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The use of an electrical energy storage system (EESS) in a microgrid (MG) is widely recognized as a feasible method for mitigating the unpredictability and stochastic nature of sustainable distributed generators and other intermittent energy sources. The battery energy storage (BES) system is the most effective of the several power storage methods available today. The unit commitment (UC) determines the number of dedicated dispatchable distributed generators, respective power, the amount of energy transferred to and absorbed from the microgrid, as well as the power and influence of EESSs, among other factors. The BES deterioration is considered in the UC conceptualization, and an enhanced mixed particle swarm optimizer (EMPSO) is suggested to solve UC in MGs with EESS. Compared to the traditional PSO, the acceleration constants in EMPSO are exponentially adapted, and the inertial weight in EMPSO decreases linearly during each iteration. The proposed EMPSO is a mixed integer optimization algorithm that can handle continuous, binary, and integer variables. A part of the decision variables in EMPSO is transformed into a binary variable by introducing the quadratic transfer function (TF). This paper also considers the uncertainties in renewable power generation, load demand, and electricity market prices. In addition, a case study with a multiobjective optimization function with MG operating cost and BES deterioration defines the additional UC problem discussed in this paper. The transformation of a single-objective model into a multiobjective optimization model is carried out using the weighted sum approach, and the impacts of different weights on the operating cost and lifespan of the BES are also analyzed. The performance of the EMPSO with quadratic TF (EMPSO-Q) is compared with EMPSO with V-shaped TF (EMPSO-V), EMPSO with S-shaped TF (EMPSO-S), and PSO with S-shaped TF (PSO-S). The performance of EMPSO-Q is 15%, 35%, and 45% better than EMPSO-V, EMPSO-S, and PSO-S, respectively. In addition, when uncertainties are considered, the operating cost falls from $8729.87 to $8986.98. Considering BES deterioration, the BES lifespan improves from 350 to 590, and the operating cost increases from $8729.87 to $8917.7. Therefore, the obtained results prove that the EMPSO-Q algorithm could effectively and efficiently handle the UC problem.

Despite notable advantages and power conversion efficiency achieved by perovskite solar cells (PSCs), they could not hit the market commercially as perovskite solar cells are bottlenecked by the toxicity issue caused by the metal lead. Lead has proven toxicity issues that can harm human beings and the ecological system to a significant extent. And so, researchers around the world have started diving deep into revolutionary sustainable photovoltaics called non-toxic lead-free PSCs for a sustainable and safe environment. This is achieved by the substitution of lead in PSCs with non-toxic or less toxic substitute metals. This review article revolves around the substitution of lead with metals such as tin (Sn), germanium (Ge), titanium (Ti), silver (Ag), bismuth (Bi) and copper (Cu) in PSCs with their properties, sustainability and challenges. This review also discusses the ways undertaken to overcome stability and efficiency-related challenges encountered in the commercialization of lead-free non-toxic PSCs such as alteration of fabrication methods and introduction of additives. Finally, the review discusses the recent advancements/technologies used in lead-free perovskite solar cells and also predicts the future perspective/technologies and futuristic applications that can be implemented in lead-free PSCs.

ABSTRACTTo identify potential limitations that may impact the quality and efficiency of end-of-life vehicle (ELV) recycling and to provide solutions for effective management, this study investigates the current ELV recycling practice and the quality assessment systems around it in Malaysia by employing a mixed-methods approach. The approach encompasses qualitative and quantitative research alongside reviewing pertinent literature. The findings reveal that ELV recycling and its accompanying quality assessment systems in Malaysia are restricted and confront many critical challenges. The challenges include the lack of appropriate processes for recycling to maximize the material recovery, and the quality and reliability of the components put into circular economy practice. As a practical solution, a stakeholder-centric framework is proposed that allows better management of ELV waste from transportation and other related sectors. This framework enhances the efficiency and quality of ELV recycling practices in Malaysia and helps assess the quality of components recovered from ELVs.

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current–voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the Gradient-Based Optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder–Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed; therefore, the proposed algorithm is called a Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E−04, case 2 is 2.43E−04, and the average integral absolute error and relative error values are 1.05E−02 and 3.51E−03, respectively, for all case studies. With Friedman’s rank test values of 2.21 for numerical optimization and 1.66 for parameter estimation optimization, the CCNMGBO stood first among all selected algorithms.

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electric vehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency has brought EV technology to the forefront of the electric power distribution systems due to their ability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoption of EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillary service-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenario by incorporating demand-side management (DSM) through EV integration. The integration of EVs with DSM techniques is hurdled with various issues and challenges addressed throughout this literature review. The various research conducted on EV-DSM programs has been surveyed. This review article focuses on the issues, solutions, and challenges, with suggestions on modeling the charging infrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separately to enhance the EV-DSM operation. Gaps in current research and possible research directions have been discussed extensively to present a comprehensive insight into the current status of DSM programs employed with EV integration. This extensive review of EV-DSM will facilitate all the researchers to initiate research for superior and efficient energy management and EV scheduling strategies and mitigate the issues faced by system uncertainty modeling, variations, and constraints.

This study evaluates the degradation of mono, poly, and thin-film silicon solar photovoltaic (PV) modules through visual and electrical measurements in Dhaka’s tropical wet and dry climate conditions. For this, several sites of rooftop solar PV installations in Dhaka, aged at least 5 years, were selected for field investigation. A visible inspection for visual degradation followed by experimental testing on various electrical characteristics of the PV modules installed on the rooftop using a portable Seaward I–V Tracer for measuring the electrical degradation was done. The electrical data were used to evaluate the degradation of short circuit current, open circuit voltage, fill factor, and maximum power in the field as compared to the nameplate values. Experimental results showed the varying types of visual degradation types such as visible cracks, snail trails, moisture intrusion, front glass, and discoloration for the investigated PV modules. The degradation of PV modules in terms of short circuit current was 0.7%/year for 85Wp mono c-Si, 7.6%/year for 150Wp poly c-Si, and 9.7%/year for 105Wp silicon thin-film, respectively. We believe this study would guide the maintenance of the solar PV systems installed on Dhaka city’s rooftop buildings.

