Solar and wind energy potential assessment for Razavi Khorasan Province in Iran

This study aims to evaluate the solar and wind energy potential across Razavi Khorasan Province,Iran,with a specific focus on the Khaf region.A preliminary assessment of mean solar radiation,mean wind speeds,and Weibull distribution parameters was conducted for different towns and zones within the province.The findings showed that Khaf has favorable characteristics for further analysis.The solar and wind energy metrics examined include global horizontal irradiance,clearness index,wind rose patterns,and turbulence intensity.At a height of 40 m,Khaf’s wind power density reached 1650 W/m^(2),indicating exceptional wind energy generation potential.Additionally,Khaf received an average annual solar radiation of 2046 kW·h/m^(2),representing significant solar energy potential.Harnessing these substantial renewable resources in Khaf could allow Razavi Khorasan Province to reduce reliance on fossil fuels,improve energy sustainability,and mitigate climate change impacts.This research contributes an in-depth assessment of Razavi Khorasan's solar and wind energy potential,particularly for the promising Khaf region.Further work may examine optimal sites for renewable energy projects and grid integration strategies to leverage these resources.

Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model accuracy.Various models were developed and trained with different preprocessing stages.Finally,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction errors.Results indicated that models trained on raw data generally performed better than those on stripped data.The Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling techniques.The study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.

Vertically aligned montmorillonite aerogel-encapsulated polyethylene glycol with directional heat transfer paths for efficient solar thermal energy harvesting and storage

The conversion and storage of photothermal energy using phase change materials(PCMs)represent an optimal approach for harnessing clean and sustainable solar energy.Herein,we encapsulated polyethylene glycol(PEG)in montmorillonite aerogels(3D-Mt)through vacuum impregnation to prepare 3D-Mt/PEG composite PCMs.When used as a support matrix,3D-Mt can effectively prevent PEG leakage and act as a flame-retardant barrier to reduce the flammability of PEG.Simultaneously,3D-Mt/PEG demonstrates outstanding shape retention,increased thermal energy storage density,and commendable thermal and chemical stability.The phase transition enthalpy of 3D-Mt/PEG can reach 167.53 J/g and remains stable even after 50 heating-cooling cycles.Furthermore,the vertical sheet-like structure of 3D-Mt establishes directional heat transport channels,facilitating efficient phonon transfer.This configuration results in highly anisotropic thermal conductivities that ensure swift thermal responses and efficient heat conduction.This study addresses the shortcomings of PCMs,including the issues of leakage and inadequate flame retardancy.It achieves the development and design of 3D-Mt/PEG with ultrahigh strength,superior flame retardancy,and directional heat transfer.Therefore,this work offers a design strategy for the preparation of high-performance composite PCMs.The 3D-Mt/PEG with vertically aligned and well-ordered array structure developed in this research shows great potential for thermal management and photothermal conversion applications.

Rapid and stable calcium-looping solar thermochemical energy storage via co-doping binary sulfate and Al–Mn–Fe oxides

Solar thermochemical energy storage based on calcium looping(CaL)process is a promising technology for next-generation concentrated solar power(CSP)systems.However,conventional calcium carbonate(CaCO_(3))pellets suffer from slow reaction kinetics,poor stability,and low solar absorptance.Here,we successfully realized high power density and highly stable solar thermochemical energy storage/release by synergistically accelerating energy storage/release via binary sulfate and promoting cycle stability,mechanical strength,and solar absorptance via Al–Mn–Fe oxides.The energy storage density of proposed CaCO_(3)pellets is still as high as 1455 kJ kg^(-1)with only a slight decay rate of 4.91%over 100 cycles,which is higher than that of state-of-the-art pellets in the literature,in stark contrast to 69.9%of pure CaCO_(3)pellets over 35 cycles.Compared with pure CaCO_(3),the energy storage power density or decomposition rate is improved by 120%due to lower activation energy and promotion of Ca^(2+)diffusion by binary sulfate.The energy release or carbonation rate rises by 10%because of high O^(2-)transport ability of molten binary sulfate.Benefiting from fast energy storage/release rate and high solar absorptance,thermochemical energy storage efficiency is enhanced by more than 50%under direct solar irradiation.This work paves the way for application of direct solar thermochemical energy storage techniques via achieving fast energy storage/release rate,high energy density,good cyclic stability,and high solar absorptance simultaneously.

