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Rural electrification is a crucial step for the socio-economic development of isolated communities. Decentralized power generation, typically more favorable for renewable energies, requires an accurate analysis of the different electrification options, whose convenience depends on multiple factors. The application of Geographical Information Systems (GISs) to energy planning allows the assessment at a local level, considering the variability and demand distribution of spatial resources. This work introduces IntiGIS-local, a GIS-based model implemented in the ArcGIS environment, designed to calculate the levelized energy cost (LEC) for different electrification options. The model allows the comparison between three power generation alternatives: solar system, diesel generator set and solar–diesel hybrid system. Configurations are adjustable through input variables, with a special focus on the confrontation between individual systems and microgrids. The objective is to provide an adequate groundwork for developing a decision-making tool to assess diverse rural electrification options in future studies. The model IntiGIS-local is tested in the case study of the Guasasa community (Cuba).

The decarbonization of the district heating (DH) sector is receiving attention worldwide. Solar energy and heat pump technologies are widely considered in existing and new DH networks. There is a need to understand the influence of solar energy on district heating experimentally. However, only a few university laboratories are focused on district heating aspects. Further, the concept of such laboratories is not adequately disseminated in the scientific literature. The main objective of this paper is to develop a conceptual design of a solar energy laboratory with a focus on district heating systems. The proposed concept forms part of the preliminary study carried out by a research group at the Tallinn University of Technology. First, a brief literature review on solar energy laboratory development is provided. Then, the conceptual design of such a laboratory is presented, along with a case study. Regardless of project size, the main components of a district heating-based solar energy laboratory are solar collectors, thermal energy storage (TES) tanks, and a control system. The proposed laboratory is expected to serve multiple roles, such as a practical laboratory to provide interdisciplinary courses for students, a research and experimental platform for researchers, and a cradle to achieve the campus green initiative. It is roughly estimated that the thermal energy output from the proposed laboratory would meet around 25% of the heat demand of the institutional building during the summer season (May, June, July, and August). It is expected that the present study will be a reference material for the development of innovative energy laboratories in educational institutions.

DC microgrid menjadi solusi yang baik untuk meningkatkan permintaan beban listrik dan merupakan cara yang efektif untuk memanfaatkan sumber energi terbarukan ke dalam sistem pembangkit terdistribusi. Energi matahari mempunyai sifat terputus-putus ketika digunakan dalam DC microgrid. Dalam penelitian sebelumnya, untuk mengatasi fluktuasi daya keluaran photovoltaic (PV) array, baterai digunakan sebagai cadangan energi. Namun, penggunaan banyak baterai membutuhkan biaya yang besar. Penelitian ini bertujuan untuk mengurangi fluktuasi daya pada DC bus ketika sumber DC microgrid dari PV array dan baterai terputus. Penelitian dilakukan menggunakan simulasi MATLAB, dengan merancang konfigurasi campuran (hybrid) DC/AC coupling. Pada konfigurasi ini, digunakan dua PV array sebagai sumber, dua multibaterai, dan jaringan utilitas. Pengaturan DC microgrid dilakukan secara terpisah oleh masing-masing konverter dengan mengirim sinyal referensi ke kontrol konverter. Pada kondisi pertama, beban DC dan beban AC disuplai dari PV array, pada kondisi kedua, beban disuplai dari baterai, sedangkan pada kondisi ketiga, beban disuplai dari jaringan utilitas. Hasil penelitian memperlihatkan bahwa ketika sumber PV array digunakan, tegangan DC bus tetap stabil 48 V, walaupun terjadi lonjakan pada pukul 08.00 dan pukul 15.00. Demikian juga ketika digunakan sumber baterai dan jaringan utilitas, tegangan DC bus dipertahankan pada level 48 V. Dalam penelitian ini, dengan tiga kondisi atau mode, DC microgrid mampu menyuplai beban tanpa terputus. Oleh karena itu, konfigurasi campuran DC microgrid mampu menyediakan daya listrik secara terus menerus.