High-impedance fault (HIF) is always a threat and the biggest challenge in the power transmission and distribution system (PTDS). For a PTDS to operate effectively, HIF diagnosis is essential. However, given the HIF’s nature and the involved complexity, detection, identification, and fault location are difficult. This will be even more complicated in conventional PTDSs as they are inefficient and highly vulnerable. Given the importance and urgent need for HIF diagnosis in PTDS, this study reviews state-of-the-art HIF phenomenon and detection techniques and proposes the use of “various signal processing techniques for fault feature extraction” and “ different classifiers for identifying HIF.” First, HIF current/voltage signals are analyzed using signal processing techniques, which include the discrete wavelet transform (DWT), pattern recognition, Kalman filtering, TT transform, mathematical morphology (MM), S transform (ST), fast Fourier transform (FFT), principal component analysis (PCA), linear discriminant analysis (LDA), and wavelet transforms, such as dual-tree, maximum overlap discrete wavelet transform (MODWT), and lifting wavelet transform (LWT). Second, the various HIF and non-HIF faults are classified using intelligent classifiers. The intelligent classifiers include artificial neural networks (ANNs), probabilistic neural networks (PNNs), genetic algorithms (GAs), fuzzy logic, adaptive neuro-fuzzy interface system, support vector machine (SVM), extreme learning machine (ELM), adaptive resonance theory, random forests (RFs), decision trees (DTs), and convolution neural networks (CNNs). In addition to the comparative discussion of various classifier techniques, their evaluation criterion and performance are prioritized. Third, this review also studied different test systems, such as radial distribution network, mesh distribution network, IEEE 4 node, IEEE 13 node feeder, IEEE 34 node feeder, IEEE 39 node feeder, IEEE 123 node feeder, Palash feeder, and test microgrid systems, to assess the pertinence of various HIF detection schemes and the behavior along with methods to locate the HIF. Overall, we believe this review would serve as a comprehensive compendium of advanced techniques for HIF diagnosis in different test systems.

Floating solar photovoltaic (FSPV) systems that allow solar panel installations on water bodies are gaining popularity worldwide as they mainly avoid land-use conflicts created by, and for their superior performance over, ground-mounted photovoltaic installations. Though many studies in the FSPV literature showed how superior FSPVs perform, we still believe there are few potential opportunities for further enhancement in performance. On the other side, the industry’s delivery of FSPV installation service to clients is often questioned, highlighting that FSPV modeling is compromised, leading to false promises on energy performance and feasibility. This might be true given the lack of modeling tools specific to FSPV. With this hypothesis, this review investigates existing modeling approaches by FSPV researchers/industry people practicing and potentially implementable energy performance enhancement strategies leading to the advancement of modeling tools. The review outcome suggested that every FSPV researcher/service provider must carefully design and optimize the FSPV system considering suitable performance enhancement strategies, for instance, replacing conventional solar panels with bifacial ones and integrating various cooling and cleaning methods. Also, while assessing the feasibility, they must follow the lifecycle-based performance indicators that broadly fall under the techno-economic-environmental and social aspects with an appropriate framework-driven assessment approach. Lastly, we have shown a conceptual FSPV project simulation tool consolidating the performance indicators and explored performance enhancement strategies that we believe would help the FSPV community.

Despite the low local energy access rates, Africa is considered a key player in the global energy transition due to its large supply of fossil fuels and a large reserve of critical minerals essential for manufacturing renewable energy components in the energy sector and storage devices in the transportation and electronics sectors. But building a sustainable society at all levels across nations would only come when there exists a just and inclusive energy transition based on the idea of “leave no one behind”. While many African countries have embarked on ambitious and transformative transition strategies, and many energy projects classified as “clean” have economic, environmental, and social implications that jeopardize the wellbeing of those already vulnerable to the impacts of climate change. This paper explores the policy implications of the just transition to ensure that efforts to steer Africa towards a lower carbon future are supported by fair, equity, and justice considerations. Our analyses provide valuable evidence for considering a just transition in Africa that will not exacerbate the current socio-economic challenges the region is facing but will support sustained poverty reduction and the achievement of faster economic growth. Our findings show that the African continent’s multiple challenges of energy security, economic growth, and affordable access must feature in its clean energy transition. We draw conclusions that an incremental transition emphasizing low-carbon development is the most feasible and pragmatic approach to transform the region’s economy and address climate change challenges.

The optimal generation for short-term hydrothermal scheduling (OGStHS) with the deliberation of various purposes is a complex non-linear constrained optimization problem. There exist numerous constraints, which make the OGStHS optimization problem more complicated. The considered constraints for this problem are mostly related to energy performance, operational conditions, water, and power infrastructure. All these constraints would generally influence the cost of fuel. In this study, a multi-objective optimization form of OGStHS is suggested to estimate the minimum cost of fuel, which mainly influences industrial operation. The water transfer delays among multi-related reservoirs and the thermal plants’ valve-point influences are considered for the accurate formulation of the OGStHS problem. Meantime, a grasshopper optimization algorithm (GOA) is performed to handle the OGStHS problem by getting optimized for both objectives concurrently. A modern approach is shown in this study to get a solution to the OGStHS problem. Furthermore, to deal with the complex restraints efficiently, modern heuristic restriction treatment processes with no drawback impact frames have been offered in this study. Two hydrothermal power systems have illustrated the suggested GOA technique’s utility and performance. Compared with other available approaches, the analytical results are admitted that GOA can provide a better understanding by decreasing fuel cost and emission concurrently.

Russia is known to be a country with enormous energy resources both renewables and non-renewables. Much of the country’s effort towards energy generation has been on the development of the non-renewables over the years. This study examined the opportunities and challenges in Russia’s Renewable energy (RE) sector. By coupling both interviews and literature reviews, a total of 8 main opportunities and 7 key challenges were identified and discussed. The Best–Worst-Method was used to assign weights to the various factors using inputs of 30 experienced experts in Russia’s RE sector. According to the obtained results, the most significant opportunity that the country would have to take advantage of is the opportunity to export RE outside the shores of the country, it recorded 27.7 percent. This is followed by the country’s target for the RE sector which scored 18%, hydrogen production and need to meet local energy requirements followed with 12% each. The greatest challenge which also serve as a hindrance to the development of RE in the country is the low attention given to clean technologies from government, it recorded a weight of 31.4%. This is followed by unequal playing field, and strict local content requirements which recorded 17.9% and 13.5%, respectively. The study ended with some strategic recommendations to authorities for the development of the sector.