Cascade utilization of full spectrum solar energy for achieving simultaneous hydrogen production and all-day thermoelectric conversion

Solar-driven photocatalytic water/seawater splitting holds great potential for green hydrogen production.However,the practical application is hindered by the relatively low conversion efficiency resulting from the inadequate utilization of solar spectrum with significant waste in the form of heat.Moreover,current equipment struggles to maintain all-day operation subjected to the lack of light during nighttime.Herein,a novel hybrid system integrating photothermal catalytic(PTC)reactor,thermoelectric generator(TEG),and phase change materials(PCM)was proposed and designed(named as PTC-TEG-PCM)to address these challenges and enable simultaneous overall seawater splitting and 24-hour power generation.The PTC system effectively maintains in an optimal temperature range to maximize photothermal-assisted photocatalytic hydrogen production.The TEG component recycles the low-grade waste heat for power generation,complementing the shortcoming of photocatalytic conversion and achieving cascade utilization of full-spectrum solar energy.Furthermore,exceptional thermal storage capability of PCM allow for the conversion of released heat into electricity during nighttime,contributing significantly to the overall power output and enabling PTC-TEG-PCM to operate for more than 12 h under the actual condition.Compared to traditional PTC system,the overall energy conversion efficiency of the PTC-TEG-PCM system can be increased by∼500%,while maintaining the solar-to-hydrogen efficiency.The advancement of this novel system demonstrated that recycling waste heat from the PTC system and utilizing heat absorption/release capability of PCM for thermoelectric application are effective strategies to improve solar energy conversion.With flexible parameter designing,PTC-TEG-PCM can be applied in various scenarios,offering high efficiency,stability,and sustainability.

KnowledgeMapping of Hybrid Solar PV andWind Energy Standalone Systems: A Bibliometric Analysis

Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive,and their use would release pollutants.Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy.Hybrid renewable energy systems(HRES)are viable for remote areas operating in standalone mode.This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades.More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace to present the results with figures and tables.Productive countries and highly cited authors are identified,and hot topics with hotspot articles are shown in landscape and timeline views.Emerging trends and new developments related to techno-economic analysis and microgrids,as well as the application of HOMER software,are predicted based on the analysis of citation bursts.Furthermore,the opportunities of hybrid energy systems for sustainable development are discussed,and challenges and possible solutions are proposed.The study of this paper provides researchers with a comprehensive understanding and intuitive representation of standalone solar-wind hybrid energy systems.

Assessing Factors Influencing Solar Energy Adoption among Small and Medium Enterprises in Mzuzu City, Malawi

One of the many renewable energy sources that offer advantages is solar energy, which also lowers energy prices and promotes environmental sustainability and energy security. Despite these advantages, various barriers, such as installation costs, have prevented small and medium-sized enterprises from investigating this invention. Malawi has a significant energy shortfall such that most businesses have been hindered from their profit maximization goals. The “Photovoltaic systems” (PV) that transform sunlight into electricity are the subject of this study. This type of solar energy system is situated on the building’s roof and generally produces electricity for businesses and even homes. Solar energy offers a great impact to small and medium enterprises in Mzuzu city with a cost-effective and dependable alternative to energy that has the potential to change the game. Therefore the aim of the study was to identify factors that encourage the adoption of solar energy among small medium enterprises in the city of Mzuzu city. And to identify some of barriers faced when adopting solar energy among small and medium enterprises in the city of Mzuzu. The research approach employed in the study was a survey. A survey is a type of research methodology in which primary data is gathered from a sample using a questionnaire. When information is to be gathered from a wider sample, a survey is employed. A bigger sample size was needed in this study in order to facilitate hypothesis testing. It is advised to apply a logical approach while using the survey. The survey utilized a five-point Likert scale. The study used convenience sampling to select study participants. The sample size in this study was determined using Cochran’s sample size formula. Statistical Package for Social Sciences (SPSS) and Microsoft Excel were used for statistical analysis. About 97.2% of the participants were aware of solar as a source of energy compared to 2.8 % who were unaware. The majority of participants use solar energy for lighting only, seconded by those who use electricity. The least number of participants use solar energy for cooling only. The majority of participants 21.5% indicated partnership and collaboration as the most motivating factor for the adoption of solar energy. This was followed by technical expertise 19.1 % the least number of participants 10.8% expressed that policy and regulatory frameworks were associated with the adoption of solar energy. This study found that there are no statistically significant factors influencing barriers to the adoption of solar energy. The price of solar energy adoption was identified as the least factor associated with the acceptance or rejection of solar energy. Nonetheless, the reasons given by the homes that had embraced solar technology aligned with the findings of other studies. This survey also found that although the public was aware of solar energy, and technology, there were still a number of factors that mattered, especially for non-adopters.