This research paper presents a smart power control approach specifically designed for an independent microgrid. The proposed hybrid system consists of various crucial components, including a PV array, super capacitor, DC bus, battery bank, and AC bus working together to generate and store electricity within the microgrid. To address the challenges arising from random fluctuations in ecological parameters and changes in load demand, a supervisory controller is developed to enhance the standalone hybrid microgrid. This allows for optimized power management within the micro grid. The Liebenberg Marquardt algorithm is used to retrieve the trained ANN machine. The two and three hidden layered ANN machines have 96% accuracy on an average, whereas the single-layer ANN machine have poor predictive ability. The proposed model is implemented and analysed using MATLAB/Simulink. The observed results from the simulation experiments validate the effectiveness of integrating available resources in ensuring the resilience and reliability of microgrids.

PV arc-faults can cause fires, damage property, and endanger people's lives. This paper proposes a method for detecting DC arcs using artificial intelligence (AI). The four steps for arc detection are thoroughly described. After removing the low-frequency range (41 kHz) and high-frequency range (>102.5 kHz) components, the middle frequency range is left for arc analysis. For AI analysis, eight inputs are used. The time sequence for the tasks is also explained, where the parallel task configuration is adopted to save the time. Furthermore, AI model training for arc detection is described, including both offline and online training. In addition, three different types of arc detection system architectures are depicted. There are three layers in the architectures: the PV-end layer, the inverter-level layer, and the cloud layer. Depending on the architecture, the algorithm is located in different layers. Furthermore, the hardware of the arc detection system is explained, as is the self-testing circuit. Finally, an experimental platform is built, and experimental results are presented to validate the proposed method.

This paper focuses on developing a water and energy-saving reliable irrigation system using state-of-the-art computing, communication, and optimal energy management framework. The framework integrates real-time soil moisture and weather forecasting information to decide the time of irrigation and quantity of water required for potato crops, which is made available to the users across a region through the cloud-based irrigation decision support system. This is accomplished through various modules such as data acquisition, soil moisture forecasting, smart irrigation scheduling, and energy management scheme. The main emphasizes is on the electrical segment which demonstrates an energy management scheme for PV-battery based grid-connected system to operate the irrigation system valves and water pump. The proposed scheme is verified through simulation and dSpace-based real-time experiment studies. Overall, the proposed energy management system demonstrates an improvement in the optimal onsite solar power generation and storage capacity to power the solar pump which save the electrical energy as well as the water in order to establish an improved solar-irrigation system. Finally, the proposed system achieved water and energy savings of around 9.24 % for potato crop with full irrigation enhancing the Water-Energy-Food Nexus at field scale.

Photovoltaic (PV) systems have gained global significance as a sustainable and renewable energy resource, playing a pivotal role in mitigating climate change and meeting growing energy demands. Like many other nations, Jordan recognizes the potential of PV systems and has been actively pursuing their adoption across various sectors, particularly in the residential sector. This paper aims to investigate the factors influencing residential customers' perceptions of PV systems and their intentions to adopt these systems in Jordan by identifying key determinants of perception and adoption and developing a comprehensive Structural Equation Model (SEM) to quantify the relationships between identified factors and their effect on the intention to adopt residential PV systems. A theoretical model was developed to achieve the study's objectives, employing confirmatory factor analysis and structural equation modeling. Data for this study was collected through a comprehensive survey targeting the residential segment. The findings indicate that social, technological, governmental, and financial factors positively and statistically significantly impact residential customers' perceptions of PV systems, fostering their intention to embrace this technology. Surprisingly, the environmental factor was deemed insignificant in influencing residential customers' decision-making processes. This research offers valuable insights to various stakeholders, including PV contractors, consultants, governmental entities, policy, and decision-makers. By understanding the key drivers and barriers shaping residential PV adoption, stakeholders can devise more effective strategies to promote sustainable energy practices, foster economic growth, and contribute to the global transition towards cleaner energy sources.

