Search

Energy management plan is utilized as an optimum strategy by using solar and wind energies, as a new preliminary implementation. The aim of the study is to create an optimum strategy through an optimization of an energy management system. The study implemented an onsite model, two numerical approaches, and an optimization analysis on a Mediterranean port. Two approaches have been used: solar energy is applied experimentally and numerically, and then wind energy is simulated. An optimization analysis integrated the two approaches together to control their operation. The results showed the installed solar panels provided sufficient generated power for the buildings. Also, the simulated wind arrays showed good behavior with increased power coefficient for the wind turbines, for future implementation. These results were validated using the DesignBuilder software and showed accurate values regarding the experiment for solar panels and CFD simulation. Eventually, a Pareto optimality analysis is applied between the solar and wind energies to reveal an energy management plan. Renewable energy offered energy to support the consumption of the port's buildings., (© 2023. The Author(s).)

The rapid growth in solar PV construction means a concurrent growth in used solar panels and end of life packaging materials. The current study assesses the risks in an integrated manner, from applying shredded end of life packaging materials (EOLPM) to soil at a utility-scale solar energy (USSE) plant. Its aim and purpose is to determine if the EOLPM would pose a potential risk to human health and/or the environment if placed as a soil amendment incorporated into the surface soil (as a surface-incorporated mulch). An integrated risk assessment, drawing upon existing chemical and phytotoxicity data and introducing carbon emissions impacts (including social cost of carbon emissions) from treatment options, was undertaken confirming effective controls and risk treatments from on-site application as a soil amendment (soil improver). Landfilling (30 t CO 2 e per MW) was estimated to cost $AUD6.8k per MW, compared to the most appropriate and selected on-site option of mulching (0.5 t CO 2 per MW and $UAD7.1k per MW). There is broad application of this approach to other remote USSE construction projects where solar PV construction growth is occurring exponentially globally., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The development and utilization of renewable energy (RE) is crucial for achieving the sustainable development goals (SDGs). The northwest China, endowed with abundant RE sources such as wind and solar power, accounts for over 70% of the country's total resources. The assessment and utilization of RE in this region has become a critical means to achieve the SDGs, particularly SDG7. However, lack of knowledge regarding the RE potential poses a barrier to achieving high-quality energy development. Thus, through a Geographical Information System (GIS) based multi-criteria analysis, we assess the solar and wind energy potential in northwest China, quantitatively examine the energy potential and its contribution towards achieving the SDGs. Our results show that a substantial portion of RE can be harnessed in northwest China, with wind energy generation reaching up to 9.84PWh/km 2 /yr at 110m and 12.43 PWh/km 2 /yr at 140m. Concurrently, solar energy can contribute up to 15.16 PWh/km 2 /yr. Xinjiang province has the highest RE potential for it contains a large share of suitable area with good resource quality. The findings illustrate the contribution of northwest China towards achieving SDGs and facilitate the formulation of more targeted resource policies., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Lijuan Si reports financial support was provided by National Natural Science Foundation of China. Lijuan Si reports a relationship with National Natural Science Foundation of China that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The facilities that energy delivers to social life and economic activities render it indispensable. Hence, it is equally critical that the energy cycle must have a sustainable structure. Therefore, it is an indisputable fact that developing and performing correct and consistent energy policies is vitally necessary. Energy consumption planning includes a continuous process to reassess existing and potential alternative energy approaches and strategies. The public and private decision-makers in charge of planning and managing energy consumption policies must adapt their strategies to novel and superior alternative resources according to sustainability and efficiency criteria. In this paper, the fuzzy EDAS method is used to address the best renewable energy consumption by taking political, economic, social, technological, legal, and environmental (PESTLE) dimensions into account. The analysis of the paper indicates the most efficient renewable energy consumption is sourced by geothermal, solar, wind, hydroelectricity, and biomass, respectively. By further investigation, it is concluded that the most optimum renewable energy consumption alternatives based on PESTLE dimensions are geothermal and solar energies., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An alternative energy source such as solar is one of the most important renewable resources. A reliable solar radiation prediction is essential for various applications in agriculture, industry, transport, and the environment because they reduce greenhouse gases and are environmentally friendly. Solar radiation data series have embedded fluctuations and noise signals due to complexity, stochasticity, non-stationarity, and nonlinearity with uncertain and time-varying nature. Aside from being highly nonlinear, solar radiation is highly influenced by the environment and environmental parameters such as air temperature, cloud cover, surface reflectivity, and aerosols. In addition, the spatial measurements of these variables are not readily available. To tackle these challenges, it is necessary to consider data preprocessing techniques and to develop and test precise solar radiation predicting models at different forecast horizons. There is, however, controversy regarding the performance of such models in various studies. Comparisons are not conducted systematically among the different studies. Using a critical literature review, the authors hope to answer these questions and believe that further investigation of solar radiation can benefit researchers and practitioners alike. This study presents a comprehensive evaluation of solar radiation modeling using artificial intelligence in the last 15 years and provides a novel detailed analysis of the available models. The studies conducted in different climates of the world that were published in distinguished journals were considered (i.e., 90 papers in total) for this purpose. Newly discovered procedures for optimizing forecasts, data cleaning, feature selection, classification methods, and stand-alone or hybrid data-driven models for solar radiation prediction and modeling were evaluated. The results strikingly showed that the most used artificial intelligence methods were artificial neural network, adaptive neuro-fuzzy inference system, and decision tree family of models. In addition, the extreme learning machine, support vector machine, and particle swarm optimization were the most used optimization techniques in solar radiation modeling. In terms of forecast horizons, the most common forecast horizon found in papers was on the daily scale (51% of studies), followed by the hourly scale (26%), and the least common was the monthly scale (18%). Based on the regional studies, the highest number of solar radiation papers originated from Asia, with Europe in second place and African countries in third place. An increasing trend in the number of papers from 2011 to 2015 was noted, and the second peak started from 2018 till the present. Under each section, a summary of findings is provided. The paper concludes with future thoughts and directions on solar radiation modeling. [ABSTRACT FROM AUTHOR]

