Design and numerical simulation of lead-free inorganic perovskites (Cs2BiAgI6) solar cell through computation method.

Cs2BiAgI6 is a lead-free inorganic perovskite material exhibits exceptional photoelectric characteristics and great environmental stability. HTL/Cs2BiAgI6is/ETLs solar cells was investigated numerically by using SCAPS 1-D Capacitance Simulator. IGZO, TiO2, WO3, MoO3, and SnO2 have been chosen as ETLs, while CuO, CuI, and MoO3 are as HTLs. The values of electrical parameters were calculated as function of thickness of the absorber layer, ETLs, HTLs, interface defect densities, doping densities, and working temperature. Comparative study shows that best configuration of obtain solar cell is MoO3/Cs2BiAgI6/IGZO. The obtain value of Jsc, Voc, FF and PCE are 23.80 mA/cm2, 1.193 V, 83.46 %, 23.711 % respectively. The value of quantum efficiency is 80-90 % in the range of 350-750 nm. These results will open the door for the widespread use of stable and environmentally friendly perovskite solar cells by providing theoretical recommendations for high performance of Cs2BiAgI6 based photovoltaic solar cells (PSCs).

A new cleaning method for solar panels inspired from the natural vibrations of tree branches and leaves.

Photovoltaic (PV) panels are similar in many aspects to the leaves of trees, both are standing in the Sun to capture the sunlight, however, PV panels get soiled especially in desert areas, while the leaves remain clean to a very good extent. The question is, why leaves remain clean while PV panels get soiled quite easily? The leaves are hanging on the stem of trees and these stems are flexible to motion, such that if the wind blows in any direction over the stem it vibrates allowing any deposited particle to fall off the surface. The objective of this research is to develop a fixation method for PV panels similar to the stems of trees, such that the panel can vibrate as the wind blows in order to minimize dust accumulation. Different fixation methods for the PV panel are designed, and the air flow around the panel is simulated using the CFD package Ansys Fluent. It has been found that a PV panel pivoted at its lower edge, such that it can revolve around the lower edge, together with a vertical wind shield attached to its upper edge and a spring attached at the middle of its backside has the largest vibration amplitude due to the applied wind compared to the other designs. Experiments have been done to infer the influence of the new fixation method of the PV panel on dust accumulation over the panel. After 6 weeks of operation, it has been found that the efficiency of the PV panel that is flexibly fixed has dropped by only 5%, while the efficiency of the panel that is rigidly fixed has dropped by 25%. It can be concluded that a PV panel operating a light post should be fixed on a flexible base that allows the panel to vibrate as the wind blows over it in order to mitigate dust.

A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems.

With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an important part of the micro-grid system, the energy storage system can realize the stable operation of the micro-grid system through the design optimization and scheduling optimization of the photovoltaic energy storage system. The structure and characteristics of photovoltaic energy storage system are summarized. From the perspective of photovoltaic energy storage system, the optimization objectives and constraints are discussed, and the current main optimization algorithms for energy storage systems are compared and evaluated. The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including algorithm principles, optimization goals, practical application cases, challenges and future development directions, providing new ideas for better promotion and application of new energy photovoltaic energy storage systems and valuable reference.

A quick comparison model on optimizing the efficiency of photovoltaic panels in collecting solar radiation.

Few scholars study light efficiency of solar-cell arrays in theory, while it is difficult to experimentally determine the maximum capacity of a photovoltaic panel to collect solar radiation. This paper proposes a solar energy comparison model (SECM), considering the sunshine duration changes every day to optimize the solar radiation collection model in an ideal state for a whole year, which is easy to use, and can quickly obtain the optimal tilt angle of photovoltaic panels and the solar radiation collecting efficiency enhancement of intelligent light tracking photovoltaic panels. The results show that the sunshine duration is an important factor affecting the solar radiation received by photovoltaic panels. In regions from 66°34'N to 66°34'S, intelligent light tracking photovoltaic panels can increase the collected solar radiation by at least 63.55%, up to 122.51% compared to stationary photovoltaic panels during the effective light time, which is much higher than what most people generally thought. And the advantage of intelligent light tracking photovoltaic panels is more obvious in high latitudes, with a longer and more variable sunshine duration. These findings provide a theoretical basis for the solar radiation collection and photovoltaic panels.

Techno-economic analysis of off-grid hybrid wind-photovoltaic-battery power system by analyzing different batteries for the industrial plant in Shiraz Industrial Town, Iran.