The BLDC (Brushless DC Motor) is utilized in electric vehicles, space missions, and mechanical applications. Neural Network Inference System reduces torque ripple for hybrid electric vehicle (PV-Battery) along with BLDC drive to achieve efficient speed performance and stability. A hybrid input source methodology is thus put forwarded to drive the stator currents giving exactly the expressed electromagnetic torque and counter-EMF harmonics. The torque and speed control technique are directed to neural network interference system, and H6 Voltage Source Inverter (H6 VSI) drives BLDC with a gate pulse signal. We examine how an ANFIS-GA torque controller may eliminate BLDC torque ripples under uninterrupted hybrid power supply in this work. MATLAB (Simulink) results show that Genetic Algorithm (GA) improves training of ANFIS better with varying set speed conditions. The ANFIS-GA controller outperforms challenging controllers under various BLDC motor driving load conditions, proving its efficiency.

The current work presents the design and modeling of a solar and hydrogen energy-based integrated energy system that provides the electricity demand of a stand-alone house located in Izmir, Türkiye. This system is mainly comprised of photovoltaic (PV) cells, battery banks, a PEM electrolyzer (PEM-El), a hydrogen (H 2) compressor, and a pressurized hydrogen tank. Electricity produced from PV cells was either used in the PEM-El for hydrogen production or stored in the batteries to meet the required energy during low solar radiation or night time. The H 2 produced by the electrolyzer was pressurized with a compressor and stored in pressure tanks at a pressure of 350 bar. A zero-dimensional (0-D) mathematical approach was applied for the system component modeling. An energy management strategy was incorporated into the model to investigate the dynamic behavior of the integrated system. Parametric studies were then conducted for different numbers of PV and batteries. It was found that the amount of hydrogen produced is higher in summer (28.58 kg with 20 PV-30 batteries in June, 47.69 kg with 30 PV-20 batteries in July, and 66.96 kg with 40 PV-30 batteries in August). On the other hand, hydrogen produced is lower in winter (11.60 kg with 20 PV-10 batteries in December, 21.82 kg with 30 PV-20 batteries in January, and 32.20 kg with 40 PV-30 batteries in February). In addition, an economic analysis was conducted for the hybrid system. • Hybrid system based on solar and hydrogen energy is investigated. • A new energy management strategy is determined for the hybrid system. • Minimum of 5 PV-7 batteries are needed to meet the electricity needs of the house. [ABSTRACT FROM AUTHOR]

Water electrolysis coupled with renewable electricity systems is one of the most promising ways that will contribute to making the energy transition toward zero carbon emissions possible. This study aims to perform a technical-economic analysis of large-scale hydrogen production facilities through the simulation and optimization of hybrid renewable energy systems that use wind turbines and photovoltaic panels. Five Moroccan cities are subjected to the approach. The PV/wind ratio, net present cost, Levelized cost of hydrogen, storage capacity, and water desalination cost are analyzed. The results show that combining photovoltaic panels and wind turbines helps produce low-cost hydrogen in Morocco, especially in Dakhla with 2.54$/kg. It's the lowest cost in Morocco compared to previous studies. The results also show the importance of using hydrogen tanks to store energy. Finally, the cost of water desalination can be neglected, as it only represents between 0.12% and 0.35% of the net present costs. • Analysis of hydrogen production from the combination of wind turbine and PV panels. • Morocco has the potential to compete in the hydrogen market, with 2.54$/kg. • Desalination cost doesn't exceed 1% of the total cost of hydrogen systems. • Increasing the production flexibility can reduce the LCOH by 17%. [ABSTRACT FROM AUTHOR]

This paper outlines the development of a high-performance maximum power point tracking (MPPT)-based solar irradiance estimator for photovoltaic (PV) systems. The suggested estimator is constructed around a simple current–voltage-based algebraic equation that hinges on the operation of the PV system at its maximum power point (MPP). In the realm of MPP operation, the overall system is driven by a nonlinear MPPT controller. To achieve this function, we integrated a hybrid incremental conductance integral backstepping (H-INC-IBS) controller to effectively regulate the PV system. This controller was specially chosen for its powerful potency in maximizing the dynamics of the PV system, leading to heightened robustness against changing environmental conditions. The simulation results are provided to showcase the suitability of the proposed estimator. Furthermore, the estimator was verified under experimental conditions, highlighting its soundness and practicality. Through evaluations and comparisons with the conventional irradiance estimator, this paper aimed to emphasize the superiority of the proposed solar irradiance estimator in providing more accurate estimations of solar irradiance for PV systems operating under MPPT supervision. [ABSTRACT FROM AUTHOR]