In acknowledgment of the devastating consequences of environmental deterioration, the Mediterranean members are committed to adopt the 2015 treaty action plans of the Paris Climate Agreement (COP21) as carbon dioxide emission (CO2) are on the rise in the Mediterranean region, which seems to be a serious challenge to our world’s environment. To this end, our study examined the impact of Foreign Direct Investment (FDI) on environmental degradation for the Mediterranean members for the period between 1995 to 2016. However, variables such as, financial development, economic growth, renewable energy and fossil fuel were further examined by the use cross-sectional-Panel pooled Auto Regressive Distributed Lag methodology, Augmented Mean Group (AMG) and Dumitrescu and Hurlin panel causality test was used for causality analysis. The co-integration results from Westerlund (2007) shows a long-run equilibrium relationship between highlighted variables. The empirical result revealed a negative relation between FDI and CO2 indicating pollutant Hallo Hypothesis (PHH). Moreover, income and its square show an inverted U-Shaped curve indicating environmental Kuznets curve (EKC) hypothesis. Both financial development and renewable energy indicated an adverse association with CO2 emission whereas fossil fuel had a positive relationship with emissions. However, there was a feedback causality among income and carbon emission as well as financial development and carbon emission. Furthermore, we observe that FDI and carbon emission, renewable energy and carbon emission, as well as fossil fuel and carbon emission were found to have one-way causal relationship. Overall, the study suggests some policy prescriptions including the implementation of conservation initiatives and the establishment of clean energy regulation and strategies for the investigated bloc.

Battery Energy Storage Systems (BESS) are essential for increasing distribution network performance. Appropriate location, size, and operation of BESS can improve overall network performance. The appropriately scaled and installed BESS helps meet peak energy demand, improve the advantages of integrating renewable and distributed energy sources, improve power quality control, and lower the cost of expanding or re-configuring the distribution networks. This paper investigates the feasibility of BESS for providing short-term and long-term ancillary services in power distribution grids by reviewing the developments and limitations in the last decade (2010–2022). The short-term ancillary services are reviewed for voltage support, frequency regulation, and black start. The long-term ancillary services are reviewed for peak shaving, congestion relief, and power smoothing. Reviewing short-term ancillary services provides renewable energy operators and researchers with a vast range of recent BESS-based methodologies for fast response services to distribution grids. Long-term ancillary services will provide the distributed network system operators and researchers with current BESS-based bulk-energy methods to improve network reliability and power quality and maximize revenue from renewable energy generation. The review presents a list of energy storage policies and BESS projects worldwide with a cost-benefit analysis. The challenges for deploying BESS in distribution grids recommended solutions for the implementation challenges, and future research directions are also presented.

The estimation of evapotranspiration (ETo) is one of the main tools for the control of crop growth and to make a rational use of water resources. To estimate this parameter accurately, it is necessary to have a daily measurement of four meteorological variables, these are: temperature, solar radiation, relative humidity and wind speed. It is not always possible to count on all the variables, that is why there are empirical methods that use a limited number of variables that make an approximate estimate of the ETo value. Each of these models are applicable to different regions with completely different climates. In this paper, a study has been carried out to define the model of ETo estimation that best adapts to the semi-arid region in South India. Two different datasets for the same period from different meteorological stations were used. In addition to the empirical methods for estimating ETo, computer models ANFIS (Adaptive Neuro-Fuzzy Inference System) were implemented. These models consist in the future estimation of a certain parameter by using current variables and a history of variables and past results. The results of this work show that ANFIS 11 model makes the best estimate with RMSD = 0.002 and r = 0.999. The RITCHIE method is the most suitable empirical model for this region, which reaches RMSD = 0.507 and r = 0.851. In addition, ranking of equations is elaborated for both datasets for daily estimates of ETo. Finally, comparison is made with the results for each case and thus confirm or reject the convenience of one model over the rest. To achieve this, a series of statistical indicators were used: Index of agreement (d), MAE (Mean absolute error), SEE (Standard error of estimate) and RMSD (Root mean square difference). Moreover, a sensitivity analysis was performed in order to compare and show the stability of the best models when an error is introduced within the input parameters. In this case, the empirical models demonstrate a better performance than the ANFIS models. This work demonstrates that the Ritchie method is a good estimator of the ETo value for a semi-arid region in southern India. In addition, the results of the ANFIS models are promising and could be used as estimation methods.

Choice of hybrid electric vehicles (HEVs) in transportation systems is becoming more prominent for optimized energy consumption. HEVs are attaining tremendous appreciation due to their eco-friendly performance and assistance in smart grid notion. The variation of energy storage systems in HEV (such as batteries, supercapacitors or ultracapacitors, fuel cells, and so on) with numerous control strategies create variation in HEV types. Therefore, choosing an appropriate control strategy for HEV applications becomes complicated. This paper reflects a comprehensive review of the imperative information of energy storage systems related to HEVs and procurable optimization topologies based on various control strategies and vehicle technologies. The research work classifies different control strategies considering four configurations: fuel cell-battery, battery-ultracapacitor, fuel cell-ultracapacitor, and battery-fuel cell- ultracapacitor. Relative analysis among different control techniques is carried out based on the control aspects and operating conditions to illustrate these techniques’ pros and cons. A parametric comparison and a cross-comparison are provided for different hybrid configurations to present a comparative study based on dynamic performance, battery lifetime, energy efficiency, fuel consumption, emission, robustness, and so on. The study also analyzes the experimental platform, the amelioration of driving cycles, mathematical models of each control technique to demonstrate the reliability in practical applications. The presented recapitulation is believed to be a reliable base for the researchers, policymakers, and influencers who continuously develop HEVs with energy-efficient control strategies.

