Energy Output Research Articles

Device optimization of all-perovskite triple-junction solar cells for realistic irradiation conditions

All-perovskite two-terminal tandem solar cells, comprising two or more junctions, offer high power conversion efficiencies (PCEs) that exceed the limits of single-junction photovoltaics. Realizing high-efficiency SCs requires carefully optimizing the photoactive layer, front electrodes, and functional layers. Here, we first aim to determine the optimal device architecture, i.e., the perovskites bandgaps and optimum layer thicknesses, for standard test conditions (STCs). We then optimize the energy yield (EY) under realistic outdoor conditions (ROCs), i.e., the overall electrical energy to be expected in one year at a specific location. In the first step, we reference our simulation with two terminal all-perovskite triple-junction SCs (2T3J-PSCs) to previously experimentally realized PSC with a PCE of 20.1% as a benchmark to derive the underlying diode parameters and use our in-house energy yield code combined with a hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) to find the optimal bandgap combination and perovskite layer thicknesses. The optimized SCs with the optimal bandgap combination offer a PCE of 25.1% with a current density of 10.1 mA/cm2. Furthermore, the effect of the other functional layers, such as transparent conductive oxide (TCO), hole transport layer (HTL), and recombination junctions (RJs), is also investigated to enhance the cell performance further. The SCs with optimized layers exhibit improved parameters: PCE of 27.1%, with a relative improvement of 34.8% in the PCE compared with the fabricated cell, and a high current matching a of 10.8 mA/cm2. The numerical results showed that the reported cell can potentially achieve a PCE of 36% at an eVoc/EG ratio of 0.72. However, the optimal parameters may vary in real-world operating conditions due to variations in temperature, humidity, and light exposure. As a result, the optimal energy yield (EY) parameters may differ. Therefore, the energy yield optimizing process is also carried out by considering ROCs in Phoenix, AZ, USA, to find the best parameters under these conditions. We found that the optimum top layer bandgap under ROCs is lower than under STCs due to a bluer, more shifted spectrum in ROCs. After that, the optimized cell under ROCs is tested in several locations exhibiting different climatic conditions (Seattle, Honolulu, Los Angeles, Miami, and Milwaukee). The numerical results show that the optimized cell under ROCs offers an increase in energy yield in several locations compared to conventional single-junction crystalline Si-SCs (PCE = 23.6%) with a high energy yield of 648.2 kWh/m2 in Phoenix, with an improvement of 39.9% in the EY compared to the Si-SC counterpart. Our results provide design guidelines for fabricating a highly efficient triple-junction perovskite SC in the lab and outdoor applications and improve the efficiency of 2T3J-PSCs beyond the 30% limit.

Exergetic analysis and multiparametric optimization of a novel three‐fluid‐based organic Rankine cycle evaporative system via Taguchi method

AbstractEvaluations were conducted on the thermal performance of an organic Rankine cycle (ORC) system using three fluids as the evaporative system at a low‐grade heat source. The modified ORC evaporators were replaced with a three‐fluid system, which included hot fluids at the top and bottom and an isopentane working fluid in the middle section. Furthermore, the thermal performance assessment with a hot fluid heat transfer ratio in the outer and inner tubes (Q2/Q1) varying from 25:75 to 75:25 has been investigated. The impact of the hot fluid's (Q2/Q1) heat transfer ratios to saturated steam on the modified ORC's thermal performance assessment was examined, with an evaporative temperature range of 45–65°C and a pinch point temperature difference (PPTD) of 3–10°C. The Taguchi technique solves multiparameter optimization using the L9 orthogonal array. The findings showed that in three‐fluid‐based modified ORC systems, the network output, exergetic efficiency, and irreversibility went down with PPTD for all three Q2/Q1 cases. For Q2/Q1 of 75:25, the ORC's energetic efficiency and overall irreversibility reached their optimum, while a PPTD of 3–10°C reduced the exergetic efficiency by 19.71%. Also, Q2/Q1 of 75:25 showed the highest and 200% higher ORC system work done at PPTD of 3°C than Q2/Q1 of 25:75—the lowest. Modified ORC network generation, energy output, and heat transfer rate showed excellent results at an evaporative temperature of 58.33°C. For optimal network productivity, Q2/Q1 of 75:25 was 160% and 40% greater than 50:50 and 25:75 at 58.33°C, respectively. The three‐fluid‐based modified ORC system performs better with a 75:25 Q2/Q1 ratio. According to Taguchi's analysis, evaporation temperature affects the improved ORC system's thermal, exergy, and network generation. Also, heat transfer ratios (F = Q2/Q1) largely affect system irreversibility.