Analysis of Current Research and Future Development Trends of Applying Solar Energy in Street Lighting

In the context of promoting green energy transition and addressing climate change globally,solar energy,as a clean and renewable energy source,has gradually become a hot topic for research.Solar streetlight systems realize energy self-sufficiency and environment-friendly lighting by integrating photovoltaic power generation technology and efficient LED lighting technology.By comprehensively analyzing the current status of the application of solar streetlights at home and abroad,this paper discusses its technical advantages,market penetration,and challenges in its development.In terms of technical characteristics,this paper focuses on analyzing the key technologies such as energy conversion efficiency and intelligent control systems of solar streetlights.

Pinning energies of organic semiconductors in high-efficiency organic solar cells

With the emergence of new materials for high-efficiency organic solar cells(OSCs),understanding and finetuning the interface energetics become increasingly important.Precise determination of the so-called pinning energies,one of the critical characteristics of the material to predict the energy level alignment(ELA)at either electrode/organic or organic/organic interfaces,are urgently needed for the new materials.Here,pinning energies of a wide variety of newly developed donors and nonfullerene acceptors(NFAs)are measured through ultraviolet photoelectron spectroscopy.The positive pinning energies of the studied donors and the negative pinning energies of NFAs are in the same energy range of 4.3−4.6 eV,which follows the design rules developed for fullerene-based OSCs.The ELA for metal/organic and inorganic/organic interfaces follows the predicted behavior for all of the materials studied.For organic-organic heterojunctions where both the donor and the NFA feature strong intramolecular charge transfer,the pinning energies often underestimate the experimentally obtained interface vacuum level shift,which has consequences for OSC device performance.

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years.Photovoltaic(PV)technology is the fastest technique to convert solar radiation into electricity.Solar-powered buses,metros,and cars use PV technology.Such technologies are always evolving.Included in the parameters that need to be analysed and examined include PV capabilities,vehicle power requirements,utility patterns,acceleration and deceleration rates,and storage module type and capacity,among others.PVPG is intermit-tent and weather-dependent.Accurate forecasting and modelling of PV sys-tem output power are key to managing storage,delivery,and smart grids.With unparalleled data granularity,a data-driven system could better anticipate solar generation.Deep learning(DL)models have gained popularity due to their capacity to handle complex datasets and increase computing power.This article introduces the Galactic Swarm Optimization with Deep Belief Network(GSODBN-PPGF)model.The GSODBN-PPGF model predicts PV power production.The GSODBN-PPGF model normalises data using data scaling.DBN is used to forecast PV power output.The GSO algorithm boosts the DBN model’s predicted output.GSODBN-PPGF projected 0.002 after 40 h but observed 0.063.The GSODBN-PPGF model validation is compared to existing approaches.Simulations showed that the GSODBN-PPGF model outperformed recent techniques.It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.

CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.

Recent advances in graphene-based phase change composites for thermal energy storage and management

Energy storage and conservation are receiving increased attention due to rising global energy demands.Therefore,the development of energy storage materials is crucial.Thermal energy storage(TES)systems based on phase change materials(PCMs)have increased in prominence over the past two decades,not only because of their outstanding heat storage capacities but also their superior thermal energy regulation capability.However,issues such as leakage and low thermal conductivity limit their applicability in a variety of settings.Carbon-based materials such as graphene and its derivatives can be utilized to surmount these obstacles.This study examines the recent advancements in graphene-based phase change composites(PCCs),where graphene-based nanostructures such as graphene,graphene oxide(GO),functionalized graphene/GO,and graphene aerogel(GA)are incorporated into PCMs to substantially enhance their shape stability and thermal conductivity that could be translated to better storage capacity,durability,and temperature response,thus boosting their attractiveness for TES systems.In addition,the applications of these graphene-based PCCs in various TES disciplines,such as energy conservation in buildings,solar utilization,and battery thermal management,are discussed and summarized.

