Search

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degradation, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst 7.1 simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at standard test condition (STC) efficiency. After considering losses, the system generates 2815.2 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27,744 kBDT from selling energy to the grid and operating costs of 64,060.60 BDT/year. Remarkably, this initiative can prevent 37,647.82 tCO2 emissions over the project’s 25-year lifespan.

A paradigm shift in power systems is observed due to the massive integration of renewable energy sources (RESs) as distributed generators. Mainly, solar photovoltaic (PV) panels and wind generators are extensively integrated with the modern power system to facilitate green efforts in the electrical energy sector. However, integrating these RESs destabilizes the frequency of the modern power system. Hitherto, the frequency control has not drawn sufficient attention due to the reduced inertia and complex control of power electronic converters associated with renewable energy conversion systems. Thus, this article provides a critical summary on the frequency control of solar PV and wind-integrated systems. The frequency control issues with advanced techniques, including inertia emulation, de-loading, and grid-forming, are summarized. Moreover, several cutting-edge devices in frequency control are outlined. The advantages and disadvantages of different approaches to control the frequency of high-level RESs integrated systems are well documented. The possible improvements of existing approaches are outlined. The key research areas are identified, and future research directions are mentioned so that cutting-edge technologies can be adopted, making the review article unique compared to the existing reviews. The article could be an excellent foundation and guidance for industry personnel, researchers, and academicians.

An autonomous microgrid is often formed by incorporating distributed generators into the distribution system. However, distributed generators have less inertia compared to traditional synchronous generators, and can cause the system frequency to become unstable. Additionally, as more clusters are integrated into the distribution microgrid, frequency instability increases. To resolve frequency instability in the microgrid cluster, this study proposes a supercapacitor control approach. The microgrid consists of several clusters which integrate wind power generators, solar PV, STP, fuel cells, aqua electrolyzers, and diesel generators. Initially, a small signal model is developed to facilitate the control design. A fractional-order supercapacitor controller is augmented with the developed small-signal model to stabilize the frequency of the microgrid. Furthermore, the controller parameters are optimized to guarantee robust controller performance. The proposed fractional-order supercapacitor controller provides more degrees of freedom compared to the conventional controller. Time-domain simulations were carried out considering several real-time scenarios to test the performance of the proposed controller. We observed that the presented approach is capable of stabilizing the system frequency in all cases. Furthermore, the proposed approach outperforms existing approaches in stabilizing the frequency of the microgrid cluster.

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R2) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO2e) and mean absolute percentage error (0.29 GtCO2e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO2 equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.

Autism spectrum disorder (ASD) is a neurological illness characterized by deficits in cognition, physical activities, and social skills. There is no specific medication to treat this illness; only early intervention can improve brain functionality. Since there is no medical test to identify ASD, a diagnosis might be challenging. In order to determine a diagnosis, doctors consider the child’s behavior and developmental history. The human face can be used as a biomarker as it is one of the potential reflections of the brain and thus can be used as a simple and handy tool for early diagnosis. This study uses several deep convolutional neural network (CNN)-based transfer learning approaches to detect autistic children using the facial image. An empirical study is conducted to select the best optimizer and set of hyperparameters to achieve better prediction accuracy using the CNN model. After training and validating with the optimized setting, the modified Xception model demonstrates the best performance by achieving an accuracy of 95% on the test set, whereas the VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0 achieved 86.5%, 94%, 92%, and 85.8%, accuracy, respectively. Our preliminary computational results demonstrate that our transfer learning approaches outperformed existing methods. Our modified model can be employed to assist doctors and practitioners in validating their initial screening to detect children with ASD disease.

The integration of distributed generation systems, including intermittent solar photovoltaic (PV) and wind, has a significant impact on the power system [...]

This paper proposes a non-linear control-based variable resistive bridge type fault current limiter (VR-BFCL) as a prospective solution to ease the effect of disturbances on voltage source converter-based high voltage DC (VSC-HVDC) systems. A non-linear controller for VR-BFCL has been developed to insert a variable optimum resistance during the inception of system disturbances in order to limit the fault current. The non-linear controller takes the amount of DC link voltage deviation as its input and provides variable duty to generate a variable effective resistance during faults. The VSC-HVDC system’s real and reactive power controllers have been developed based on a current control loop where direct axis and quadrature axis currents are used to control the active and reactive power, respectively. The efficacy of the proposed non-linear control-based VR-BFCL solution has been proved with balanced as well as unbalanced faults. The results confirm that the oscillations in active power and DC link voltage have been significantly reduced by limiting the fault current through the insertion of an optimum effective resistance with the proposed control technique. The real time digital simulator (RTDS) has been used to implement the proposed approach. The performance of the proposed non-linear control based VR-BFCL is compared with that of traditional fixed duty control.

Power systems are becoming more and more complex in nature due to the integration of several power electronic devices. Protection of such systems and augmentation of reliability as well as stability highly depend on limiting the fault currents. Several fault current limiters (FCLs) have been applied in power systems as they provide rapid and efficient fault current limitation. This paper presents a comprehensive literature review of the application of different types of FCLs in power systems. Applications of superconducting and non-superconducting FCLs are categorized as: (1) application in generation, transmission and distribution networks; (2) application in alternating current (AC)/direct current (DC) systems; (3) application in renewable energy resources integration; (4) application in distributed generation (DG); and (5) application for reliability, stability and fault ride through capability enhancement. Modeling, impact and control strategies of several FCLs in power systems are presented with practical implementation cases in different countries. Recommendations are provided to improve the performance of the FCLs in power systems with modification of its structures, optimal placement and proper control design. This review paper will be a good foundation for researchers working in power system stability issues and for industry to implement the ongoing research advancement in real systems.

This paper proposes the use of bridge type fault current limiters (BFCLs) as a potential solution to reduce the impact of fault disturbance on voltage source converter-based high voltage DC (VSC-HVDC) systems. Since VSC-HVDC systems are vulnerable to faults, it is essential to enhance the fault ride-through (FRT) capability with auxiliary control devices like BFCLs. BFCL controllers have been developed to limit the fault current during the inception of system disturbances. Real and reactive power controllers for the VSC-HVDC have been developed based on current control mode. DC link voltage control has been achieved by a feedback mechanism such that net power exchange with DC link capacitor is zero. A grid-connected VSC-HVDC system and a wind farm integrated VSC-HVDC system along with the proposed BFCL and associated controllers have been implemented in a real time digital simulator (RTDS). Symmetrical three phase as well as different types of unsymmetrical faults have been applied in the systems in order to show the effectiveness of the proposed BFCL solution. DC link voltage fluctuation, machine speed and active power oscillation have been greatly suppressed with the proposed BFCL. Another significant feature of this work is that the performance of the proposed BFCL in VSC-HVDC systems is compared to that of series dynamic braking resistor (SDBR). Comparative results show that the proposed BFCL is superior over SDBR in limiting fault current as well as improving system fault ride through (FRT) capability.