Multi‐timescale optimal scheduling of microgrids for generating new energy output scenarios based on correction error sampling intervals

AbstractMicrogrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation.

Multi-scenario Flexibility Requirements Analysis of High Proportion of New Energy Access to Power System

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long time scale components primarily reflecting the overall trend of net load, while the mid-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short time scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for "generation-grid-load-storage" across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Spirulina Unleashed: A Pancreatic Symphony to Restore Glycemic Balance and Improve Hyperlipidemia and Antioxidant Properties by Transcriptional Modulation of Genes in a Rat Model

Hyperlipidemia is the root cause of numerous chronic conditions, leading to high mortality rates around the globe. Spirulina (Arthrospira platensis) microalgae serve as a promising reservoir of bioactive compounds with diverse pharmacological properties. The current study examined the nutritional profile of spirulina powder in relation to strict glycemic control, specifically focusing on its potential to lower lipid levels. In an in vivo investigation, normal healthy male Wistar albino rats (n = 60) were divided into two groups: a negative control group (NC) of ten rats and a high-fat diet group (n = 50) that were fed a cholesterol-rich diet until their cholesterol levels reached or exceeded 250 mg/dL. Subsequently, the hypercholesterolemic rats were then randomly allocated to several treatment groups: a positive control (PC); a standard treatment diet (STD) involving fenofibrate at a dose of 20 mg/kg body weight; and three experimental groups (T1, T2, and T3) that received spirulina powder supplementation at doses of 300, 600, and 900 mg per kg body weight, respectively, for the period of 12 weeks. Blood samples were analyzed for oxidative stress biomarkers, insulin levels, lipid profiles, liver function, and expression of gene levels in the diabetogenic pathway. The study utilized spectrophotometric colorimetric methods to identify oxidative stress biomarkers, serum kit methods to measure lipid profiles and liver enzymes, and the assessment of qPCR for mRNA quantity. According to the research findings, spirulina powder has certain noteworthy features. It had the greatest quantity of chlorogenic acid (4052.90 µg/g) among seven phenolics and two flavonoid compounds obtained by HPLC-UV analysis. Furthermore, the proximate analysis demonstrated that spirulina is high in protein (16.45 ± 0.8%) and has a significant energy yield of 269.51 K-calories per 100 g. A maximal spirulina dose of 900 mg/kg/wt significantly lowered oxidative stress, cholesterol, triglyceride, low-density lipoproteins (LDL), and insulin levels (p ≤ 0.05). In contrast, high-density lipoprotein (HDL) and total antioxidant capacity (TAC) levels increased significantly (p ≤ 0.05) compared to all other groups, except the NC group. The study provides remarkable proof about the pharmacological impact of spirulina powders. Significant reductions (p ≤ 0.05) in liver enzymes {alanine aminotransferase (ALT) and aspartate aminotransferase (AST)} were observed across all treatment groups, with the exception of the NC, compared to the positive control. The treatment groups had significantly greater gene expression levels of INS-1, PDX-1, IGF-1, and GLUT-2 than the positive control group (p ≤ 0.05). These findings highlight spirulina’s potential as a long-term regulator of hyperglycemia in rat models with induced hyperlipidemia, owing to its phenolic bioactive components that serve as antioxidants.