The global interest in environmental issues and sustainable energy has propelled extensive research in photovoltaic (PV) technologies. Brazil has emerged as one of the top ten solar energy producers and flexible PV suppliers in the world. In this context, organic photovoltaic cells (OPVs) have garnered attention due to their flexibility and ability to integrate into various surfaces, albeit facing challenges regarding lifespan and efficiency compared to silicon cells. This review examines the current state of research on OPVs and thin-film solar technologies, employing the systematic literature review methodology Proknow-C. The review includes an analysis of raw materials such as conductive polymers, fabrication processes including film deposition and encapsulation, and technological advancements that enhance the efficiency and stability of OPVs. Utilizing the Proknow-C methodology, a bibliographic portfolio was constructed to identify the current state of research in this field. Of 268 papers from major scientific databases, only 30 were deemed relevant to the theme, highlighting a significant research gap. This paper is a valuable resource for researchers, providing an updated overview and a foundation for future investigations in organic photovoltaics. The results emphasize the importance of materials such as conductive polymers and donor–acceptor molecules and the role of nanotechnology in advancing OPVs. Innovations in manufacturing techniques, such as inkjet and blade coating-based printing techniques, are shown to increase efficiency by providing precise control over film thickness and uniformity and reducing costs through lower material waste. Overall, this review highlights the necessity of further exploration and collaboration within the scientific community to address the challenges and propel advancements in organic photovoltaic cells. These advancements are crucial for transitioning to cleaner energy sources, reinforcing the ongoing significance of thin-film technologies in energy innovation. [ABSTRACT FROM AUTHOR]