Telecommunication stations, particularly operating in rural areas, are usually powered by diesel generators due to the lack of access to the utility grid. However, the growing cost of energy due to the constantly increasing fuel prices and the related greenhouse gas emissions contributing to global warming have driven telecom companies to seek better energy management solutions. In this paper, we study the economic feasibility of an environmentally friendly power supply system for rural telecommunication station in the city of Skikda, northeast Algeria. The proposed system is a standalone hybrid PV–wind system with pre-existing diesel generators and battery storage. Different system configurations are considered in the study: (a) diesel generators only, (b) PV–diesel–battery, (c) wind–diesel–battery, (d) PV–wind–diesel–battery, and lastly (e) PV–wind–battery; this helps to select the optimal solution based on the lowest net present cost (NPC) and the cost of energy (COE) of each configuration. The optimization is performed using HOMER PRO software 3.14.2 version. The results showed that a hybrid system of 5 kW DG, 3.81 kW of PV capacity, three wind turbines, and a 14-battery bank is the best design for the proposed power system with an NPC of USD 85673 and a COE of USD 0.214. The greenhouse gas emissions were considerably reduced by more than half making the proposed system a technically, economically, and environmentally viable solution. [ABSTRACT FROM AUTHOR]

Islanded microgrids with low-inertia distributed energy resources (DERs) are prone to frequency fluctuations. With the increasing integration of DERs in microgrids, the complexity of control and stability has also increased. Moreover, the integration of DERs into microgrids may result in a power imbalance between energy supply and demand during sudden changes in load or energy generation. This can cause frequency variations in the microgrid, which could have disastrous consequences such as equipment damage or even blackouts. This paper proposes a control strategy to ensure the efficient operation of an islanded hybrid microgrid consisting of a PV generator, battery energy storage system (BESS), and emergency diesel generator. According to Energy Exchange Model proposed in this paper, the hybrid system presented operates independently without being connected to the electrical grid, where the PV system and BESS act as the main energy sources, while the emergency diesel generator provides active power backup with voltage and frequency regulation. The novel in this paper is also that DER aids in frequency regulation during active power transients by delivering and absorbing active power in accordance with the inverter's suggested P droop control strategy. In this way inverter droop control decreases system frequency nadir emulating so called "synthetic inertia". To design both the islanded hybrid system and the proposed control strategy, the MATLAB/Simulink environment is utilized. Based on the results, it can be concluded that the analyzed microgrid system is capable of maintaining stability and operating efficiently in a range of operating conditions. [ABSTRACT FROM AUTHOR]

The diagnostic evaluation of myeloproliferative neoplasms (MPNs) depends on the close correlation between clinical features, morphologic assessment of a trephine bone marrow biopsy, and molecular markers. Typically, MPNs have driver mutations in JAK2 , CALR , or MPL , as well as mutations in genes related to epigenetic regulation, RNA splicing, and signaling. Mutations in these genes are a hallmark of diagnostic, prognostic, and therapeutic assessment in patients with MPNs. In line with the World Health Organization classification, all myeloproliferative disorders require molecular characterization to support diagnoses or confirm entities defined by underlying molecular abnormalities. A structured molecular analysis workflow is essential for a rapid and cost-effective diagnosis of MPN. The purpose of this review is to explore the role of molecular diagnostics in the assessment of BCR::ABL1 -negative MPNs. [ABSTRACT FROM AUTHOR]

The rapid pace of drug development in hematology has led to multiple approvals for myelofibrosis (MF) and polycythemia vera (PV) in recent years. Moreover, there are many innovative agents and combinations being explored for myeloproliferative neoplasms (MPNs). In the past year, there have been several advances in MF, PV, and essential thrombocythemia. In MF, investigational approaches are focusing on strategies to optimize inhibition of signal transduction (including JAK inhibition), modify epigenetics, enhance apoptosis, target DNA replication, transform host immunity, and/or alter the tumor microenvironment. In PV, ropeginterferon alfa‐2b has been introduced to the market in the United States, and data continue to accumulate to support the safety and efficacy of this treatment. Hepcidin mimesis is also emerging as a novel way to treat erythrocytosis. In essential thrombocythemia, ropeginterferon alfa‐2b is being evaluated, as are therapies to modify epigenetics and inhibit CALR. The enhanced focus on MPNs brings hope that our field can improve morbidity and mortality in this group of diseases. Using a variety of mechanisms, many innovative agents and combinations are being explored for myeloproliferative neoplasms. There is great optimism that at least some of the strategies under investigation will bring improved outcomes to patients with these diseases. [ABSTRACT FROM AUTHOR]

