboelectric current generation efficiency even without increasing the size of the device.

Transportation sector in Pakistan has been rapidly growing, leading to an increase in diesel and petroleum consumption, heightened energy import bill, air pollution, and traffic congestion. The China-Pakistan economic corridor (CPEC) is a cluster of different multi-billion-dollar projects with a significant emphasis on enhancing regional connectivity, and the transportation sector is considered its backbone. Electric road transportation appears to be a game-changer to tackle energy, economy, and environmental issues of a country. This research paper is focused on a scheme and valuation of deploying electric transportation (e-trans) for mass transit under the CPEC umbrella in Pakistan. This paper identifies barriers and challenges and explores various technological options for adopting suitable electric vehicles (EVs) for mass transportation. The paper investigates technical infrastructural requirements, financial, and policy implications for the successful deployment of the proposed electric transportation. In particular, it proposes an EV charging infrastructure based on solar power generation to avoid any electricity burden to recharge EVs from the national power grid. In order to be comprehensive, it presents a case study to develop an EV charging station network with an estimation of the cost for accommodating charging requirements of one thousand electric trucks and hundred mobile charging trucks. Obtained results demonstrate that merely 0.29% of the CPEC's energy sector approved budget will be incurred for deploying the proposed charging station network to support e-trans over the studies road segment, which appears to be a feasible initiative. Important factors which may influence the proposed scheme for implementation of e-trans under the CPEC is analyzed using SWOT analysis. The recommended set of policies for deploying electric transportation and its quantitative financial analysis will help the concerned stakeholders to discover and execute the necessary steps to ensure sustainable electric transportation under the CPEC in Pakistan.

Categorization of PV faults is an essential task for improving the efficiency and reliability of a photovoltaic (PV) system. Output characteristics of a solar (PV) system can be severely affected under various fault conditions including short circuit, module mismatch, open circuit, and multiple faults under shading conditions. Such PV faults can potentially be analyzed through the PV characteristic curve analysis using a multilayer neural network with a scaled conjugate gradient algorithm (SCG). This paper presents an extensive investigation for categorization, i.e., classification of the above-mentioned PV faults using the SCG algorithm. The major contribution of the presented research work is the categorization of PV faults in sixteen different classes considering polycrystalline and thin-film PV technologies with two different configurations, including SP and TCT. The fault classification is achieved with high accuracy of 99.6% and a fast-computational time of 0.08 sec. The results are validated through the plot of the Confusion Matrix and Region of Convergence (ROC) with their performance evaluation in MATLAB. The achieved accuracy and fast computational time prove the effectiveness of the multilayer neural network-based approach for classification of the PV faults to increase power output, efficiency, and lifespan of PV systems.

Faults detection and analysis in PV system are considered critical for ensuring safety and increasing output power of PV arrays. PV faults do not only reduce output power and efficiency but also lessen the working life time of a system. Most common and chronic PV faults are line to line, line to ground, shadowing fault, and arc fault while less common and acute faults are hotspot, degradation, bypass diode, and connection faults. Event of PV fault detection failures, such as most recent in Mount Holly, USA in 2011 evinced the improvement in current fault detection and mitigation techniques to shrink such failures. There are various limitations in the existing fault detection techniques, as identified in this paper, which may cause misdetection of the faults. This paper is focused on mathematical formulation of various PV faults and lead to the latter's critical analysis in terms of efficiency, accuracy, complexity, and reliability. The presented work also helps to identify nature and causes of occurrence of a PV fault. This research work serves as a special set of references and recommendations for researchers and PV manufacturing industry to improve fault detection prospects in solar PV systems.

With the integration of intermittent energy sources in the power system, the main focus of thermal power plant design is to optimize the conventional system by improving efficiency and flexibility of power generation in the start-up and base-load procedures. This paper focuses on the behavior of thermal power generation with respect to load variations, fuel variations, weather conditions and internal dynamics of the system to aid in energy planning and the optimization of power system. Dynamic modeling and subsequent simulation is to make improvements. Therefore, to model the system, this research study has used a dynamic modeling and a simulation framework (OpenModelica based on modelica language) which is an object-oriented and equation-based language. This framework allows a non-causal modeling and a flexible reuse of the models since data-flow direction is not predefined like other state of the art simulating tools i.e. MATLAB. The proposed model analyses the trend of electrical power generation of KOT ADDU thermal power plant to predict the future demand of electrical power. The achieved results show more than 90% accuracy of the proposed model in the transient operation such as start-up and base load procedure.