This paper presents an in-depth analysis and investigation on the performance of static photovoltaic (PV) array configurations subjected to various partial shading conditions (PSCs). Under PSCs, the electrical characteristics of the PV modules are critically monitored and reasons for their behavioral changes are highlighted. By doing so, this study aims to improve the efficiency of PV systems by minimizing mismatch losses and determining the optimum array configuration which is characterized by the highest maximum power and lowest relative losses under PSCs. Besides, this study complements and carries forward the previous studies through the detailed analysis of each configuration subjected to various practically probable PSCs. Three different PV array sizes ( $5\times4$ , $5\times5$ , and $3\times10$ ) are used to analyze the results and performance under considered shading scenarios. MATLAB/Simulink platform is used to model and simulate the PV array using the single diode (5-parameters) model. In-depth analysis of current flow across cross-ties and bypass diodes activation shows that the diagonal shading pattern leads to lower power loss (PL). Besides, the Total Cross-Tied (TCT) configuration demonstrates superior performance under most of the PSCs compared to other configurations. These results provide valuable information about the performance of PV array which may lead to better estimation and prediction of global maximum power (GMP) generation of a PV system.

This paper is aimed to investigate the crushing response of single square honeycomb panels under quasi-static compression loading. Two types of materials are used in this study, which refers to 100 % polylactic acid (PLA) and 70 % PLA filled 30 % carbon fibre (PLA/CF). Single honeycomb panels were fabricated through additive manufacturing technique, and assembled using slotting technique. The effect of boundary factor on the single square honeycomb panels have been studied, which refers to none, single-side, double-side boundary conditions. The effect of material properties on the crushing response has also involved. For the tensile test, it was concluded that the PLA/CF specimen offered the higher young modulus with 428.75 MPa than 360.76 MPa of PLA specimen. For the quasi-static compression test, the compressive modulus and strength of the single honeycomb sandwich panel showed 489.69 MPa and 18.32 MPa with boundary type 1,which provided the highest value compared to other two boundary condition types. Moreover, the square honeycomb sandwich panels with PLA/CF material and type 3 boundary condition offered the better crushing performance on energy absorption (EA) with 66.42kJ and specific energy absorption (SEA) with 2282.47 kJ/kg. In addition, the crushing behaviour and failure mode were also involved and discussed in this study.

Clean and environment-friendly energy harvesting are of prime interest today as it is one of the key enablers in achieving the Sustainable Development Goals (SDGs) as well as accelerates social progress and enhances living standards. India, the second-most populous nation with a population of 1.353 billion, is one of the largest consumers of fossil fuels in the world which is responsible for global warming. An ever-increasing population is projected until 2050, and consequently, the energy demand in the upcoming decades will be co-accelerated by the rapid industrial growth. The Ministry of New and Renewable Energy (MNRE) with the support of National Institution for Transforming India (NITI) Aayog is working to achieve the Indian Government's target of attaining 175 GW through renewable energy resources. Many Indian states are currently increasing their renewable energy capacity in an objective to meet future energy demand. The review paper discusses in-depth about the three Indian states, namely Karnataka, Gujarat, Tamil Nadu, which pioneers the renewable energy production in India. The global energy scenario was discussed in detail with Indian contrast. Further, the barriers to the development of renewable energy generation and policies of the Indian government are discussed in detail to promote renewable energy generation throughout India as well as globally since the challenges are similar for other nations. This study analyzed various prospects of the country in renewable energy which has been done in a purpose to help the scholars, researchers, and policymakers of the nation, as it gives an insight into the present renewable energy scenario of the country.

Abstract Background This paper investigates the performance of a single-sloped pitched roof building-integrated photovoltaic (SSPR-BIPV) system. A typical rural building having a roof area of 60 sq. m is considered for the study. It was observed that the considered roof area could accommodate, nearly 7 kW BIPV system, and on the other side, most buildings seen in Malaysia are of the roof based constructed at different roof pitches. Considering this, a SSPR-BIPV is proposed and analyzed in seven different pitched roofs (the steepness of roof slopes are 15°, 18°, 22°, 30°, 35°, 40°, and 45°) under the tropical weather conditions in Malaysia. Results and discussion As per the study constraints, the 7 kW CdTe BIPV under seven different pitched roofs is capable of generating electricity within the range of 9510 to 7710 kWh. Performance ratio and energy losses for seven pitched roofs were identified to be in the range of 74.92 to 76.37% and − 23.63 to − 25.08% respectively. From the results, it is observed that in a BIPV system, as the building roof pitch increases, the energy yields and performance ratios tend to decrease, thereby increasing the overall energy losses. Conclusion Hence, this study reveals that there exists a considerable influence of pitched roof over the BIPV performance and suggests using lower roof pitch angles during the installation of BIPV roof especially in the case tropical weather conditions.

This paper evaluates and discusses the building-integrated photovoltaic (BIPV) system energy losses during the energy conversion process from sunlight into electricity. An approach based on partial performance ratio (P-PR) and cumulative performance ratio (C-PR) is used. The P-PR and C-PR are evaluated considering eight stages possible in the energy conversion stage in photovoltaic (PV) energy systems. A large-scale BIPV system is proposed by retrofitting the existing university buildings is investigated, and the observed results depict the impact of energy losses over the performance ratio decline. Results showed that the P-PR of the BIPV as per eight energy conversion stages varies between 85.6 and 100%. The C-PR varies between 74 and 100%, recording an overall system performance ratio (PR) of 74% under tropical weather conditions. The energy losses are varied within the range of −0.27 to −14.8% (−4 to −223 kWh/kWp) in different energy conversion stages. The total energy losses in the BIPV system account for 26% (−419 kWh/kWp). Based on the observations, reasons for various losses and their mitigation strategies are explored. Thus, we believe this study could provide useful information on the feasibility of considering retrofitting the university buildings with the BIPV modules.

The scope for water-based photovoltaic (WPV) installations has raised questions related to its performance in comparison to traditional land-based photovoltaic (LPV) installations. In literature, few studies exist, and they mainly highlighted the performance of WPV and LPV based on energy analysis but fails to give a detailed understanding. In this paper, the exergy based parametric approach is used to understand the realistic performance and the energy conversion process of the photovoltaic (PV) module in both water and land environments. Three amorphous silicon thin-film PV modules installed in ground-mount PV (GMPV), floating PV (FPV), and submerged PV (SPV) methods are considered for experimentation to understand the exergy performance. The experimental results show that the exergy efficiency is varied for each installation method. It is observed that the exergy efficiency of SPV is 3.07% higher than the FPV and 43.65% higher than GMPV installation methods, respectively. Overall, the thermodynamic analysis of the thin-film PV module provided in this study delivered a critical understanding of the performance, and this study will serve as a useful reference in selecting the best installation methods.

