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The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU's redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework. [ABSTRACT FROM AUTHOR]

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller's robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, Tp, and Tij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively. [ABSTRACT FROM AUTHOR]

In this research project, the optimal design and design evaluation of a hybrid microgrid based on solar photovoltaics, wind turbines, batteries, and diesel generators were performed. The conventional grid-tied mode was used in addition to dispatch strategy-based control. The study's test location was the loads in the Electrical, Electronic and Communication Engineering (EECE) department at Pabna University of Science and Technology (PUST), Pabna, Bangladesh. DIgSILENT PowerFactory was employed to determine the power system-based behaviors (electrical power, current, voltage, and frequency) of the proposed hybrid system, while a derivative-free algorithm was used for the expense, optimal size, and emission assessments. While developing the microgrid, load following (LoF) and cycle charging (CyC) control were employed. The microgrid is supposed to have a 23.31 kW peak load requirement. The estimated microgrid's levelized cost of energy (LE), its net present cost (NC), its operating cost, and its annual harmful gas emissions were estimated in this work. Additionally, since the microgrid is grid-connected, the amount of energy output that might be exported to the grid was also estimated, which will potentially increase during blackouts. The power system responses found in this study ensure that the various microgrid components' voltage, frequency, current, and power outcomes are steady within the designated range, making the microgrid practical and robust. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RESs) and energy storage schemes (ESSs) integrated into a microgrid (MG) system have been widely used in power generation and distribution to provide a constant supply of electricity. The power electronics converters, particularly the bidirectional power converters (BPCs), are promising interfaces for MG infrastructure because they control the power management of the whole MG system. The controller of BPCs can be designed using several different control strategies. However, all the existing controllers have system stability, dynamics, and power quality issues. Therefore, this study demonstrates the development of an LCL-filtered grid-connected bidirectional AC–DC converter's (BADC) control strategy based on voltage-oriented control (VOC) to overcome these issues. The proposed VOC-based inner current control loop (ICCL) is implemented in synchronous dq-coordinate with the help of proportional-integral (PI) controllers. An observer-based active damping (AD) is also developed in order to estimate the filter capacitor current from the capacitor voltage instead of directly measuring it. This developed AD system helps to damp the resonance effect of the LCL filter, improves system stability, and also eliminates the practical challenges of measuring capacitor current. The proposed controller with AD is able to realize bidirectional power transfer (BPT) with reduced power losses due to the elimination of passive damping and improved power quality, system dynamics, and stability. The mathematical modeling of the suggested system was developed, and the structure of the system model was established in the MATLAB/Simulink environment. The performance of the proposed system was validated with real-time software-in-the-loop (RT-SIL) simulation using the OPAL-RT simulator for a 16 kVA converter system. The real-time (RT) simulation results show that the BADC with the proposed control scheme can provide better dynamic performance and operate with tolerable total harmonic distortion (THD) of 2.62% and 2.71% for inverter and rectifier modes of operation, respectively. [ABSTRACT FROM AUTHOR]

Currently, the generation of energy relies primarily on the usage of harmful fossil fuels all over the world. New technologies, on the other hand, have enabled renewable energy resources to have a reduced environmental effect, greater long‐term sustainability, and, in some cases, to be cheaper than fossil fuels. Microgrids that use renewable resources can deliver low‐cost clean electricity while also improving local resiliency. They do, however, have certain issues in terms of regulating internal frequency and voltage within a microgrid, which is hampered by the intermittent nature of renewable sources. An islanded Microgrid for San Andres, Colombia has been designed for 24.57 kW peak load composed of a PV‐wind‐storage system by considering dispatch strategy based control in Homer Pro and assessed utilizing Simulink/MATLAB. Lastly, a PLC‐SCADA system along a HMI (Human‐Machine Interface) in C# was created to monitor the Microgrid in real‐time and produce an alarm as the microgrid elements are not operating within their ideal range of operational. The data from HMI is automatically uploaded to a cloud database. This work incorporates the dispatch control based techno‐economic design, assessment and a complete supervisory control strategy for an optimum microgrid. [ABSTRACT FROM AUTHOR]