Water scarcity poses significant challenges in arid regions like the Middle East and North Africa (MENA) due to constant population growth, considering the effects of climate change and water management aspects. The desalination technologies face problems like high energy consumption, high investment costs, and significant environmental impacts by brine discharge. This paper researches the relationships among water scarcity, energy-intensive desalination, and the development of renewable energy in MENA, with a particular focus on the Gulf Cooperation Council (GCC) countries. It examines innovations in solar-powered desalination, considering both solar photovoltaic (PV) and solar thermal technologies, in combination with traditional thermal desalination methods such as multi-effect distillation (MED) and multi-stage flash (MSF). The environmental impacts associated with desalination by brine discharge are also discussed, analyzing innovative technological solutions and avoidance strategies. Utilizing bibliometrics, this report provides a comprehensive analysis of scientific literature for the assessment of the research landscape in order to recognize trends in desalination technologies in the MENA region, providing valuable insights into emerging technologies and research priorities. Despite challenges such as high initial investment costs, technical complexities, and limited funding for research and development, the convergence of water scarcity and renewable energy presents significant opportunities for integrated desalination systems in GCC countries. Summarizing, this paper emphasizes the importance of interdisciplinary approaches and international collaboration by addressing the complex challenges of water scarcity and energy sustainability in the MENA region. By leveraging renewable energy sources and advancing desalination technologies, the region can achieve water security while mitigating environmental impacts and promoting economic development. [ABSTRACT FROM AUTHOR]

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm.

The debate on the benefits of renewable electrification in late industrializing economies has mostly focused on improved electricity access and climate mitigation arguments. The literature pays less attention to understanding the opportunities for sustainable industrialization. This paper contributes to the latter with insights from a case study of Solinc East Africa, a Kenyan solar module manufacturer that has remained in the market, despite intense competition from imports of low-cost solar panels. Drawing on in-depth fieldwork in the solar energy sector in Kenya, we apply a global value chain framework to explore how and why Solinc has been able to sustain its business. Our findings highlight the challenges of localizing solar manufacturing in Kenya and suggest it may only be possible in rare instances. Specifically, a unique combination of circumstances and factors were revealed that enabled Solinc to gain several advantages including (i) initial access to knowledge and materials from upstream linkages; (ii) downstream integration and partnerships with key distributors and customers; (iii) close proximity to customers; and (iv) provision of complementary and increasingly high value-added services. Our findings present a more positive perspective on the localization of related services, which we argue deserves more attention in the sustainable industrialization debate. [ABSTRACT FROM AUTHOR]

The current non-renewable energy-based transportation system is getting us places while simultaneously killing us. The global oil and gas markets are the most sizeable - 4677.45 billion-dollar industries - in the world in terms of revenue due to human dependency on fast transportation. In pre-industrial revolution times, the average rate of global temperature increase was 0.13-degrees Fahrenheit. The introduction of fossil fuel-based vehicles has more than doubled those numbers, thus resulting in accelerated global warming. Electrical vehicles have a few challenges of their own that we have discussed in this paper, finding solutions to them are based on Yoichi Hori's paper to make our model more energy efficient. Our model has been validated by researches done in the past, this paper combines the findings of formerly published papers to produce a working model of our current design with proof of concept. To reduce the carbon footprint of coal and fossil fuel-based electricity production, solar energy has been considered to charge the vehicle. It is not only an efficient choice, but it is also economical. It saves the consumer around $1778 annually in fuel cost and 38.5g/km in carbon emissions. Using a wireless charging station solves a major UX design problem, making charging an effortless experience. This method is 88.05% efficient at 31.5A. Supercapacitors have high power densities, therefore, being a good power source for the EV in theory, however, research shows that current supercapacitor technology is unable to perform as a standalone power source. Hence our model combines supercapacitors with conventional batteries to power the EV, additionally a break recovery system stores energy in the supercapacitor. This powers the car's acceleration. Our model priorities the use of materials that can be repurposed hence carbon-based electrodes are used - Graphene is a potential choice as it consists of high energy cycles. [ABSTRACT FROM AUTHOR]

This paper considers two pertinent research inquiries: 'Can an AI-based predictive framework be utilised for the optimisation of solar energy management?' and 'What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?' The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and $ R^2 $ R 2 . A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency. [ABSTRACT FROM AUTHOR]