This paper presents the experimental study into the design and the electrical performance of a few novel curved blade vertical axis wind turbines. In the wind turbines, blade shape and quantity are crucial. In this study, curved wind blades are designed, fabricated, and used in developing the small-scale vertical axis wind turbines. Wind tunnel tests were carried out, and Results were presented for cases of vertical axis wind turbines with 2-blade, 3-blade, 4-blade configurations. Electrical parameters were monitored and compared for three different configurations. Results show that the curved blade wind turbine with a 3-blade configuration performs better when compared to the other two configurations.

Ethanol, as a fuel for compression ignition (CI) engine, has some advantages over diesel fuel, and these include the reduction of soot, carbon monoxide (CO) and unburned hydrocarbon (HC) emissions. However, there are few concerns about ignition properties, and emissions (especially the particulate and NOx). For minimizing the emissions from the ethanol-fueled compression ignition (EFCI) engine, the exhaust gas recirculation (EGR) technique is proposed. In this paper, the EGR technique is used where some amount of the exhaust gas is drawn off from the exhaust system, cooled, and redirected back into the cylinders. The experimental setup of the ethanol-fueled diesel engine (EFDE) with EGR is developed. Using the setup, the performance, emission, and combustion characteristics of EFDE in different scenarios of EGR (ethanol + coating, ethanol + coating+10% EGR, ethanol + coating+20% EGR) were carried out. We observe that the exhaust fills the combustion chamber; however, it is not involved in the combustion reaction that takes place in the cylinder due to its low oxygen content. Hence, the combustion process speed is reduced, with the result that the peak flame temperature in the combustion chamber is lowered. This dramatically affected the characteristics of the EFDE. The obtained results of EFDE scenarios were compared with the diesel scenario. Overall, it is observed that the variation in the %EGR rates has a significant impact on the performance, emission, and combustion characteristics of EFDE.

This article presents performance data concerning a 1MW crystalline photovoltaic (PV) plant installed in the semi-arid climate of India. Data includes the daily average samples from January 2012 to February 2016, related to solar irradiance on the plane of the array, electrical energy injected into the grid, reference yield, final yield, and the performance ratio. Furthermore, the decomposition time series for the performance ratio by applying the classical seasonal decomposition (CSD), Holt-Winters seasonal model (HW), and Seasonal and Trend decomposition using Loess (STL) is also provided for quantifying of the degradation rate of the PV system. The data are provided in the supplementary file included in this article. The dataset is related to the paper entitled “Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India.” [1].

This paper reports on how the technology behind cryptocurrency (Bitcoin) i.e. Blockchain could offer its services in distributed energy system (DES), noting on the issues related to operating conditions, energy generation monitoring, energy sharing and trading, financial flows, emission inventory, carbon emission trading and many more. Information on blockchain implication in DES is reported. Keywords: Blockchain, Distributed energy system (DES), Blockchain technology (BCT), IoT, Blockchain in energy

In this paper, five different types of water heating systems namely electric instantaneous, electric storage, natural gas instantaneous, natural gas storage, and heat pump are studied for Australian conditions. The study is carried out for understanding the life cycle cost of each system under similar water demand conditions. The focus is made on life-cycle cost (LCC) that include the running costs on an annual basis, and total running cost for the system life, maintenance cost; and greenhouse gas (GHG) emissions. The observations reveal that five systems had major differences in the upfront cost, running cost, and life-cycle cost, and GHG emissions. The observed LCC and GHG emissions for electric instantaneous, electric storage, natural gas instantaneous, natural gas storage, and heat pump are 9005 AU$, 12,724 AU$, 6955 AU$, 7479 AU$, and 8098 AU$, and 1862 tons of CO2, 2306 tons of CO2, 851 tons of CO2, 917 tons of CO2, and 842 tons of CO2 respectively. Keywords: Solar water heater, Life-cycle cost, Heat pump, Gas heated water, Electrically heated water, GHG emissions from water heating

We report the preliminary results of the degradation study of large-scale photovoltaic (PV) system installed using 1006.74 kWp crystalline silicon (c-Si) PV array. A linear least square (LLS) fitting method is adopted, and the degradation rate (DR) is calculated for the four years based on monitored operational data in semi-arid climates of India. Keywords: Crystalline silicon, PV modules, Degradation rate, Performance ratio, PV in semi-arid climates, Large-scale PV, Linear least square

Random forests estimates (RFE) based machine learning algorithm is proposed for forecasting the three years ahead solar irradiance. Accordingly, the corresponding degradation rate (DR) influenced energy potentials are evaluated. Here, the DR of amorphous silicon (a-Si) PV system is estimated based on the simulated performance ratios with historical weather data. Prediction of energy potentials is helpful in decision making on improving the solar power project implementations in selected tropical savanna climates region of south India. Keywords: Thin film (a-Si) PV, Machine learning, Degradation rate, Solar forecasting, Year ahead PV power, Performance ratio

A new concept called Wavevoltaics is proposed by integrating the photovoltaic (PV) cells over the vacant open sky surface of wave devices. This article is intended to deliver the conceptual view of modelling this device and a method to estimate the available power potential and to evaluate the performance of Wavevoltaic device. Keywords: Wavevoltaics, Novel wave device, PV on wave device, Novel PV installation, Wave floats for PV, PV in water, Aquavoltaics, Floating PV

Power quality (PQ) has become an important topic in today’s power system scenario. PQ issues are raised not only in normal three-phase systems but also with the incorporation of different distributed generations (DGs), including renewable energy sources, storage systems, and other systems like diesel generators, fuel cells, etc. The prevalence of these issues comes from the non-linear features and rapid changing of power electronics devices, such as switch-mode converters for adjustable speed drives and diode or thyristor rectifiers. The wide use of these fast switching devices in the utility system leads to an increase in disturbances associated with harmonics and reactive power. The occurrence of PQ disturbances in turn creates several unwanted effects on the utility system. Therefore, many researchers are working on the enhancement of PQ using different custom power devices (CPDs). In this work, the authors highlight the significance of the PQ in the utility network, its effect, and its solution, using different CPDs, such as passive, active, and hybrid filters. Further, the authors point out several compensation strategies, including reference signal generation and gating signal strategies. In addition, this paper also presents the role of the active power filter (APF) in different DG systems. Some technical and economic considerations and future developments are also discussed in this literature. For easy reference, a volume of journals of more than 140 publications on this particular subject is reported. The effectiveness of this research work will boost researchers’ ability to select proper control methodology and compensation strategy for various applications of APFs for improving PQ.