During the long-term operation of the photovoltaic (PV) system, occlusion will reduce the solar radiation energy received by the PV module, as well as the photoelectric conversion efficiency and economy. However, the occlusion detection of the PV power station has the defects of low efficiency, poor accuracy, and untimely detection, which will cause unknown system losses. Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. In order to accurately obtain the occlusion area and position information of the PV panel, a PV module occlusion detection model based on the Segment-You Only Look Once (Seg-YOLO) algorithm is established. Based on the YOLOv5 algorithm, the loss function is modified, the Segment Head detection module is introduced, and the convolutional block attention module (CBAM) attention mechanism is added to achieve the accurate detection of small targets by the algorithm model and the fast detection of the PV module occlusion area identify. The model performance research is carried out on three types of occlusion datasets: leaf, bird dropping, and shadow. According to the experimental results, the proposed model has better recognition accuracy and speed than SSD, Faster-Rcnn, YOLOv4, and U-Net. The precision rate, recall rate, and recognition speed can reach 90.52%, 92.41%, and 92.3 FPS, respectively. This model can lay a theoretical foundation for the intelligent operation and maintenance of PV systems. [ABSTRACT FROM AUTHOR]

A new generation of metal-free organic dyes with a range of donor (D1) and acceptors (A1-A3) were designed and examined for dye-sensitized solar cells (DSSCs) based on (3a) dye as a literature. Triphenylamine (TPA), thiophene (π) and 2-cyanoacrylic acid groups each perform the roles of an acceptor (A), donor (D) and spacer in order to produce a D- π -A system. To investigate the intramolecular charge transfer (ICT), electronic distribution, ultra-violet visible (UV–Vis) absorption wavelengths, molecular electrostatic potential (MEP) and photovoltaic (PV) parameters of the D1 and A1–A3 molecules, density functional theory (DFT) and time-dependent DFT (TD-DFT) were used. The classification of the tunable donor D1 and A1–A3 determines the PV performance of the dye molecules. Results show that the A2 dye replacement group increases the performance of PV cells via red-shifting absorption spectra. Also, when compared to 3a, A2 dye have lower energy gap ( E g) and superior UV–Vis spectra that cover the full visible range. These results demonstrate the viability of molecular tailoring as an approach to improve D- π -A sensitizer proposal for efficient DSSCs fabrication. Perylene-based organic dyes (D1 and A1–A3) are designed and systematically investigated for DSSCs. To investigate the intramolecular charge transfer (ICT), electronic distribution, ultra-violet visible (UV–Vis) absorption wavelengths and photovoltaic (PV) parameters of the D1 and A1–A3 molecules, density functional theory (DFT) and time-dependent DFT (TD-DFT) were used. All the dyes might be used for the potential in DSSCs. [ABSTRACT FROM AUTHOR]