The impact of long-term organic horticultural systems on energy outputs and carbon storages in relation to extreme rainfall events

Enhancing resilience of agroecosystems of Mediterranean area is a challenge that involves both researchers and different stakeholders and, in this context, increasing crop diversity by redesigning agricultural systems can be considered among the most important tools. Therefore, the response of agroecological practices to climate change effects was tested in a long-term experiment on organic horticultural crops (MITIORG), which is characterized by a soil hydraulic arrangement in ridges, strips and the use (with different management options) of cover crops within cash crops rotations. The main objective of this study was to show how powerful is the sustainability assessment of agroecological practices by converting crops yield and biomass into energy outputs and carbon storages, in diversified horticultural systems. The obtained outputs (expressed in energy and carbon equivalents) were evaluated and analyzed considering the site-specific meteorological data in more than 10 horticultural cropping cycles, from autumn-winter 2014-15 to autumn-winter 2020-21. The Ridge and Strips (RS) system 1 (RS1 - cover crops as living mulch on ridges and break crops in strips, both with no-till termination) showed an enhancement of about 18% of energy output and carbon (C) storages compared to RS2 (ridges and strips with green manured cover) when extreme precipitation events occurred. Moreover, RS3 (ridges and strips without cover crops) recorded a reduction of about 5 and 9% of energy output and C storage, respectively, compared to the mean of RS1 and RS2 in periods with extreme events. Our results highlighted that using more diversified agroecological systems improved their overall average outputs, ensuring greater resilience during extreme weather events, since at least part of crop productions was safeguarded. Therefore, it is important to combine techniques that allow long-term resilience, such as choosing and well managing cover crops (agroecological service crops), according to site and systems specific conditions.

Fluorinated co-solvent electrolytes enable lithium metal batteries to operate at low temperatures

Enhancing the energy output of batteries in low temperatures is critical to broadening the application areas of advanced electronic devices, which can be accomplished by utilizing high-voltage cathodes to match lithium metal anode, optimizing electrolyte electrochemical windows and forming stable solid electrolyte interphases to provide facile ion-transmission kinetics at low temperature. Herein, we demonstrate a fluorinated ester co-solvent electrolyte, which applies 1 M LiPF6 in an ethyl 4,4,4-trifluoro butyrate/fluoroethylene carbonate (FEC) (4/1 by volume ratio) that jointly satisfies high voltage cathode and lithium metal anode reversibility at required ambient. The performance of the battery is due to the rapid dissociation of Li+ and the formation of a fluorine-rich inorganic electrolyte interface in the fluorinated solvent. Therefore, the fluorinated ester co-solvent electrolyte system can provide 209.9 mAh g−1, 194.8 mAh g−1, and 175.5 mAh g−1 when discharged at room temperature, −20 ℃ and −40 ℃, respectively, those far exceeds the performance of carbonate electrolytes. This work advances the development of low-temperature lithium metal batteries.

Design optimization of floating offshore wind farms using a steady state movement and flow model

Abstract In this study, we demonstrate how to carry out the design optimization of floating offshore wind farms by considering the impacts brought by the movements of the floating wind turbines. A steady state movement and flow model is applied in the modelling process. This model is developed by building a turbine surrogate model for estimating the steady state movement and power production of floating wind turbines and incorporating it in an open-source static wind farm simulator, PyWake. Using this modelling methodology, layout optimization problems of floating offshore wind farms are solved by using algorithms like Random Search to maximize the energy yield while satisfying certain constraints. Case studies are carried out for two wind farms with met-ocean conditions from a real planned site in Scottland to show the potential of layout optimization for floating wind farms.

Experimental Study of Bifacial Photovoltaic Module Performance on a Sunny Day with Varying Backgrounds Using Exergy and Energy Analysis

In this research, ethe performance of bifacial photovoltaic (PV) modules under varying background conditions is explored, specifically green grass, brown clay, and white gravel, on a sunny day. By leveraging both exergy and energy analysis, this research aims to provide a comprehensive evaluation of bifacial module efficiency compared to traditional monofacial modules. The experimental setup simulates diverse installation environments, including rooftops and ground-mounted systems, by varying background reflectance. Key performance metrics such as energy yield, exergy yield, and overall efficiency were measured. The findings reveal that bifacial modules installed over white gravel backgrounds achieve the highest exergy profile and efficiency during peak solar radiation periods, attributed to the enhanced reflectivity of white gravel. These insights can inform strategic decisions regarding the selection and placement of bifacial modules to optimize energy and exergy outputs in real-world scenarios. This study contributes valuable knowledge to the advancement of renewable energy technologies, offering guidance for researchers, developers, and policymakers focused on sustainable energy solutions.