This article presents the experimental results of the underwater performance of amorphous silicon (a-Si) thin-film photovoltaic (TFPV) module. Electrical performance characteristics (current, voltage, power input, power output, and power conversion efficiency) of a-Si TFPV are evaluated considering the two installation conditions of Submerged Photovoltaics (SPV): shallow and deep waters. Experimental results showed that the a-Si TFPV could convert sun energy into electricity even in underwater environments. It is observed that there is a little drop in power outputs in deep waters than to shallow waters due to the variation in incident sunlight on to the photovoltaic (PV) module. Based on the observations, few possible applications for underwater PV system are explored. The article is concluded by highlighting few critical points for future research. Keywords: Power conversion efficiency, PV performance, Floating photovoltaics, Sub-merged PV, PV in deep waters, Underwater PV applications

The alleged reliability has led the longest warranty period for Photovoltaic (PV) modules up to 20–25 years; it becomes possible after understanding the failure mode and degradation analysis of PV module. Failure mode decreases the performance of the PV module throughout the long-term outdoor exposure. The main objective of the present study is to identify the failure mechanism and failure mode of solar PV modules and their impact on degradation in operating conditions. Assessment of previous studies on rate indicates the highest performance losses at initial stage of outdoor exposure and a degradation drop-off of 0.014% per year. In this context, risk priority number (RPN) analysis is carried out to identify the severity of the failure mode, which affect the system performance for c-Si technologies. However, hot spot and de-lamination are degradation modes related to safety issue with lower value of RPN

In the superplastic forming process (SFP), exceptional formability can be achieved. The formability in SFP is far superior when compared to traditional methods. Through SFP, the most complex shapes with high dimensional tolerance were formed. In this article, bi-axial superplastic forming was conducted after the rolling process on various percentages of silicon carbide composite (SiCp) particles in the gas pressure forming tester. Results of this study are mainly focused on finding favorable forming parameters (temperature, pressure, and time) for the superplastic formability of various Al/SiCp prepared by the stir casting process. Keywords: Al/SiCp, Superplastic forming, Aluminium alloy, Temperature, Pressure

Metal machining is always associated with the generation of heat. Though heat generated in metal cutting is carried away by the chips, continuous engagement of tool with the workpiece increase the temperature at the tool–work interface and spreads to its neighborhood. In this article, a novel method of removing heat from the tool-work interface is implemented with the use of phase change heat transfer device (heat pipes) and applied for drilling operation carried out by U-drills. A custom built U-drill is designed with a heat pipe placed axially. The temperature of the tool-work interface is estimated analytically using the experimental values of cutting force components. Experiments were conducted at various speeds and feed to measure the performance of the U-drill. It is found that the proposed method of heat pipe (HP) embedded drill bit is able to remove heat and operates at a lower temperature at the tool-chip interface. Results show that a considerable reduction in the cutting force generated at various machining conditions. The heat pipe embedded U-drill will be useful in drilling components made of composite materials where coolants are not widely employed. It was evident from the experimental works that the inclusion of heat pipe in U-drill has brought forth a reduction of cutting temperature of about 30%. This also brought forth a better cutting parameter, and a significant reduction in tool wear increased the tool life which invariably decreases the production cost and time. Keywords: Heat pipe, U-drill, Cutting temperature, Surface finish, Metal machining

Solar photovoltaic trees (SPVT's) are chosen as an alternative option for electricity generation due to numerous benefits (especially in land utilization, urban infrastructure, and landscaping). Currently, SPVT's are available in many designs, and one among them is the novel phyllotaxy pattern. Technically, SPVT's seem to be more reliable and cost-effective. The expected average lifetime of the photovoltaic (PV) modules used in SPVT's is around 25 years. Once, the lifetime is over, these SPVT's must undergo a waste management process, and the scope for recycling is very high. Even though the scope for PV waste recycling is very high, many recyclers face the problems especially in estimating material recovery potential. In this paper, thermal and chemical treatments based end-of-life (EOL) method is used to estimate the material recovery potential from the phyllotaxy pattern-based SPVT. Initial estimations on the total embodied materials of the SPVT system is evaluated based on the components material fraction. Next, under the applied EOL management method, material recovery potential is estimated as per the material (aluminum, copper, glass, silver, and steel). Lastly, a few limitations with this EOL method are highlighted, and presented results aim to serve as useful data for the recyclers to have decisions. Keywords: Solar tree, Phyllotaxy patterns, Photovoltaics, Crystalline modules, PV waste, End-of-life management, Material recovery, Recycling of PV waste, Thermal and chemical treatments

Superplastic forming is primarily used for thin-walled and complex shape structures. This process is much successful in industries mainly for the fabrication of intricate components used for aerospace and automobile industries. In this paper, superplastic forming behavior and microstructural evolution in Al/5%SiCp composites were studied at different temperatures through the hot tensile test. In a hot tensile test, a maximum elongation (i.e., 227%) was obtained at the 580 °C temperature condition. When the temperature was more than 580 °C, the elongation was reduced to less than 100%. Similarly, when the temperature is adjusted to a value lesser than 550 °C, the reduction in the elongation is seen which is less than 100%. Here, the microstructure study of the test samples is studied through the optical microscope. From the microstructure analysis, the grain refinement is taken place by dynamic re-crystallization and the intergranular deformation with grain boundary sliding is observed. It is understood that, the dynamic re-crystallization and grain boundary sliding increases the percentage of elongation and on the other hand the ductility reduces due to the presence of excessive liquid phase at the grain boundaries. Keywords: Superplastic forming, Al/SiCp composites, Tensile test, Microstructure analysis, Aluminum alloy, AA6063/SiCp