Rural electrification is necessary for both the country's development and the well-being of the villagers. The current study investigates the feasibility of providing electricity to off-grid villages in the Indian state of Odisha by utilizing renewable energy resources that are currently available in the study area. However, due to the intermittent nature of renewable energy sources, it is highly improbable to ensure a continuous electricity supply to the off-grid areas. To ensure a reliable electricity supply to the off-grid areas, three battery technologies have been incorporated to find the most suitable battery system for the study area. In addition, we evaluated various demand side management (DSM) techniques and assessed which would be the most suitable for our study area. To assess the efficiency of the off-grid system, we applied different metaheuristic algorithms, and the results showed great promise. Based on our findings, it is clear that energy-conservation-based DSM is the ideal option for the study area. From all the algorithms tested, the salp swarm algorithm demonstrated the best performance for the current study. [ABSTRACT FROM AUTHOR]

The integration of plug-in electric vehicles (PEVs) in residential distribution networks demands a significant amount of electrical load where random and uncoordinated charging affects the quality and performance of the distribution network. Random and uncoordinated charging may increase the peak demand and can increase stress on critical network assets such as line, transformer, and switching devices. Moreover, the charging of PEVs in a low network reduces the voltage of the system below the lower limit. On the other hand, using PEVs as storage in the V2G mode can improve the network condition. Therefore, it is critical to properly manage the charging and discharging operation of PEVs. This paper proposes a multi-objective-based charging and discharging coordination of PEVs with the operation of the capacitor and on-load tap changer (OLTC). With the proposed strategy, the distribution network is operated safely, and charging is ensured for all PEVs connected to the network. The main consideration of this research is to reduce the daily power loss, operational cost, and voltage deviation of the system. The metaheuristic optimization binary firefly algorithm (BFA) has been applied to coordinate PEV charging and discharging as well as capacitor and OLTC operation in the system. A modified IEEE 31 bus 23 kV distribution system is used to implement the proposed strategy. From the obtained results, it is found that the combined PEV charging and discharging coordination with capacitor and OLTC operation reduces the power loss and cost by 34.16% and 12.68%, respectively, with respect to uncoordinated charging and enhances the voltage condition of the network. [ABSTRACT FROM AUTHOR]

The heterogeneous network (HetNet) is a specified cellular platform to tackle the rapidly growing anticipated data traffic. From a communications perspective, data loads can be mapped to energy loads that are generally placed on the operator networks. Meanwhile, renewable energy-aided networks offer to curtailed fossil fuel consumption, so to reduce the environmental pollution. This paper proposes a renewable energy based power supply architecture for the off-grid HetNet using a novel energy sharing model. Solar photovoltaics (PV) along with sufficient energy storage devices are used for each macro, micro, pico, or femto base station (BS). Additionally, a biomass generator (BG) is used for macro and micro BSs. The collocated macro and micro BSs are connected through end-to-end resistive lines. A novel-weighted proportional-fair resource-scheduling algorithm with sleep mechanisms is proposed for non-real time (NRT) applications by trading-off the power consumption and communication delays. Furthermore, the proposed algorithm with an extended discontinuous reception (eDRX) and power saving mode (PSM) for narrowband internet of things (IoT) applications extends the battery lifetime for IoT devices. HOMER optimization software is used to perform optimal system architecture, economic, and carbon footprint analyses while the Monte-Carlo simulation tool is used for evaluating the throughput and energy efficiency performances. The proposed algorithms are validated through the practical data of the rural areas of Bangladesh from which it is evident that the proposed power supply architecture is energy-efficient, cost-effective, reliable, and eco-friendly. [ABSTRACT FROM AUTHOR]

Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system's output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively. [ABSTRACT FROM AUTHOR]

A nonlinear damping controller using partial feedback linearization (PFL) technique is designed with an aim to mitigate subsynchronous resonance (SSR) in series-compensated doubly-fed induction generator (DFIG)-based wind farms, which is integrated to simplified Nordic power system. The controller using PFL technique is implementable into a partially linearized form of a system with DFIG wind farm that results in reduced system order and small computational burden. The derivation of control law does not also demand thorough knowledge of nonlinear and ambiguous system dynamics. The proposed controller design approach has four distinguished steps: 1) scrutinizing partial linearizability, 2) performing partial linearization, 3) assessing stability of internal system dynamics, and 4) obtaining control laws. Small-signal stability analysis is performed that includes electrical damping analysis and eigenvalue analysis. The controller is found to demonstrate a superior performance in SSR mitigation. Time-series simulation is carried out to confirm the veracity of the outcomes obtained from the small-signal stability analysis. [ABSTRACT FROM PUBLISHER]

Optimising voltage levels to a controlled stable level at a facility can not only reduce the cost of energy but also enhance equipment performance, prolong equipment life, reduce maintenance costs and reduce greenhouse gas emissions. Voltage optimisation (VO) technology has been widely used in a number of different industries locally and internationally, but not to a large extent within the red meat processing sector in Australia. To determine whether VO technology can be implemented, and whether it is technically and economically viable for red meat processing sites, this study investigated, through case study analyses, the potential effectiveness of VO technology in Australian abattoirs. Through an extensive literature survey, the study initially explored the need and considerations of deploying VO technologies at a typical red meat processing plant. To determine the advantages of using VO technology the study then performed site analyses to investigate power quality (PQ) issues, such as voltage regulation, harmonics and power factor, at two typical medium-sized abattoirs, one in Western Australia and another in Queensland. Finally, an economic assessment of the use of VO in the red meat processing industry was undertaken to identify the potential electricity savings and payback periods. From the case study analyses, it is evident that power quality issues, such as under voltage, overvoltage, and harmonic distortion, can be reduced and significant energy savings can be achieved with the optimum selection of VO technology and voltage level. The outcomes of this study will enable engineering and operations staff to be better informed about the economic and technical benefits of (and possible issues with) using VO technologies in an abattoir. [ABSTRACT FROM AUTHOR]

Growing demand of energy consumption, subsequent increase in energy generation costs, and increased greenhouse gas (GHG) emissions, as well as global warming from the conventional energy sources, encourages interest worldwide to bring a higher percentage of renewable energy sources such as biogas into the energy mix to build a climate friendly environment for the future. Moreover, due to high investment and maintenance costs, governments are not providing enough support for grid extension and delivering electricity to remote locations or rural areas, in particular, in under-developing countries like Bangladesh. Therefore, this paper presents an Energy Neutral Home System (ENHS) that can meet all its energy requirements from low-cost, locally available, nonpolluting biogas generated from animal waste, in particular, chicken and cow manure. The proposed ENHS has been developed for rural community, typically an area of 200 families, and will not only provide cooking gas and sustainable and affordable power supply to the community with low emissions, but will also facilitate high quality fertilizer for agricultural purposes. In-depth analysis clearly demonstrates that the proposed ENHS not only offers electricity and cooking gas to the community with the lowest costs, but also reduces the energy crisis and GHG emissions and can play an active role in developing socio-economic infrastructure of rural communities in Bangladesh in many ways. [ABSTRACT FROM AUTHOR]