The world has moved toward renewable energy resources for three major reasons: (1) to mitigate climate change arising from the excessive emission of greenhouse gases, (2) to protect health by lowering greenhouse gas emissions, and (3) to meet ever-increasing demands for energy. Shiraz is a major city in Iran and struggles with pollution challenges due to the presence of highly polluting industries. The increased energy demand and the lack of a demand-supply trade-off have led to frequent power outages in Shiraz in recent years. Batteries have been of great interest to researchers as they have a wide range of compounds and variety in the market and strongly influence the function and initial costs of hybrid energy systems. This study models a hybrid renewable energy system using four different batteries, that is, lead-acid, Li-ion, vanadium redox, and zinc-bromine batteries. These four scenarios were subjected to techno-economic analysis in HOMER. The system was assumed to generate 3000 kW of industrial power and 300 kWh of office/domestic power. It was demonstrated that the hybrid system with the lead-acid battery was the most optimal system to supply power to the case-study industrial plant for both industrial and domestic load, with a levelized cost of energy of 0.47 USD/kWh and an initial cost of 6.02 million USD. However, the hybrid system with the Li-ion battery will become more optimal than the system with the lead-acid battery if Li-ion batteries continue to become more affordable in < 5 years. This system would decrease CO2 emissions by 1,060,133 kg every year as compared to the diesel system.

Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems.

Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s.

Nonlinear MPPT techniques to control hybrid power systems.

In recent years, grid-connected multifunctional photovoltaic (PV) systems have proven to be highly efficient. This system integrates PV panels with a DC-DC boost converter (DC-DC-BC) and the electrical distribution grid (DEG). Linking the PV to the AC-DEG is accomplished through a three-level multifunctional voltage source inverter (MVSI). The DC-DC-BC component in this study is engineered to perform maximum power point tracking (MPPT) irrespective of normal or abnormal conditions. The conventional MPPT technique poses several challenges and constraints on system usage. Hence, the suggestion is to adopt synergetic control (SC) and sliding mode control (SMC) to enhance the MPPT technique's performance within the proposed system framework. Moreover, predictive direct power control is applied to the MVSI-based shunt active power filter, utilizing a phase-locked loop technique to achieve multiple objectives: minimizing energy fluctuations, injecting active power, correcting power factors, compensating reactive power, and mitigating harmonic currents. To implement the proposed system, the MATLAB is used for this purpose, with several tests used to study the behavior of the controls proposed in this work. Numerical results indicate significant reductions in active and reactive power fluctuations, with estimated rates of 38.46% and 15.30%, respectively, compared to traditional strategies. Moreover, the total harmonic distortion (THD) of the source current after filtering is reduced by 31.88% under solar irradiation of G = 1000 Wm2. Before filtering, the THD of current experiences a reduction estimated at 97.65%. These findings underscore the superior performance of the proposed control technique across all evaluated aspects.

Enhancing solar photovoltaic energy production prediction using diverse machine learning models tuned with the chimp optimization algorithm.

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

Acousto-Drag Photovoltaic Effect by Piezoelectric Integration of Two-Dimensional Semiconductors.

Light-to-electricity conversion is crucial for energy harvesting and photodetection, requiring efficient electron-hole pair separation to prevent recombination. Traditional junction-based mechanisms using built-in electric fields fail in nonbarrier regions. Homogeneous material harvesting under a photovoltaic effect is appealing but is only realized in noncentrosymmetric systems via a bulk photovoltaic effect. Here we report the realization of a photovoltaic effect by employing surface acoustic waves (SAWs) to generate zero-bias photocurrent in the conventional layered semiconductor MoSe2. SAWs induce periodic modulation to electronic bands and drag the photoexcited pairs toward the traveling direction. The photocurrent is extracted from a local barrier. The separation of generation and extraction processes suppresses recombination and yields a large nonlocal photoresponse. We distinguish the acousto-electric drag and electron-hole pair separation effect by fabricating devices of different configurations. The acousto-drag photovoltaic effect, enabled by piezoelectric integration, offers an efficient light-to-electricity conversion method, independent of semiconductor crystal symmetry.

Coordinated Multi-Scenario Optimization Strategy for Park Photovoltaic Storage Based on Master-Slave Game.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master-slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators' profit, while the lower-level objective is to minimize the user's power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.

The Double-Cross of Benzotriazole-Based Polymers as Donors and Acceptors in Non-Fullerene Organic Solar Cells.