This article predicts the performance, energy loss, and degradation of a 200 kW roof-integrated crystalline photovoltaic (PV) system installed at IRB Complex-5, Chandigarh in the Northern part of India. PVsyst simulation tool is used for predicting the energy generation, and energy loss. Analysis of the energy generation and various input parameters are carried out for evaluating the capacity factor (CF), performance ratio (PR), and efficiencies. For a detailed analysis of the energy losses, a three-stage approach (sunlight reaching onto the PV array, sunlight into DC electricity conversion, and DC to AC electricity conversion) is used. The light-induced degradation (LID), which is possible in the PV modules in the second stage (sunlight into DC electricity conversion) is predicted using the PVsyst simulation modeling. Apart from this, the possible degradation rate (DR) in the crystalline PV systems is considered based on the All-India survey of PV module reliability reports. The predicted results show that 292954 kWh of energy generation is possible on an annual basis from the planned PV system. The system is estimated to operate with a yearly CF, PR, and energy losses as 16.72%, 77.27%, and −26.5% respectively. The estimated DR of the PV system would lie between −0.6 to −5%/year, and the possible LID is −2.5%/year. Finally, the energy losses due to LID, and DR are predicted as −8109.99 kWh/year, and −1757.724 to −14647.7 kWh/year respectively. Keywords: Crystalline solar, Energy loss, Light-induced degradation (LID), Degradation rate, Roof-integrated PV, Performance prediction, PVsyst, Model solar city

This paper compares the performance of photovoltaics (PV) for building applications in two configurations: building applied photovoltaics (BAPV) and building integrated photovoltaics (BIPV). A 32.7 kWp PV capacity is proposed for a roof building and its performance in BAPV and BIPV configurations with three PV technologies namely crystalline (c-Si), CIS, and CdTe is analyzed. The standard methodology with performance parameters such as energy yield (EY), yield factor (YF), capacity utilization factor (CUF), performance ratio (PR), and losses is used. It is found that the EY, YF, and year to year energy production variability of BIPV and BAPV technologies varies from 43,700–46,800 kW h, 1336.39–1431.19 kW h/kWp, and 1910–2100 kW h respectively. The CUF and PR vary from 15.25–16.33%, and 72.23–77.36% respectively. Irrespective of PV configuration and technology, observed losses due to the angle of incidence, spectral effects, effects of change in irradiance and module temperatures are observed to be − 2.8%, − 1 to − 5%, and − 7.4 to − 13.6% respectively. Total system losses range from −22.6 to 27.8% causing a fair amount of loss in the PV efficiency. Among, three PV technologies, CdTe is observed to perform better than CIS, and c-Si. Keywords: PV modules, BAPV and BIPV, Performance ratio, Capacity utilization factor, Building integrated photovoltaics, Building applied photovoltaics

China intends to develop its renewable energy sector in order to cut down on its pollution levels. Concentrated solar power (CSP) technologies are expected to play a key role in this agenda. This study evaluated the technical and economic performance of a 100 MW solar tower CSP in Tibet, China, under different heat transfer fluids (HTF), i.e., Salt (60% NaNO3 40% KNO3) or HTF A, and Salt (46.5% LiF 11.5% NaF 42% KF) or HTF B under two different power cycles, namely supercritical CO2 and Rankine. Results from the study suggest that the Rankine power cycle with HTF A and B recorded capacity factors (CF) of 39% and 40.3%, respectively. The sCO2 power cycle also recorded CFs of 41% and 39.4% for HTF A and HTF B, respectively. A total of 359 GWh of energy was generated by the sCO2 system with HTF B, whereas the sCO2 system with HTF A generated a total of 345 GWh in the first year. The Rankine system with HTF A generated a total of 341 GWh, while the system with B as its HTF produced a total of 353 GWh of electricity in year one. Electricity to grid mainly occurred between 10:00 a.m. to 8:00 p.m. throughout the year. According to the results, the highest levelized cost of energy (LCOE) (real) of 0.1668 USD/kWh was recorded under the Rankine cycle with HTF A. The lowest LCOE (real) of 0.1586 USD/kWh was obtained under the sCO2 cycle with HTF B. In general, all scenarios were economically viable at the study area; however, the sCO2 proved to be more economically feasible according to the simulated results.

Farm machinery like water sprinklers (WS) and pesticide sprayers (PS) are becoming quite popular in the agricultural sector. The WS and PS are two distinct types of machinery, mostly powered using conventional energy sources. In recent times, the battery and solar-powered WS and PS have also emerged. With the current WS and PS, the main drawback is the lack of intelligence on water and pesticide use decisions and autonomous control. This paper proposes a novel multi-purpose smart farming robot (MpSFR) that handles both water sprinkling and pesticide spraying. The MpSFR is a photovoltaic (PV) powered battery-operated internet of things (IoT) and computer vision (CV) based robot that helps in automating the watering and spraying process. Firstly, the PV-powered battery-operated autonomous MpSFR equipped with a storage tank for water and pesticide drove with a programmed pumping device is engineered. The sprinkling and spraying mechanisms are made fully automatic with a programmed pattern that utilizes IoT sensors and CV to continuously monitor the soil moisture and the plant’s health based on pests. Two servo motors accomplish the horizontal and vertical orientation of the spraying nozzle. We provided an option to remotely switch the sprayer to spray either water or pesticide using an infrared device, i.e., within a 5-m range. Secondly, the operation of the developed MpSFR is experimentally verified in the test farm. The field test’s observed results include the solar power profile, battery charging, and discharging conditions. The results show that the MpSFR operates effectively, and decisions on water use and pesticide are automated.

Harnessing energy from the sunlight using solar photovoltaic trees (SPVTs) has become popular at present as they reduce land footprint and offer numerous complimentary services that offset infrastructure. The SPVT’s complimentary services are noticeable in many ways, e.g., electric vehicle charging stations, landscaping, passenger shelters, onsite energy generated security poles, etc. Although the SPVT offers numerous benefits and services, its deployment is relatively slower due to the challenges it suffers. The most difficult challenges include the structure design, the photovoltaic (PV) cell technology selection for a leaf, and uncertainty in performance due to weather parameter variations. This paper aims to provide the most practical solution supported by the performance prioritization approach (PPA) framework for a typical multilayered SPVT. The proposed PPA framework considers the energy and sustainability indicators and helps in reporting the performance of a multilayered SPVT, with the aim of selecting an efficient PV leaf design. A three-layered SPVT (3-L SPVT) is simulated; moreover, the degradation-influenced lifetime energy performance and carbon dioxide (CO2) emissions were evaluated for three different PV-cell technologies, namely crystalline silicon (c-Si), copper indium gallium selenide (CIGS), and cadmium telluride (CdTe). While evaluating the performance of the 3-L SPVT, the power conversion efficiency, thermal regulation, degradation rate, and lifecycle carbon emissions were considered. The results of the 3-L SPVT were analyzed thoroughly, and it was found that in the early years, the c-Si PV leaves give better energy yields. However, when degradation and other influencing weather parameters were considered over its lifetime, the SPVT with c-Si leaves showed a lowered energy yield. Overall, the lifetime energy and CO2 emission results indicate that the CdTe PV leaf outperforms due to its lower degradation rate compared to c-Si and CIGS. On the other side, the benefits associated with CdTe cells, such as flexible and ultrathin glass structure as well as low-cost manufacturing, make them the best acceptable PV leaf for SPVT design. Through this investigation, we present the selection of suitable solar cell technology for a PV leaf.