In the contemporary world, energy stands as an essential driving force behind socio-economic development. However, with the alarming levels of environmental pollution, there has been a growing focus on renewable energy sources. These sustainable alternatives are gaining increasing attention, particularly as the depletion of fossil fuels becomes more ap-parent. Natural components such as sunlight, wind, and rainfall are used to generate renewable energy. Among these non-traditional renewable sources, solar energy emerges as a prominent contender for power generation. Solar power generation has gained recognition as a promising and environmentally sustain-able renewable energy source to meet growing global energy demands while minimizing cli-mate change consequences. This paper extensively examines solar power generation techniques, encompassing Photovoltaic (PV) Systems and Solar Thermal Technologies. The paper explores the present state of solar power generation technology, outlines its advantages, and researches the various challenges obstructing its widespread adoption. Furthermore, the study offers insights into potential solutions and outlines future directions for advancing solar power generation techniques. [ABSTRACT FROM AUTHOR]

Copyright of OKARA: Geografia em Debate is the property of OKARA Geografia em Debate and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The power area has been dealing with the issues of the nonstop expansion sought after an absence of adequate age of electric energy utilizing petroleum derivatives. The ecological issues like contamination and a worldwide temperature alteration, the ceaselessly inflating cost of petroleum derivatives and the lack of petroleum products are mostly worried to collect energy from reasonable and clean sources. One of the abundant sources of energy is the solar energy. Using PV technologies, electrical energy can be produced. Aside from low productivity, different elements, particularly ecological factors, for example, dust, hail, stickiness and temperature and establishment components, for example, slant point, establishment site, and height impact the exhibition of the PV module. In this review paper, the innovations in the photo-voltaic technologies, the power producing capacity, various radiation absorbing materials used, along with its application. Various existing models, grid patterns, and its possible future explorable areas are additionally studied. [ABSTRACT FROM AUTHOR]

Federal and state governments have recently expanded their regulatory mandates and incentives to promote renewable energy (RE) use in their planned new buildings. This requires planners to analyze and optimize their preliminary design decisions, such as building dimensions, orientation, location, and window-to-wall ratio, to maximize the use of RE in their buildings. To support designers in this critical task, this paper presents a novel model for optimizing preliminary building designs. The two optimization objectives of the optimization model focus on maximizing harvested RE and minimizing construction cost by identifying optimal building dimensions, orientation, window-to-wall ratio, and site layout. The optimization model complies with all design requirements, such as building dimensions, natural lighting, and cost-effectiveness constraints. The model performance is analyzed using two application examples to illustrate its capabilities in considering the impact of the surrounding environment on design decisions. The results of this analysis confirm the model contributions in identifying a set of nondominated optimal solutions that provide tradeoffs among the two optimization objectives of the developed model. These capabilities are expected to support building planners in identifying an optimal preliminary building design that maximizes the use of RE while minimizing all related construction costs. [ABSTRACT FROM AUTHOR]

The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems. Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios. This paper explores how the battery energy storage capacity requirement for compressed-air energy storage (CAES) will grow as the load demand increases. Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization. Results show that providing bulk CAES to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. The capacity requirement of CAES increases by ≤33.3% with a 1.5 times increase in the load demand and by ≤50% with a two-times increase in the load demand. In this analysis, a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation. The least competitive nuclear option plays this role and reduces system costs by 16.4%, curtails the annual main node by 36.8%, and decreases the CAES capacity requirements by ≤80.7% in the case of a double-load demand. While CAES has potential in addressing renewable variability, its widespread deployment is constrained by geographical, societal, and economic factors. Therefore, if California is aiming for an energy system that is reliant on wind and solar power, then an additional dispatchable power source other than CAES or similar load flexibility is necessary. To fully harness the benefits of bulk CAES, the development and implementation of cost-effective approaches are crucial in significantly reducing system costs. [ABSTRACT FROM AUTHOR]