To realize Carbon Neutrality, renewable energy would be the main source of electricity, such as photovoltaic (PV) power generation in the Japanese power system. However, a decrease in the number of operating thermal generators, the main source of balancing power in the Japanese balancing market, is causing concerns regarding a shortage of balancing power in the future. PV can therefore provide balancing power in addition to energy; thus, the PV power economic value in electricity markets will become diversified. A solution examination and rationality analysis of technical issues for PV‐based balancing power creation are needed. Here, by applying the proposed headroom control method based on the PV output curtailment beforehand, PV generator optimizes benefit by bidding into the balancing market when the spot market price is very low. Optimizing the bidding strategy on the spot market and balancing market is conducted, and battery integration is also considered. The optimization simulation results validate the possibility of creating balancing power by a PV power producer, and the total revenue valuation verifies a more beneficial effect. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Polycythemia vera (PV) is classically thought to be associated with low erythropoietin (EPO) levels. Here, we present a review of the utility of using EPO levels in diagnosing polycythemia. We conducted a systematic literature review of the Medline data through Pubmed and Google Scholar. We included the articles which described confirmed PV associated with elevated EPO level. Our search strategy included the following terms in Pubmed (((polycythemia vera[MeSH Terms]) OR (jak2 protein tyrosine kinase[MeSH Terms])) OR (Myeloproliferative Disorders[MeSH Terms])) AND (Erythropoietin[MeSH Terms]), and 'polycythemia vera with erythropoietin' in Google Scholar. Our research yielded four cases of PV with elevated EPO levels. The most common symptom was a headache. Thrombotic phenomena happened in a single case in the form of Budd-Chiari syndrome. The mean Hb level was 20.2 gm/dl, and the EPO level was 213 mlU/mL. Although PV is usually associated with low EPO levels, high levels do not exclude this diagnosis. Workup should include testing for JAK2 mutation and bone marrow biopsy in the presence of suggestive signs and symptoms. Novel biomarkers are also being proposed to aid in the diagnosis. Although elevated EPO levels suggest secondary causes of polycythemia, cases where elevated EPO levels were associated with an underlying PV are reported in the literature, and we have summarized a review of them. Workup for polycythemia should include JAK2 mutation testing if signs and symptoms suggest PV even if EPO is elevated. [ABSTRACT FROM AUTHOR]

This article describes the multi-objective spotted hyena optimization (MOSHO) based control of a three-phase three-wire grid-tied dual-stage PV system. The MOSHO delivers an optimized step size $ (\boldsymbol{\delta }) $ (δ) for the incremental conductance (InC) based maximum power point tracking (MPPT) algorithm, as well as optimizes the PI controller gains for DC bus voltage $ ({{{\mathbf V}_{{\mathbf dc}}}}) $ ( V d c ) regulation. The $ \boldsymbol{\delta } $ δ optimized MPPT facilitates the faster and more accurate tracking of maximum power point with fewer fluctuations at steady-state. The MOSHO optimized PI controller regulates the $ {\textrm{V}_{\textrm{dc}}} $ V dc and delivers better stability with reduced $ {\textrm{V}_{\textrm{dc}}} $ V dc variations in DC bus voltage during steady-state and dynamic states, i.e. irradiation variation, abnormal and unbalanced grid voltage. The optimized DC bus generates an accurate loss component of current, which further enhances the voltage source converter (VSC) performance in terms of precise reference currents generation and power quality improvement. The proposed system performs satisfactorily in a MATLAB simulation environment as per IEEE519 standards. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted to address the issue of the reduced efficiency of solar photovoltaic (PV) cells at high temperatures. To address this problem, a hybrid cooling system has been developed. This system uses a thermal collector to convert waste heat into reusable heat. Selecting the best configuration and operational parameters for the collector is crucial for maximizing system performance. To achieve this, we conducted computational fluid dynamics (CFD) modeling using ANSYS. Various factors affecting the cooling of PV solar cells were analyzed, including the collector design, mass flow rate, and concentration of the Al2O3 nanofluids. Results showed that the 12S finned thermal collector system exhibits the lowest temperature for PV solar cells, at approximately 29.654 oC. The electrical efficiency of PV solar cells is influenced by the concentration of Al2O3 nanofluids. We found that the 12S finned collector system with 1% water/Al2O3 nanofluid achieved the highest efficiency (approximately 11.749%) at a flow rate of 0.09 kg/s. The addition of finned collectors affects efficiency and variations in fluid mass flow rates, and there is no relation between the connector type and different Al2O3 nanofluid concentrations. In other words, the cooling system can be optimized to enhance the efficiency of the PV solar cells under high-temperature conditions. [ABSTRACT FROM AUTHOR]