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.

Nowadays, the application of distributed energy sources (DES) has been extensively employed to serve the power system by supplying the power into the grid and improving the power quality (PQ). Therefore, DES is one solution that can efficiently overcome the energy crisis and climate change problems. The DES, such as solar photovoltaic (PV), wind turbine (WT), and battery energy storage systems (BESS), are incorporated to form the microgrid (MG), which are interfaced with the power system. However, interfacing MG to the power system is undoubtedly a big challenge. Therefore, more focus is required on the control strategy to control the MG with the power system. To address the PQ problems, a controlled MG integrated with a hybrid shunt active power filter (HSAPF) is provided in this work. For controlling the MG integrated HSAPF, different control strategies are applied. In this work, a learning-based incremental conductance (LINC) technique is used as a maximum power point tracking (MPPT) for tracking the maximum power in PV and WT. The voltage source inverter (VSI) of HSAPF is controlled using a wavelet-based technique with a synchronous reference frame (SRF). The main focus is to improve the PQ by compensating the harmonics and regulating the reactive power in both grid-interactive and islanded condition and also supply continuous and adequate power to the non-linear load. The power system model has been developed with MATLAB/Simulink tool, which shows the efficiency of the proposed method. The results obtained have been satisfactorily under various operating conditions and can be validated further using the real-time dSPACE.

Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

Even in today’s modern electric grid infrastructure, the uncertainty in the power supply is more often seen and is mainly due to power outages. The reasons for power outages might be any of the following: extreme weather events, asset failure, natural disasters, power surges, acute accidents, and even operational errors by the workforce. Such uncertain situations are permitting us to think of it as a resilience problem. In most cases, the power outages may last from a few minutes to a few weeks, depending on the nature of the resilience issue and the power supply system (PSS) configuration. Therefore, it is imperative to understand and improve the resilience of a PSS. In this paper, a four-component resilience framework is proposed to study and compare the resilience of three different PSS configurations of residential electricity users (REUs) considering the realistic power outage conditions in the humid subtropical ecosystem. The proposed PSS configurations contain electric grid (EG), natural gas power generator (NGPG), battery energy storage (BES), and photovoltaics (PV) as the assets. The three PSS configurations of a REUs are EG + BES, EG + NGPG + BES, and EG + PV + BES, respectively, and in these, one REU is only the consumer and the other two REUs are prosumers. By using the proposed framework, simulations are performed on the three PSS configuration to understand the increasing load resiliency in the event of a power outage. Also, a comparative techno-economic and life cycle based environmental assessment is performed to select the most resilient PSS configuration among the EG + BES, EG + NGPG + BES, and EG + PV + BES for an REU. From the results, it was established that EG + PV + BES configuration would enhance the power resilience of an REU better than the other two PSS configurations. Besides, it is also observed that the identified resilient PSS configuration is cost-effective and environmentally efficient. Overall, the proposed framework will enable the REUs to opt for the PSS configuration that is resilient and affordable.

The semi-transparent photovoltaic (STPV) module is an emerging technology to harness the solar energy in the building. Nowadays, buildings are turning from energy consumers to energy producers due to the integration of the STPV module on the building envelopes and facades. In this research, the STPV module was integrated on the rooftop window of the experimental room at Kovilpatti (9°10′0″ N, 77°52′0″ E), Tamil Nadu, India. The performance of the STPV modules varies with respect to the geographical location, incident solar radiation, and surface temperature of the module. The surface temperature of the STPV module was regulated by the introduction of the mixture of graphene oxide and sodium sulphate decahydrate (Na2SO4·10H2O). The various concentration of the graphene oxide was mixed together with the Na2SO4·10H2O to enhance the thermal conductivity. The thermal conductivity of the mixture 0.3 concentration was found to be optimum from the analysis. The instantaneous peak temperature of the semi-transparent photovoltaic phase change material (STPV-PCM) module was reduced to 9 °C during summer compared to the reference STPV. At the same time, the energy conversion efficiency was increased by up to 9.4% compared to the conventional STPV module. Due to the incorporation of the graphene oxide and Na2SO4·10H2O, the daily output power production of the STPV module was improved by 12.16%.

The bearingless concept is a plausible alternative to the magnetic bearing drives. It provides numerous advantages like minimal maintenance, low cost, compactness and no requirement of high-performance power amplifiers. Controlling the rotor position and its displacements under parameter variations during acceleration and deceleration phases was not effective with the use of conventional controllers like proportional–integral–derivative (PID) and fuzzy-type controllers. Hence, to get the robust and stable operation of a bearingless switched reluctance motor (BSRM), a new robust dynamic sliding mode controller has been proposed in this paper, along with a sensorless operation using a sliding ode observer. The rotor displacement tracking error functions and speed tracking error functions are used in the designing of both proposed methods of the sliding mode switching functions. To get a healthy and stable operation of the BSRM, the proposed controller’s tasks are divided into three steps. As a first step, the displaced rotor in any one of the four quadrants in the air gap has to pull back to the centre position successfully. The second step is to run the motor at a rated speed by exciting torque phase currents, and finally, the third step is to maintain the stable and robust operation of the BSRM even under the application of different loads and changes of the motor parameters. Simulation studies were conducted and analysed under different testing conditions. The suspension forces, rotor displacements, are robust and stable, and the rotor is pulled back quickly to the centre position due to the proposed controller’s actions. The improved performance characteristics of the dynamic sliding mode controller (DSMC)-based sliding mode observer (SMO) was compared with the conventional sliding mode controller (SMC)-based SMO.