Nutrient and energy conservation through nano-fertilizers in maize

In the current scenario, achieving food security while conserving resources and energy is a significant challenge. Maize is a widely cultivated but nutrient-exhaustive crop. The adoption of nanotechnology-based nano-fertilizers offers a pathway to achieving sustainable yields while reducing fertilizer requirements and conserving energy. A field experiment was conducted during the Kharif season of 2021 to explore nutrient and energy conservation through nano-fertilizers in maize at the University of Agricultural Sciences, GKVK, Bengaluru. The experiment involved nine treatments comprising various combinations of the recommended dose of fertilizers (RDF) with nano-urea and nano Di-Ammonium Phosphate (DAP) under a Randomized Complete Block Design (RCBD). The results indicated that Treatment T5 - 75% of the Recommended Dose of Nitrogen (RDN) + Nano-N—achieved a higher yield (10.20% higher than the conventional practice, T1-RDF + Farmyard Manure (FYM)) and improved nutrient uptake at harvest [299.22, 55.56, and 208.26 kg of nitrogen (N), phosphorus (P), and potassium (K) per hectare, respectively]. This treatment also demonstrated greater physiological efficiency (36.11, 200.66, and 52.70 kg of maize per kg of N, P, and K, respectively), higher energy output (260,851 MJ ha?¹), improved energy use efficiency (16.93), enhanced energy productivity (0.627 kg MJ?¹), and better energy profitability (15.93). Using 75% of RDN + Nano-N increases yield while reducing fertilizer use and conserving energy.

Reinforcement Learning for EV Fleet Smart Charging with On-Site Renewable Energy Sources

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK’s target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions.

Advances in Cold-Climate-Responsive Building Envelope Design: A Comprehensive Review

Extreme low temperatures, heavy snowfall, ice accumulation, limited daylight, and increased energy consumption in cold climates present significant challenges but also offer opportunities for improving building efficiency. Advanced materials and technologies in climate-responsive envelopes can enhance sustainability, reduce carbon footprints and operational costs, and improve thermal comfort under these environmental conditions. This literature review combines theoretical aspects of building performance in cold climates with a summary of current and critical applications in building envelope design, identifying research gaps and proposing future research directions. It has been shown that various BIPV systems require further climate-based studies to optimize solar energy yield. For example, integrating PV layers and PCM within DSFs can reduce cooling loads, but more research is needed on PCM transition temperatures and ventilation strategies in cold climates. A notable research gap exists in building-integrated vegetative systems, particularly regarding soil thickness, irrigation, hygrothermal performance, and snow accumulation. Despite excellent winter performance in buildings incorporating CLT components, they face increased cooling energy consumption and potential overheating in summer. Additionally, the high initial moisture content in CLT raises the risk of mold growth, especially when covered with vapor-tight layers. The design examples in this paper emphasize the need for further investigation to achieve sustainable, low-carbon, energy-efficient envelope designs for cold climates.

On-Farm Production of Renewable Energy in 2014–2022

The main purpose of this study is to present family farms as consumers and producers of renewable energies which provide them with an opportunity to reduce operating costs. The time scope of the study is 2014–2022, and the Farm Accountancy Data Network is used as the data source. The following research methods were employed: comparative and descriptive analysis, intensity indicators, ranking assignment and panel regression. Based on the values of energy output and energy costs, the rankings revealed a strong position of the Netherlands and Germany. As demonstrated by the study, energy production and consumption volumes depend on the farms’ economic size, but are not impacted by production type. Another finding is that energy production covers only one-third of its costs. Also, both production volumes and costs were on a growth path on a year-over-year basis, with similar growth ratios. The European Union’s leaders in energy consumption and production are the Netherlands, Germany, Belgium, Denmark, Czech Republic, Hungary, Luxembourg, Slovakia and Sweden. The study included the structuring of panel models for energy output and costs and identified their determinants. Energy output depends on total inputs and grows as they grow. Energy costs, in turn, are related to utilized agricultural area, total output and family farm income. An important limitation of this study is that FADN is a provider of high-level data. Hence, it is impossible to tell what specific sources of renewable energy are used by farms, and how they are affected by such exogenous factors as climate, earmarked subsidies or energy policy. The findings from this study are discussed in the context of the European Commission’s recommendations laid down in the Bioeconomy Strategy of the EU (2013), the Seventh Environment Action Program, the New Innovation Agenda of the European Union, the Report “Transforming Our World: the United Nations 2030 Agenda for Sustainable Development” and the Circular Economy Action Plan.