Utilization of Solar Energy in Irrigation Systems in Bangladesh

The demand for water pumping in urban water supply and irrigation in Bangladesh is significantly influenced by electricity deficits and high diesel costs. To address these challenges, the adoption of solar power for water pumping emerges as a viable alternative to traditional systems reliant on grid power and diesel. In recent years, there has been a growing emphasis on clean and renewable energies, aligning with the environmental and economic priorities of Bangladesh. The agricultural sector, serving as the backbone of the country’s economy, witnesses an escalating demand for water as the population increases. The extraction and transfer of water for agricultural and drinking purposes translate to high-energy consumption. Leveraging the abundant and essentially free solar energy, particularly during the crop growth periods when irrigation is crucial, presents an optimal solution. This study underscores the underutilization of this vital resource in Bangladesh and advocates for the widespread implementation of solar energy conversion programs, specifically in photovoltaic pumping systems. By comparing these systems with conventional diesel pumps, this paper aims to inspire policymakers, statesmen, and industry professionals to integrate green energy into the water sector. The envisioned outcome is a strategic shift towards sustainable development, with a focus on harnessing solar power to pump water for villages and agriculture, thus contributing to economic and environmental sustainability.

Optimal Location to Use Solar Energy in an Urban Situation

This study conducted in Lima, Peru, a combination of spatial decisionmaking system and machine learning was utilized to identify potentialsolar power plant construction sites within the city. Sundial measurementsof solar radiation, precipitation, temperature, and altitude were collectedfor the study. Gene Expression Programming (GEP), which is based on theevolution of intelligent models, and Artificial Neural Networks (ANN) wereboth utilized in this investigation, and the results obtained from each werecompared. Eighty percent of the data was utilized during the training phase,while the remaining twenty percent was utilized during the testing phase. Onthe basis of the findings, it was determined that the GEP is the most suitablenetwork for predicting the location. The test state’s Nash-Sutcliffe efficiency(NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Followingthe generation of the final map based on the results of the GEP model, itwas determined that 9.2% of the province’s study area is suitable for theconstruction of photovoltaic solar power plants, while 53.5% is acceptable and37.3% is unsuitable. The ANN model reveals that only 1.7% of the study areais suitable for the construction of photovoltaic solar power plants, while 66.8%is acceptable and 31.5% is unsuitable.

Investment in Generation of Photovoltaic Solar Energy: A Fezsibility Study with Flexibility and Uncertainty

The objective of this research will be to calculate the feasibility of investing in a solar energy generation project through the development of a methodology that allows the capture of environmental uncertainties by improving decision making. The article presents a comparative study of the feasibility analysis of investment in a solar mini solar energy for a Shopping, considering a regime of certainty and uncertainty. The assumed stochastic variables were energy tariff and price of solar panels. The trajectories were simulated with the binomial approach that combined resulted in a quadratic diagram. The applied methodology presented the best recommendation and the option to wait was the most valuable. The exchange of the energy obtained from LIGHT by own generation of energy with solar photovoltaic source will be viable for the manager since it observes the behavior of the variables over time and follows the rules of optimal decision.

A Solar Energy System Design for Green Hydrogen Production in South-Western Nigeria, Lagos State, Using HOMER & ASPEN

Solar system design for green hydrogen production has become the most prominent renewable energy research area, and this has also actively fueled the desire to achieve net-zero emissions. Hydrogen is a promising energy carrier because it possesses more energy capacity than fossil fuels and the abundant nature of renewable energy systems can be utilized for green hydrogen production. However, the design of an optimized electrical energy system required for hydrogen production is crucial. Solar energy is indeed beneficial for green hydrogen production and this research designed, discussed, and provided high-level research on HOMER design for green hydrogen production and deployed the energy requirement with ASPEN Plus to optimize the energy system, while also incorporating fuzzy logic and PID control approaches. In addition, a promising technology with a high potential for renewable hydrogen energy is the proton exchange membrane (PEM) electrolyzer. Since its cathode (hydrogen electrode) may be operated over a wide range of pressure, a control process must be added to the system in order for it to work dynamically efficiently. This system can be characterized as an analogous circuit that consists of a resistor, capacitor, and reversible voltage. As a result, this research work also explores the Fuzzy-PID control of the PEM electrolysis system. Both the PID and Fuzzy Logic control systems were simulated using the control simulation program Matlab R2018a, which makes use of Matlab script files and the Simulink environment. Based on the circuit diagram, a transfer function that represents the mathematical model of the plant was created, and the PEM electrolysis control system is determined to be highly significant and applicable to the two control systems. The PI controller, however, has a 30.8% overshoot deficit, but when the fuzzy control system is compared to the PID controller, it is found that the fuzzy control system achieves stability more quickly, demonstrating its benefit over PID.