Featured Application: This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources. The "dual carbon" strategy has drawn attention to distributed PV systems for their flexibility and variability, but the rising need for direct-current (DC) loads on the load side has created additional difficulties for microgrid system upgrades. In this article, a PV-based microgrid design approach for residential buildings is suggested, working on the assumption that distributed PV systems are given top priority to handle domestic DC needs. The residential DC microgrid system's overall design concept is first put out, and the circuit system is then concentrated to supply the main idea for the ensuing verification of the system's viability. Secondly, the actual power generation in the selected area was clarified by testing, and then the electricity consumption of DC loads accounted for about 20.03% of the total power consumption according to the survey of 100 users. In addition, the circuit system is subjected to spectral model measurements and physical measurements to verify the operational performance of the circuit system; the feasibility of the PV microgrid system is further verified using dual testing of the PV system and the circuit system. The test results show that the proposed DC microgrid system can accurately provide the required voltage for small household DC appliances, such as 24 V, 14 V, 5 V, etc. Finally, the system economics were analyzed, and the equipment payback years were estimated. The supply and demand of PV power generation and DC appliances can be balanced via the construction of a microgrid. This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources. [ABSTRACT FROM AUTHOR]

Microgrids, with integrated PV systems and nonlinear loads, have grown significantly in popularity in recent years, making the evaluation of their transient behaviors in grid-connected and islanded operations paramount. This study examines a microgrid's low-voltage ride-through (LVRT) and high-voltage ride-through (HVRT) capabilities in these operational scenarios. The microgrid's behavior was analyzed using both electromagnetic transient (EMT) and RMS simulation methods. Two operational modes, grid-connected and islanded, were considered. A three-phase diesel generator acted as a reference machine in islanded mode. Findings highlighted distinct behaviors in the two operational modes. The EMT simulation revealed in-depth characteristics of electrical parameters, showing high-frequency oscillations more precisely than the RMS simulation. Additionally, the transient recovery times were longer in islanded mode compared to grid-connected mode. The EMT simulation offers a more detailed portrayal of transient behaviors than the RMS simulation, especially in capturing high-frequency disturbances. However, its completion time becomes significantly extended with longer simulation durations. Microgrids showcase distinct transient behaviors in grid-connected versus islanded modes, especially in LVRT and HVRT scenarios. These findings are critical for the design and operation of modern microgrids. [ABSTRACT FROM AUTHOR]

In desert regions like the Middle East, dust has a profound impact on the environment, climate, air quality, and solar devices. The size of dust particles determines the extent of these effects. Dust deposition (DD) measurements show that coarse dust particles with geometric radius r > 10 μm comprise most of the deposited mass. Still, these particles are not represented in the current models that are tuned to fit the observed aerosol optical depth (AOD). As a result, the existing models and reanalysis products underestimate DD and dust emission (DE) almost 3 times. This is the first study to constrain the dust simulations by both AOD and DD measurements to quantify the effect of coarse and fine dust using the WRF‐Chem model. We found that, on average, coarse dust contributes less than 10% to dust shortwave radiative forcing (RF) at the surface but comprises more than 70% of DE. Annual mean net RF over the Arabian Peninsula and regional seas locally reaches −25 W m−2. Airborne fine dust particles with radii r < 3 μm are mainly responsible for the significant dimming (5%–10%) of solar radiation, cooling the surface, and hampering solar energy production. However, dust mass deposition is primarily linked to coarse particles, decreasing the efficiency of photovoltaic panels by 2%–5% per day. Therefore, incorporating coarse dust in model simulations and data assimilation would improve the overall description of the dust mass balance and its impact on environmental systems and solar devices. Plain Language Summary: Most dust emission schemes used in aerosol chemical transport models are constrained primarily by observed aerosol optical depth (AOD) and do not generate enough coarse dust particles. This study represents the first attempt to constrain the dust emission scheme using AOD and dust deposition (DD) measurements conducted at King Abdullah University of Science and Technology (KAUST). By incorporating both AOD and DD observations, we aimed to enhance the accuracy of the dust emission scheme and provide a more comprehensive understanding of dust mass balance and dynamics in the region. We found that accounting for generating coarse and giant dust particles led to a tripling of dust emissions compared with the previous estimates. We assessed the size distribution of dust particles and their impact on shortwave and longwave radiation. We quantified how particles of different sizes contributed to the regional radiative forcing and examined the implications of DD on the performance and efficiency of solar panels for optimizing solar energy systems in dust‐prone regions. Key Points: Models and reanalysis products underestimate coarse dust emission and dust deposition by 2–3 timesFine dust affects radiation, but coarse dust dominates mass deposition ratesAtmospheric dust dims solar radiation, and coarse dust causes the soiling of solar panels [ABSTRACT FROM AUTHOR]