Organic solar cells (OSCs) are considered a very promising technology to convert solar energy to electricity and a feasible option for the energy market because of the advantages of light weight, flexibility, and roll-to-roll manufacturing. They are mainly characterized by a bulk heterojunction structure where a polymer donor is blended with an electron acceptor. Their performance is highly affected by the design of donor-acceptor conjugated polymers and the choice of suitable acceptor. In particular, benzotriazole, a typical electron-deficient penta-heterocycle, has been combined with various donors to provide wide bandgap donor polymers, which have received a great deal of attention with the development of non-fullerene acceptors (NFAs) because of their suitable matching to provide devices with relevant power conversion efficiency (PCE). Moreover, different benzotriazole-based polymers are gaining more and more interest because they are considered promising acceptors in OSCs. Since the development of a suitable method to choose generally a donor/acceptor material is a challenging issue, this review is meant to be useful especially for organic chemical scientists to understand all the progress achieved with benzotriazole-based polymers used as donors with NFAs and as acceptors with different donors in OSCs, in particular referring to the PCE.

Effects of the photovoltaic fishery culture model on muscle nutritional quality and volatile flavor compounds of Litopenaeus vannamei.

This study used Litopenaeus vannamei to compare the muscle nutritional quality and volatile flavor compounds of animals raised in the photovoltaic fishery culture model (PM) and the common pond breeding model (CM). Amino acids, fatty acids, and volatile flavor substances were identified and analyzed using an automatic amino acid analyzer and headspace solid phase microextraction(HS-SPME) combined with GC/MS. There were no significant differences between the two culture models in terms of general nutrients, mineral contents, and amino acid compositions in the muscles of L. vannamei. In the PM group, the proportion of flavor amino acids in total amino acids was higher. Based on the amino acid score (AAS) and chemical score (CS), it was found that methionine and cystine were the first limiting amino acids in the muscle samples. The essential amino acid index (EAAI) value was approximately 77 for both models, indicating high-quality proteins. The muscles contained nine types of fatty acids, with the PM group showing significantly higher levels of both monounsaturated and total fatty acids. A total of 23 volatile flavor compounds were identified in both models. The contents of 1-nonanal, n-tridecane, and alpha-terpineol were higher when cultured in the PM. Conversely, the contents of hexanal, 2-ethylhexanol, and dipentene were lower in the PM group. The photovoltaic fishery culture model has the potential to enhance income through photovoltaic power generation. In addition, this study found that the fatty acid composition of L. vannamei was improved in the PM, without compromising muscle composition or flavor. These results provide a theoretical basis for evaluating the meat quality of L. vannamei under different culture models and offer data to support and guide the promotion of the PM.

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems.

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

Status of photovoltaic water pumping systems in Iran: A comprehensive review.

This study investigates the current status of photovoltaic water pumping systems (PVWPSs) in Iran, a country endowed with significant solar irradiation potential, notably in its southern and central regions. Despite this potential, there is a scarcity of comprehensive studies on solar water pumping systems within the country. This purpose of this study is to conduct a thorough review of the existing literature to assess the state of solar water pumping in Iran. The adoption of PVWPS across various provinces demonstrates the system's versatility, proving effective in both highly sunny and less irradiated regions. Iran's widespread utilization of PVWPS is attributed to its ample irradiations, even in its northern areas, which possess lower solar irradiance levels. There are limited comprehensive studies encompassing technical, economic, environmental, and social aspects of solar PV water pumping projects in Iran. Most of the research has been conducted during the last few years, indicating an increased recognition of the possible advantages of this technology. Finally, this review provides valuable insights for researchers and farmers, showcasing the benefits of solar PVWPS. It sets the stage for further innovation and implementation in the country's agricultural landscape, emphasizing the need for continued exploration and adoption of this sustainable approach.

In Situ Formation of Iodide Precursor for Perovskite Quantum Dots with Application in Efficient Solar Cells.

Perovskite quantum dots (PQDs) become a kind of competitive material for fabricating high-performance solar cells due to their solution processability and outstanding optoelectronic properties. However, the current synthesis method of PQDs is mostly based on the binary-precursor method, which results in a large deviation of the I/Pb input ratio in the reaction system from the stoichiometric ratio of PQDs. Herein, a ternary-precursor method with an iodide source self-filling ability is reported for the synthesis of the CsPbI3 PQDs with high optoelectronic properties. Systematically experimental characterizations and theoretical calculations are conducted to fundamentally understand the effects of the I/Pb input molar ratio on the crystallographic and optoelectronic properties of PQDs. The results reveal that increasing the I/Pb input molar ratio can obtain ideal cubic structure PQDs with iodine-rich surfaces, which can significantly reduce the surface defects of PQDs and realize high orientation of PQD solids, facilitating charge carrier transport in the PQD solids with diminished nonradiative recombination. Consequently, the PQD solar cells exhibit an impressive efficiency of 15.16%, which is largely improved compared with that of 12.83% for the control solar cell. This work provides a feasible strategy for synthesizing high-quality PQDs for high-performance optoelectronic devices.