This paper presents an extensive analysis of the UK's largest bifacial photovoltaic (PV) power plant, located in North Yorkshire. Commissioned in January 2020, this trailblazing facility, with a total installed capacity of 34.7 MW, is a benchmark for the evaluation of bifacial solar technology within the region. This pioneering study provides a thorough comparative assessment of bifacial and monofacial PV systems through a methodical investigation of their energy production, degradation rates, and spectral responses over a 4-year operational period. Our findings reveal that bifacial PV modules, distributed across four segments of the power plant, demonstrate a remarkable average power gain ranging between 15.12% and 17.31% compared with monofacial modules. Despite experiencing marginally higher annual degradation rates—1.17% for bifacial compared with 0.91% for monofacial systems—bifacial modules show superior resilience and energy yield, particularly during winter months when albedo effects are pronounced due to snow coverage. The study also highlights the strategic importance of spectral response analysis in optimizing PV performance. Bifacial modules have shown greater efficiency in capturing infrared radiation—a property that could be exploited to enhance overall energy yield under specific environmental conditions. The empirical data indicate a consistent performance of bifacial modules with an average normalized energy output clustering around the expected efficiency level. Therefore, the results of this study are pivotal for understanding the practical implications of deploying bifacial PV technology on a large scale. They provide valuable data for stakeholders in the solar energy sector, guiding future installations and innovations in solar panel technology. [ABSTRACT FROM AUTHOR]

One of the main issues in power systems relates to scheduling of energy resources. With the ever-increasing penetration of renewable energies with intermittent power output, this issue has turned into an even more significant problem. Renewable energy sources (RESs) have captured attention due to their low environmental emission and also low running cost. One drawback that may be brought into power systems is the surplus power generation by such generation technologies that should be carefully addressed in power system-related problems. This paper proposes the unscented transform modeling to consider the stochastic behavior of charge and discharge of EVs, random performance of photovoltaic, load demand and wind turbine systems. Due to the unpredictable nature of solar and wind power outputs, as well as plug-in electric vehicle owners' behavior when supplying or receiving power from the grid, a stochastic programming-based approach is proposed to operate microgrids in grid-connected configuration mode. The integration of vehicle to grid (V2G) has a good ability to minimize the operating cost of the MG. An integrated optimization model is presented in this study for optimal operation of the MG with high penetration of PEVs and RESs. Modified sunflower optimization algorithm (MSFO) algorithm is applied in this paper to address the optimization problem. The single-objective stochastic optimization is used for minimizing the total operating cost over the day taking into consideration the uncertainties due to the RESs' power output intermittency, including wind speed and solar irradiance and load demand forecast error. Several case studies are taken into account to show the efficiency of the optimal operation with PEVs. [ABSTRACT FROM AUTHOR]

Climate change is a global issue that requires collective action to address. One of the most pressing concerns is reducing emissions resulting from combustion processes. The use of renewable energy sources and green energy has become a trend worldwide. Solar energy is one of the most promising sources due to its abundance and simplicity of implementation. The city of Aswan, located in the South of Egypt, has a high solar radiation that makes it ideal for utilizing solar power. The current study investigates the optimal design for a sustainable building electricity system at the Aswan Campus of the Arab Academy for Science, Technology & Maritime Transport (AASTMT) in Egypt. The campus has four electricity sources: a utility grid, PV panels, batteries, and a diesel generator, along with a weather station. Experimental investigations have been carried out in this research paper to study the performance characteristics of solar power. Moreover, HOMER pro software is used to model various configurations of the campus grid, including different photovoltaic (PV) panel types and tracking systems. The simulations are compared with real-world data collected from a weather station on campus. Additionally, CO2 and NO2 emissions are measured to assess the environmental impact of different scenarios. The total net cost over the life cycle is also calculated for different cases. The results demonstrate that the addition of a PV renewable system can reduce traditional grid usage by 38% and emissions by 50%. A decrease in the Levelized Cost of Energy (LOCE) from USD 0.0647 to USD 0.0535 is reported. Moreover, the difference in NCP cost between dual-axis tracking and fixed zero angle is USD 143,488. The dual degree tracker for PV panels can further enhance energy production by 30% more, compared with fixed panels, while reducing carbon dioxide emissions by more than 20%. The simulation results reveal that tracking systems provide greater energy generation, and that a cost–benefit analysis may prioritize fixed panels in some cases. The results from the HOMER software simulations closely match those of the experimental data, which is that the total presentation error does not exceed 8%, demonstrating the software's effectiveness for optimizing renewable energy systems. This study demonstrates that a comprehensive analysis and optimization of a building's energy sources can significantly reduce costs, lower emissions, and promote the use of renewable energy, particularly solar power. [ABSTRACT FROM AUTHOR]