The abundance, availability, and climate-friendly characteristics of solar photovoltaic (PV) energy encourage nations around the globe to adopt it to assist in overcoming global warming as well as build a sustainable society for the future. The intermittent nature of solar energy generation and the associated power electronic inverters with connected consumer loads creates a number of potential challenges in integrating large-scale PV into the grid that affects power quality of the distribution networks. This paper investigates the impacts of varying PV integration into the grid through experimental and simulation studies. Initially, several experiments were conducted with varying PV penetration and load conditions using the Renewable Energy Integration Facility at CSIRO, Newcastle, Australia. Later, a simulation model was developed that mimics the experimental facility used at CSIRO to investigate the adverse impacts on integrating large-scale PV into the grid using the power system simulation software PSS Sincal. Experimental and simulation analyses clearly indicate that integration of PV into the grid causes power quality issues such as voltage instability, harmonic injection, and low power factor into the networks and the level of these impacts increases with the increase of PV penetration. [ABSTRACT FROM AUTHOR]

The advancement in solar photovoltaic (PV) technology, the cost and efficiency of PVs have encouraged users worldwide to adopt more and more PVs as it is free from greenhouse gas emissions and unlimited in nature. Integration of roof-top solar PV systems is currently emerging rapidly in Australia as the governments are giving attractive incentives and encouraging households to build a sustainable climate-friendly society for the future. The key major barriers to the integration of roof-top solar PV systems are the uncertainties in the performance of the low voltage distribution network due to the intermittent nature of solar PV sources. In this paper, a model was developed to investigate the potential technical impacts of integrating roof-top solar PV systems into the low voltage distribution network in a subtropical climate. The results show that integration of roof-top solar PV in the customer premises causes uncertainties such as voltage fluctuations, phase unbalance, distribution transformer overloading, reactive power compensation, and harmonic injections that detract the overall power quality of the typical distribution network. [ABSTRACT FROM AUTHOR]

Recent advances in wireless sensor networking (WSN) techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems. These have the potential for use in the monitoring of railway signaling systems and rail tracks. Energy efficiency is one of the most important design factors for the WSNs as the typical sensor nodes are equipped with limited power batteries. In earlier research, an energy-efficient cluster-based adaptive time-division multiple-access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed by the authors for the purpose of communication between the sensors placed in a railway wagon. This paper proposes another new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period. In addition to railway applications, the EA-TDMA and E-BMA protocols are suitable for generic wireless data communication purposes. Both analytical and simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented in this paper to demonstrate the superiority of the EA-TDMA and E-BMA protocols. [ABSTRACT FROM PUBLISHER]

This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally sized by minimizing the net present cost of electricity for four options of electricity rates. A practical model was developed by considering grid constraints, daily supply of charge of electricity, salvation value and degradation of PV and BESS, actual annual data of load and solar, and current market price of components. A rule-based energy management system was examined for GCHs to control the power flow among PV, BESS, load, and grid. Various sensitivity analyses are presented to examine the impacts of grid constraint and electricity rates on the cost of electricity and the sizes of the components. Although the capacity optimization model is generally developed for any case study, a grid-connected house in Australia is considered as the case system in this paper. It is found that the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC compared to other configuration and options. The optimal capacities of rooftop PV and BESS were obtained as 9 kW and 6 kWh, respectively, for the PV-BESS configuration with TOU-Flat according to two performance metrices: net present cost and cost of electricity. [ABSTRACT FROM AUTHOR]

Tracking performance and stability play a major role in observer design for speed estimation purpose in motor drives used in vehicles. It is all the more prevalent at lower speed ranges. There was a need to have a tradeoff between these parameters ensuring the speed bandwidth remains as wide as possible. This work demonstrates an improved static and dynamic performance of a sliding mode state observer used for speed sensorless 3 phase induction motor drive employed in electric vehicles (EVs). The estimated torque is treated as a model disturbance and integrated into the state observer while the error is constrained in the sliding hyperplane. Two state observers with different disturbance handling mechanisms have been designed. Depending on, how they reject disturbances, based on their structure, their performance is studied and analyzed with respect to speed bandwidth, tracking and disturbance handling capability. The proposed observer with superior disturbance handling capabilities is able to provide a wider speed range, which is a main issue in EV. Here, a new dimension of model based design strategy is employed namely the Processor-in-Loop. The concept is validated in a real-time model based design test bench powered by RT-lab. The plant and the controller are built in a Simulink environment and made compatible with real-time blocksets and the system is executed in real-time targets OP4500/OP5600 (Opal-RT). Additionally, the Processor-in-Loop hardware verification is performed by using two adapters, which are used to loop-back analog and digital input and outputs. It is done to include a real-world signal routing between the plant and the controller thereby, ensuring a real-time interaction between the plant and the controller. Results validated portray better disturbance handling, steady state and a dynamic tracking profile, higher speed bandwidth and lesser torque pulsations compared to the conventional observer. [ABSTRACT FROM AUTHOR]