Thermal engineering and chemical kinetics of wood pellets torrefication

Relevance. The widespread distribution and availability of biomass in the regions of Russian Federation make it possible to consider it as a renewable energy source with a lower negative impact on the environment compared to fossil fuels, since biomass is recognized as a carbon-neutral fuel. The use of biomass as a fuel in the form of pellets is increasing, which provides increased density and heat value. At the same time, pellets as a fuel or as a technological raw material have a significant disadvantage – low hydrophobicity. This problem is solved by torrefaction – low-temperature pyrolysis at a temperature of 200–300°C to produce bio-carbon. The study of the wood pellets torrefaction in order to improve it is an urgent task. Aim. To identify the impact of the parameters of the torrefaction of wood pellets on the thermal characteristics of the product (the highest heat value of bio-coal) and the regime parameters of the process (mass and energy yields), as well as to determine the parameters of the chemical kinetics of low-temperature pyrolysis as the basis for mathematical modeling and development of efficient torrefaction reactors. Object. Torrefied coniferous wood pellets of domestic production (Moscow region). Methods. Experimental study of the low-temperature pyrolysis thermal characteristics; synchronous thermal analysis in an inert atmosphere. Results. It was found that during the torrefaction of wood pellets in an inert atmosphere, a change in the holding time in the range of 30–60 minutes does not significantly affect the higher heat value, mass and energy yield of bio-coal, while an increase in temperature from 250 to 300°C reduces mass and energy yield, and the higher heat value increases by 26%. The dynamics of mass loss and thermal effects when heated to 600°C at a rate of 10 K/min are determined by means of the synchronous thermal analysis. It is established that the chemical kinetics of the process is described by a generalized reaction of the order n=1.507, in the Arrhenius equation the activation energy E=71.25 kJ/mol, the pre-exponential factor A=4772 s–1.

AI-driven predictive maintenance and optimization of renewable energy systems for enhanced operational efficiency and longevity

The rapid growth of renewable energy systems necessitates advanced strategies for maintenance and optimization to ensure long-term operational efficiency and sustainability. Traditional approaches often fall short in predicting failures and optimizing performance across diverse and dynamic renewable energy infrastructures. This study investigates the application of artificial intelligence (AI) techniques for predictive maintenance and optimization of renewable energy systems, with the aim of enhancing operational efficiency and extending system longevity. We employ a combination of machine learning algorithms, including deep neural networks and reinforcement learning, to develop predictive models and optimization strategies. These models are trained on large-scale datasets collected from operational wind farms, solar installations, and hydroelectric plants. Our results demonstrate that AI-driven approaches can predict equipment failures with 92% accuracy, reducing unplanned downtime by 35% compared to traditional methods. Moreover, AI-optimized operational parameters improved overall energy output by 8.5% across the studied systems. The proposed framework also showed adaptability to various environmental conditions and system configurations, suggesting broad applicability across the renewable energy sector. This research underscores the significant potential of AI in revolutionizing maintenance practices and operational strategies in renewable energy systems, paving the way for more reliable, efficient, and sustainable clean energy production.