Mechanism analysis of rural residents' participation in green energy transition: A community-level case study in Nanjing, China.

Achieving a transition to green energy requires the government to adopt new policies on green industrial products and energy. Throughout this process, rural residents often face various challenges due to economic and other factors. However, some rural residents are motivated to participate in this transition due to the economic benefits of energy usage and rooftop photovoltaic (PV). This study takes a rural community in the outskirts of Nanjing, China, as an example and applies Granovetter's embeddedness theory and a threshold model to analyze the factors influencing rural residents' engagement in a fair transition to green energy. Research hypotheses are proposed accordingly. The results indicate that rural residents are influenced by multiple factors in the adoption process of rooftop PV projects, primarily encompassing economic and trust-related aspects. From an economic perspective, rural residents evaluate the viability of rooftop PV systems by considering the marketing strategies employed by PV enterprises and the governmental pressure to reduce carbon emissions. They make rational calculations to determine the return on investment, and only when the economic threshold is surpassed will they reach the anticipated level of participation. From the perspective of trust, rural residents' participation in rooftop PV projects is also influenced by trust factors. The level of trust that rural residents have in rooftop PV enterprises, governments, and community organizations plays an important role in their willingness to participate in the green energy transition. Based on these findings, the research paper concludes that local government should continue providing tailored public information and services to facilitate the progress of rooftop PV projects.

Enhanced LSTM-based robotic agent for load forecasting in low-voltage distributed photovoltaic power distribution network.

To ensure the safe operation and dispatching control of a low-voltage distributed photovoltaic (PV) power distribution network (PDN), the load forecasting problem of the PDN is studied in this study. Based on deep learning technology, this paper proposes a robot-assisted load forecasting method for low-voltage distributed photovoltaic power distribution networks using enhanced long short-term memory (LSTM). This method employs the frequency domain decomposition (FDD) to obtain boundary points and incorporates a dense layer following the LSTM layer to better extract data features. The LSTM is used to predict low-frequency and high-frequency components separately, enabling the model to precisely capture the voltage variation patterns across different frequency components, thereby achieving high-precision voltage prediction. By verifying the historical operation data set of a low-voltage distributed PV-PDN in Guangdong Province, experimental results demonstrate that the proposed "FDD+LSTM" model outperforms both recurrent neural network and support vector machine models in terms of prediction accuracy on both time scales of 1 h and 4 h. Precisely forecast the voltage in different seasons and time scales, which has a certain value in promoting the development of the PDN and related technology industry chain.

Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative study.

Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work.

Electrical switching properties of Ag2S/Cu3P under light and heat excitation.

In this paper, we prepared and investigated the electrical switching behaviors of Cu3P/Ag2S heterojunction in the absence/presence of light/heat excitation. The structure exhibited bipolar memristor characteristics. The resistive switching mechanism is due to the formation of Ag conductive filaments and phase transition in Cu3P. We found that the resistance ratio (ROFF/RON) increased by a factor of 1.4/1.8 after light/heat excitation. The underlying mechanism was due to the photoelectric effect/Seebeck effect. Our results are helpful for the understanding of the resistive switching performance of Cu3P/Ag2S junctions, providing valuable insights into the factors influencing resistive switching performance and a clue for the enhancement of the memristor performance.

Advancing Energy Sustainability Through Solar-to-Fuel Technologies: From Materials to Devices and Systems.

To achieve carbon neutrality and sustainable development, innovative solar-to-fuel systems have been designed through the integration of solar energy harvesting and electrochemical devices. Over the last decade, there have been notable advancements in enhancing the efficiency and durability of these solar-to-fuel systems. Despite the advancements, there remains significant potential for further improvements in the performance of systems. Enhancements can be achieved by optimizing electrochemical catalysts, advancing the manufacturing technologies of photovoltaics and electrochemical cells, and refining the overall design of these systems. In the realm of catalyst optimization, the effectiveness of materials can be significantly improved through active site engineering and strategic use of functional groups. Similarly, the performance of electrochemical devices can be enhanced by incorporating specific additives into electrolytes and optimizing gas diffusion electrodes. Improvements in solar harvesting devices are achievable through efficient passivant and self-assembled monolayers, which enhance the overall quality and efficiency of these systems. Additionally, optimizing the energy conversion efficiency involves the strategic use of DC converters, photoelectrodes, and redox media. This review aims to provide a comprehensive overview of the advancements in solar-powered electrochemical energy conversion systems, laying a solid foundation for future research and development in the field of energy sustainability.