Cross-border power trading through grid interconnection has gained significant attention in South Asia to support energy deficit states of the South Asian Association for Regional Cooperation (SAARC) region. Nonetheless, cross border grid interconnection requires technical standardization and grid code synchronization to ensure technically feasible, reliable, safe and economical power exchange among the SAARC states. Importantly, grid code harmonization necessitates the uniformity of technical data and codes among the member states. In this paper, we investigate the relevant factors such as regional barriers, trade potential, a gap in the grid codes, standardization of technical data, energy pricing, load profile, the load factor of different states, and regulatory framework of different states. All these factors are found to be helpful in formulating a set of recommendations for seamless power grid interconnections and power trading. We have developed a power pool model for the SAARC region with an objective to effectively realize the objectives of cross-border power trade in the region. The presented model takes account of transparency in market-clearing price, bidding of data, season-wise load profile, market volume, best case practices, institutional setup and rigorous case studies to ensure seamless grid interconnection and reliable power trading within the region.

It has been recognized that an increased penetration of electric vehicles (EVs) may potentially alter load profile in a distribution network. As EVs are regarded as a diversely distributed load so a deterministic method, to predict EV charging load, may not account for all possible factors that could affect the power system. Thus, a stochastic approach is applied that takes into account various realistic factors such as EV battery capacity, state of charge (SOC), driving habit/need, i.e., involving type and purpose of trip, plug-in time, mileage, recharging frequency per day, charging power rate and dynamic EV charging price under controlled and uncontrolled charging schemes. A probabilistic model of EVs charging pattern associated with residential load profile is developed. The probabilistic model gives an activity based residential load profile and EV charging pattern over a period of 24 h. Then, the model output is used to assess the power quality index such as voltage unbalance factor under different electric vehicle penetration levels at different nodes of the system. An uneven EV charging scenario is identified that could cause the voltage unbalance to exceed its permissible limit.

This paper is aimed at modelling of a distinct smart charging station for electric vehicles (EVs) that is suitable for DC quick EV charging while ensuring minimum stress on the power grid. Operation of the charging station is managed in such a way that it is either supplied by photovoltaic (PV) power or the power grid, and the vehicle-to-grid (V2G) is also implemented for improving the stability of the grid during peak load hours. The PV interfaced DC/DC converter and grid interfaced DC/AC bidirectional converter share a DC bus. A smooth transition of one operating mode to another demonstrates the effectiveness of the employed control strategy. Modelling and control of the different components are explained and are implemented in Simulink. Simulations illustrate the feasible behaviour of the charging station under all operating modes in terms of the four-way interaction among PV, EVs and the grid along with V2G operation. Additionally, a business model is discussed with comprehensive analysis of cost estimation for the deployment of charging facilities in a residential area. It has been recognized that EVs bring new opportunities in terms of providing regulation services and consumption flexibility by varying the recharging power at a certain time instant. The paper also discusses the potential financial incentives required to inspire EV owners for active participation in the demand response mechanism.

In this study, banana peel powder (BPP) was used to remove carcinogenic crystal violet (CV) dye from aqueous solution. The BPP adsorbent was characterized by Fourier-Transform Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM). In batch sorption experiments, the maximum removal of CV was recorded 93% at optimum levels of operating parameters, i.e., pH 7.0, adsorbent dosage 0.1 g, contact time 10 min, initial adsorbate concentration 90 ppm and temperature 20 C o. The kinetics of the adsorption process was found best to follow the pseudo first order model with high value of R 2 (0.9999) and low values of error functions. The Langmuir isotherm model was the best fit with high R 2 (0.9552) low values of error models. The plots of Freundlich and D-R isotherms confirmed the feasibility ( n = 1.3109 L g − 1 ) and the physisorption nature ( E s = 0.1107 kJ mol − 1 ) of the process respectively. The negative values of thermodynamic parameters (Δ G & Δ H) revealed that the adsorption was spontaneous and exothermic in nature respectively. The results of the desorption study showed that 91% of the adsorbent was regenerated. The novelty of the present research is that no work has been reported till date to remove crystal violet dye by using banana peel specifically. The high adsorption and desorption efficiencies (>90%) suggest that BPP possesses characteristics to be used as an effective, fast and low-cost adsorbent for adsorption of CV dye from industrial effluents. Adsorption technique was employed to remove crystal violet dye by using banana peel powder. The hydroxyl ( OH − ) & carboxylate ( OH − ) groups on adsorbent surface provided excellent binding with adsorbate molecules. Equilibrium attainment in 10 min at neutral pH. 93% adsorption and 91% desorption. [ABSTRACT FROM AUTHOR]