Given the recent increasing public focus on climate change issues, the share of electricity generation by renewable energy resources is increasing day by day. Increased renewables share will give us robust, sustainable, and climate-friendly energy systems for the future. Renewable energy penetration with the current power systems needs substantial research, planning and development which are now the primary focus throughout the world. In this study, a global renewable energy scenario is explained in detail in contrast with India, considering a case study elucidating the comprehensive review of the Gujarat state in India. The primary focus is on Gujarat state's actions plans to pertain to harvest renewable energy and maximizing its share in the energy mix. This study examines the actions and the policies adopted by the Gujarat government to overcome the potential barriers in order to support non-conventional as well as renewable energy development. It also investigates the numerous techno-economic and social constraints with possible solutions in promoting the deployment of upcoming renewable energy resources across Gujarat. This study can be used as a guideline for the government, policymakers, utilities, stakeholders and researchers to promote an increased renewable energy share in Gujarat as well as at other places around the globe. [ABSTRACT FROM AUTHOR]

A correction to the article "A practical biogas based energy neutral home system for rural communities of Bangladesh" that was published online on March 2, 2016 is presented.

Wastewater containing toxic substances is a major threat to the health of both aquatic and terrestrial ecosystems. In order to treat wastewater, nanomaterials are currently being studied intensively due to their unprecedented properties. The unique features of nanoparticles are prompting an increasing number of studies into their use in wastewater treatment. Although several studies have been undertaken in recent years, most of them did not focus on some of the nanomaterials that are now often utilized for wastewater treatment. It is essential to investigate the most recent advances in all the types of nanomaterials that are now frequently employed for wastewater treatment. The recent advancements in common nanomaterials used for sustainable wastewater treatment is comprehensively reviewed in this paper. This paper also thoroughly assesses unique features, proper utilization, future prospects, and current limitations of green nanotechnology in wastewater treatment. Zero-valent metal and metal oxide nanoparticles, especially iron oxides were shown to be more effective than traditional carbon nanotubes (CNTs) for recovering heavy metals in wastewater. Iron oxide achieved 75.9% COD (chemical oxygen demand) removal efficiency while titanium oxide (TiO 2 ) achieved 75.5% COD. Iron nanoparticles attained 72.1% methyl blue removal efficiency. However, since only a few types of nanomaterials have been commercialized, it is important to also focus on the economic feasibility of each nanomaterial. This study found that the large surface area, high reactivity, and strong mechanical properties of nanoparticles means they can be considered as a promising option for successful wastewater treatment., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The COVID-19 pandemic affects all of society and hinders day-to-day activities from a straightforward perspective. The pandemic has an influential impact on almost everything and the characteristics of the pandemic remain unclear. This ultimately leads to ineffective strategic planning to manage the pandemic. This study aims to elucidate the typical pandemic characteristics in line with various temporal phases and its associated measures that proved effective in controlling the pandemic. Besides, an insight into diverse country's approaches towards pandemic and their consequences is provided in brief. Understanding the role of technologies in supporting humanity gives new perspectives to effectively manage the pandemic. Such role of technologies is expressed from the viewpoint of seamless connectivity, rapid communication, mobility, technological influence in healthcare, digitalization influence, surveillance and security, Artificial Intelligence (AI), and Internet of Things (IoT). Furthermore, some insightful scenarios are framed where the full-fledged implementation of technologies is assumed, and the reflected pandemic impacts in such scenarios are analyzed. The framed scenarios revolve around the digitalized energy sector, an enhanced supply chain system with effective customer-retailer relationships to support the city during the pandemic scenario, and an advanced tracking system for containing virus spread. The study is further extended to frame revitalization strategies to highlight the expertise where significant attention needs to be provided in the post-pandemic period as well as to nurture sustainable development. Finally, the current pandemic scenario is analyzed in terms of occurred changes and is mapped into SWOT factors. Using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution based Multi-Criteria Decision Analysis, these SWOT factors are analyzed to determine where prioritized efforts are needed to focus so as to traverse towards sustainable cities. The results indicate that the enhanced crisis management ability and situational need to restructure the economic model emerges to be the most-significant SWOT factor that can ultimately support humanity for making the cities sustainable., Competing Interests: The authors report no declarations of interest., (© 2021 Elsevier Ltd. All rights reserved.)