Management of Hybrid Hydropower and Floating Solar Systems at Num Ngum 1 in Lao PDR

The integration of a hybrid hydro-floating solar power (HPP-FPV) system is covered in this study with the goal of improving energy management and producing more electricity. The production capacity of the hybrid system is almost twice as high as that of the original HPP system, demonstrating its potential for higher energy generation. Because it forecasts weather and modifies electricity generation accordingly. The analysis highlights how crucial it is to make sure that the hybrid system's electricity generation doesn’t go beyond the current power grid's maximum transmission capacity. This is essential to preserving the consistency and caliber of the power that is provided. The research's overall goal of increasing the output of renewable energy through creative system integration and practical management techniques is captured in the abstract.

Investigating the Performance of Glass Fibre-Reinforced Polymer (GFRP) in the Marine Environment for Tidal Energy: Velocity, Particle Size, Impact Angle and Exposure Time Effects

Tidal energy, with its potential to provide a consistent energy output and reduce carbon emissions, has garnered significant interest. This study, which evaluates the performance of tidal turbine blades in seawater conditions and with sand particles, presents a novel approach. A slurry rig was developed to examine composite materials, and a glass fibre-reinforcement polymeric material was tested over a range of particle sizes, velocities, and impact angles. In addition, this paper used a new test protocol with 14 days (336 h) and 91 days (2184 h) of pre-exposure time of materials before testing. The results, which show significant changes in the erosive mechanisms of GFRP in short- and long-term pre-exposure time as a function of these variables, have profound implications for the design and performance of tidal turbine blades. The study also utilised scanning electron microscopy (SEM), depth profiling analysis, and erosion mapping techniques to compare the erosion behaviours of GFRP. These tools can be used to optimise such materials in tidal turbine conditions.

Iterative approach for photonic crystal devices design

Background. An approach to the design of photonic crystal (PhC) devices is proposed, which differs from the known general-purpose optimization methods (for example, a genetic algorithm or gradient procedures) by using information about diffraction patterns at different frequencies when optimizing an element designed to operate at the selected wavelength. The following is an approach to the design of photonic crystal elements. The proposed approach differs from known general-purpose optimization methods (e.g., genetic algorithm or gradient procedures) by using diffraction pattern information at different frequencies while optimizing an element designed to operate at the selected wavelength. The design of functional PhC structures with expected characteristics (for example, waveguides) for a certain wavelength (albeit given by a monochromatic radiation source) is described. Aim. Development based on the FDTD method and confirmation of the functionality of the iterative procedure for calculating the characteristics of metal-dielectric PhC lattices. Methods. The study is based on an iterative approach to the design of PhC elements based on the use of the FDTD method. Results. Model examples were used for demonstration of practical convergence and applicability of the developed iterative procedure. The efficiency of the PhC waveguide, understood as the ratio of the output energy to the input energy, was increasing at each iteration up to 97.2%. Conclusion. The method of synthesis of metal-dielectric PhC structures with preset properties based on application of developed iterative procedure is proposed and argued. The results of the analysis of the 2D PhC waveguide based on a set of round copper rods show the applicability of the proposed method.

Design an on-grid PV system to supply electricity to a school in Babil city using PVsyst software

The world attention is increasingly directed towards solar energy, particularly in countries of high solar radiation rates such as Iraq, due to the limitation of fossil fuel resources and the high pollution rates associated with using them. Engineers and researchers utilize different techniques in order to design the photovoltaic systems with low cost and high performance. This research aims at designing an optimal on-grid PV system to feed a school, located at Babel, Iraq using PVsyst program. The PVsyst methodology includes defining the geographical location of the project, determining the demand electrical load, selecting the type of the PV system components, and finally running the simulation. Detailed results, including the produced energy, loses diagram, economic and environmental evaluations can be obtained. The results show that a PV system comprising of 90 (450 Wp) panels and 3 (12 kWac) inverters on an area of 198 m2 can produce a net output energy at 70.23 MWh. The economic evaluation show that the project cost can be recovered within less than 10 years, and a net positive gain up to 20,000 USD can be attained during the 25 years project lifetime. A huge carbon emission equal to 1279.6 tCo2 can be saved as a result of this PV system installation. There is an obvious need to invest in solar energy for economic and environmental considerations. The main drawback of PV systems installations is the high capital cost. Using commercial packages, particularly PVsyst program, can significantly contributes toward optimizing the system design and hence reducing the project cost.