Renewable resources are a crucial component in power system research within contemporary intelligent power systems. It is imperative that they are given meticulous consideration. Microgrids (MGs) have emerged as a solution to facilitate the integration of renewable energy sources on a large scale. The incorporation of power production innovations that exhibit high levels of unpredictability in their generation would have a notable impact on the management of power resource planning within microgrids. Therefore, it is imperative to implement efficient power management mechanisms in microgrids. This study introduces a scheduling arrangement for a microgrid (MG) that includes a solar power unit (PV), which operates on a day-ahead basis. This paper explores the effects of various weather variables on the power generated by photovoltaic units and the optimum scheduling of microgrids. The present study employs the modified fluid search optimization algorithm to address the challenge of optimum management of energy in a microgrid connected to the grid that is characterized by a high level of unpredictability. The simulation results of the study indicate that the implementation of a photovoltaic model in an actual setting enhances the precision of the system for managing energy and reduces the overall operational costs of the interconnected microgrid. The algorithm under focus has been evaluated on a standard MG. The method under consideration has been executed on the MATLAB/Simulink computational environment and its efficacy has been evaluated. [ABSTRACT FROM AUTHOR]

The integration of a CSP tower system with a PV solar field, sharing a thermal energy storage, is modeled and discussed. The tower system uses a new-design solid particle fluidized bed receiver integrated with a thermal storage, where hot particles are directly collected to store daily energy for overnight production of electricity. The PV solar field is aimed to supply the daily energy demand; when there is a surplus of PV energy production, the electric energy is converted to heat and accumulated in the thermal storage too. The integration of the two energy systems is modeled, building efficiency functions for all the sub-components of the integrated plant (heliostat field, receiver, storage, power block, PV field). Yearly simulations are performed for two different locations, Spain and Australia, obtaining that a system with a peak power of 10 MWe CSP + 15 MWe PV can supply—with a limited curtailment—a fraction of more than 60% (respectively, 62% and 68%) of a realistic electric load with a peak demand around 10 MW, to be compared with the 45/47% of the same load obtained adopting a PV-only system with the same overall peak power. In the integrated system, PV directly supplies 40/41% of the load, the remaining 23/28% being produced by the power block (mainly fed by the CSP). [ABSTRACT FROM AUTHOR]

Multistage inverters containing DC-DC converters are more common in grid-tied photovoltaic (PV) systems, because of the low DC voltage value provided by the PV generator, which requires a two-stage topology. The challenge in the latter case is to control the DC-link voltage with an accurate estimation of the current injected into the grid by PV system. In this paper, a two-stage grid-tied PV design based on a high-gain DC-DC converter and a 7 level packed U cells (7L-PUC) inverter, controlled by Finite-Control-Set Model Predictive Control (FCS-MPC) is presented, with the proposal of a novel model for controlling the DC-link voltage by considering system losses. The simulation and experimental results demonstrate the high effectiveness of the proposed control strategy in terms of power quality as well as response time and accuracy under changes in weather conditions and demonstrate the feasibility of the proposed system. [ABSTRACT FROM AUTHOR]

With energy efficiencies close to two times higher than traditional photovoltaic (PV), concentrated photovoltaic (CPV) systems represent a promising solution for solar power generation. In the same way, the converging Levelized Cost of Energy (LCOE) of both technologies favors interest toward CPV systems. In order to assess more clearly the potential of this technology, an up-to-date evaluation of the power electronic conversion techniques used in CPV to increase the yielded energy is crucial. This assessment not only sheds light on the latest advancements, but also provides insights into design trade-offs, performance limitations, and potential areas for improvement in CPV systems. This work focuses on the DC/DC converters used as an intermediary stage of conversion between the panels and a central grid-tied inverter. Electrical and economical metrics are used to compare actual converters developed and presented in a comprehensive literature review. [ABSTRACT FROM AUTHOR]