The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID-19 cases, multivariate LSTM models is employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% as compared to the other considered algorithms for the studied performance metrics. Country-specific analysis and city-specific analysis of COVID-19 cases for India and Chennai, respectively, are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID-19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

The demand of electricity has been reduced significantly due to the recent COVID-19 pandemic. Governments around the world were compelled to reduce the business activity in response to minimize the threat of coronavirus. This on-going situation due to COVID-19 has changed the lifestyle globally as people are mostly staying home and working from home if possible. Hence, there is a significant increase in residential load demand while there is a substantial decrease in commercial and industrial loads. This devastating situation creates new challenges in the technical and financial activities of the power sector and hence most of the utilities around the world initiated a disaster management plan to tackle this ongoing challenges/threats. Therefore, this study aims to investigate the global scenarios of power systems during COVID-19 along with the socio-economic and technical issues faced by the utilities. Then, this study further scrutinized the Indian power system as a case study and explored scenarios, issues and challenges currently being faced to manage the consumer load demand, including the actions taken by the utilities/power sector for the smooth operation of the power system. Finally, a set of recommendations are presented to support the government/policymakers/utilities around the world not only to overcome the current crisis but also to overcome future unforeseeable pandemic alike scenario., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Elsevier Ltd. All rights reserved.)

The race for better electrochemical energy storage systems has prompted examination of the stability in the molybdate framework (MMoO 4 ; M = Mn, Co, or Ni) based on a range of transition metal cations from both computational and experimental approaches. Molybdate materials synthesized with controlled nanoscale morphologies (such as nanorods, agglomerated nanostructures, and nanoneedles for Mn, Co, and Ni elements, respectively) have been used as a cathode in hybrid energy storage systems. The computational and experimental data confirms that the MnMoO 4 crystallized in β-form with α-MnMoO 4 type whereas Co and Ni cations crystallized in α-form with α-CoMoO 4 type structure. Among the various transition metal cations studied, hybrid device comprising NiMoO 4 vs activated carbon exhibited excellent electrochemical performance having the specific capacitance 82 F g -1 at a current density of 0.1 A g -1 but the cycling stability needed to be significantly improved. The specific capacitance of the NiMoO 4 electrode material is shown to be directly related to the surface area of the electrode/electrolyte interface, but the CoMoO 4 and MnMoO 4 favored a bulk formation that could be suitable for structural stability. The useful insights from the electronic structure analysis and effective mass have been provided to demonstrate the role of cations in the molybdate structure and its influence in electrochemical energy storage. With improved cycling stability, NiMoO 4 can be suitable for renewable energy storage. Overall, this study will enable the development of next generation molybdate materials with multiple cation substitution resulting in better cycling stability and higher specific capacitance.