The Benefits of Solar Energy on the Provision of Sustainable Affordable Housing in Nigeria

Solar energy is the most abundant form of energy on Earth. Solar energy brings impactful benefits and products that are expected to make homes more reliable, sustainable, and affordable. Thanks to technological advancements like the solar cell, we can gather this energy and turn it into electricity. The construction industry has an exceptional chance of benefiting from this sustainable energy. Many recognised benefits have been spelled forth in the construction industry, such as providing homes with clean energy with no trace of ozone depleting material emission. There are many people in Nigeria who are not linked to the public electric grid, and the energy sector produces and generates less than 58% of the entire amount of energy required. As stated in the Nigeria’s National Energy General Plan, the Sustainable Energy programme aims to enhance the country’s use of solar electricity. This paper focuses on the role of solar energy in the provision of sustainable affordable housing in Nigeria. It considers the description, method, and utilisation of solar energy with a focus on residential and commercial buildings.

Solar Energy Harvesting Using a Timer-Based Relay Selection

In this paper,the throughput and delay of cooperative communications are derived when solar energy is used and relay node is selected using a timer.The source and relays harvest energy from sun using a photo voltaic system.The harvested power is used by the source to transmit data to the relays.Then,a selected relay amplifies the signal to the destination.Opportunistic,partial and reactive relay selection are used.The relay transmits when its timer elapses.The timer is set to a value proportional to the inverse of its Signal to Noise Ratio(SNR).Therefore,the relay with largest SNR will transmit first and its signal will be detected by the other relays that will remain idle to avoid collisions.Harvesting duration is optimized to maximize the throughput.Packet’s waiting time and total delay are also computed.We also derive the statistics of SNR when solar energy is used.The harvested power from sun is proportional to the sum of a deterministic radiation intensity and a random attenuation due to weather effects and clouds occlusion.The fixed radiation intensity depends on season,month and time t in hour.The throughput of cooperative communications with energy harvesting from sun was not yet studied.

Potentiality of Solar Photovoltaic Energy in Paraiba, Brazil, and the Relation with Sustainable Development

The use of non-renewable energy has been a major environmental concern and, therefore, there is a need to look for other renewable energy sources, especially photovoltaic’s. In view of this, an attempt was made to quantify the potential of solar irradiance in the State of Paraiba, as an alternative source for conversion and use in electrical energy, these determinations being the main objectives. Global solar irradiance and solar photovoltaic data were extracted from scientific publications and/or made available on the websites of the National Institute of Meteorology (INMET), the Ministry of Mines and Energy and the National Electric Energy Agency, among others. For the case study, semi-structured questionnaires were applied in different business establishments in Campina Grande, with questions related to socioeconomic aspects and photovoltaic technology. Data were analyzed using descriptive statistics criteria and using an Excel spreadsheet. The main results indicated that the Brazilian energy matrix is predominantly from renewable sources. The Northeast is the second region with the highest production of photovoltaic solar energy and the State of Paraiba occupies its fourth position in the generation of this type of energy. The option of photovoltaic technology is a promising alternative, especially for rural areas, where there is not always a conventional electricity grid. The high availability of solar energy in northeastern Brazil, in almost all months of the year, especially in the state of Paraiba, demonstrates the existence of a high potential to generate electricity from photovoltaic systems. This technology contributes to local sustainable development, as it is an activity that generates employment and income, without degrading the environment.

Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

For heating systems based on electricity storage coupled with solar energy and an air source heat pump(ECSA),choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency.In this paper,four cities in three climatic regions in China were selected,namely Nanjing in the hot summer and cold winter region,Tianjin in the cold region,Shenyang and Harbin in the severe cold winter region.The levelized cost of heat(LCOH)was used as the economic evaluation index,and the energy consumption and emissions of different pollutants were analyzed.TRNSYS software was used to simulate and analyze the system performance.The Hooke-Jeeves optimization algorithm and GenOpt software were used to optimize the system parameters.The results showed that ECSA systemhad an excellent operation effect in cold region and hot summer and cold winter region.Compared with ECS system,the systemenergy consumption,and the emission of different pollutants of ECSA system can be reduced by a maximum of 1.37 times.In cold region,the initial investment in an air source heat pump is higher due to the lower ambient temperature,resulting in an increase in the LOCH value of ECSA system.After the LOCH value of ECSA system in each region was optimized,the heating cost of the system was reduced,but also resulted in an increase in energy consumption and the emission of different pollutant gases.