The dual threats of energy depletion and global warming place the development of methods for harnessing renewable energy resources at the center of public interest. Solar energy is one of the most promising renewable energy resources. Sun trackers can substantially improve the electricity production of a photovoltaic (PV) system. This paper proposes a novel design of a dual-axis solar tracking PV system which utilizes the feedback control theory along with a four-quadrant light dependent resistor (LDR) sensor and simple electronic circuits to provide robust system performance. The proposed system uses a unique dual-axis AC motor and a stand-alone PV inverter to accomplish solar tracking. The control implementation is a technical innovation that is a simple and effective design. In addition, a scaled-down laboratory prototype is constructed to verify the feasibility of the scheme. The effectiveness of the Sun tracker is confirmed experimentally. To conclude, the results of this study may serve as valuable references for future solar energy applications.

The use of solar energy is now a common and modern alternative that many countries throughout the world have adopted. Different studies on PV systems have been documented in the literature; however, several reviews focus excessively on particular facets of solar modules. In this paper, the literature on PV systems published between 2000 and 2023 was reviewed thoroughly. This review is structured in three main parts. Primarily, the main factors impacting dust deposition on solar modules are discussed. These include temperature, wind speed, inclination angle, location, climatic conditions, photovoltaic module surface characteristics, and dust characteristics. Many methods for mitigating and reducing dust as well as approaches to cleaning PV modules are also reviewed in this study. The many types of solar modules, together with their most important characteristics and operational effectiveness, are presented. As more solar photovoltaic panels expand their end of life (EOL), solutions are required to recycle and dispose of solar photovoltaic panels at the lowest economic cost and with the least environmental damage through reduced carbon emissions and greenhouse gases. Subsequently, this paper further reviews solar PV energy for a green environment and PV waste recycling and its costs. Moreover, integrating solar energy with other clean energy constituting an energy source for hard-to-reach areas and an alternative to fuel are discussed. Therefore, this comprehensive review of the use of photovoltaic systems for green energy production is helpful in an increased share of clean energy for various energy sectors in the future. [ABSTRACT FROM AUTHOR]

The ageing of coal-fired power stations in South Africa has led to regular power outages. Therefore, the country will need to urgently increase its electricity capacity to meet further energy demand from growing urbanization and population growth. This challenge has increased interest in alternative energy sources, such as renewable energy (RE). South Africa is gradually implementing appropriate renewable energy practices, reducing carbon emissions, cleansing the air, and assuring a more sustainable future. This paper summarizes the availability, current state, and future potential of renewable energy choices in South Africa. This paper also evaluates specific policy measures and government actions aimed at eliminating barriers and increasing renewable energy deployment in the future. It also considers the South African network's specialized techno-economic analysis. The findings show that South Africa is still heavily reliant on coal, with 78% of the current installed capacity coming from coal power in 2022, compared to 9.3% for solar and wind energy. [ABSTRACT FROM AUTHOR]

This paper takes a cursory look at the problem of inadequate power supply in the rural healthcare centres of a developing country, specifically Nigeria, and proffers strategies to address this issue through the design of hybrid renewable energy systems combined with the existing unreliable grid in order to meet the healthcare load demand, thus ensuring higher reliability of available energy sources. The simulations, analysis and results presented in this paper are based on meteorological data and the load profiles of six selected locations in Nigeria, using which hybrid grid-connected systems integrating diesel, solar and wind energy sources are designed with configurations to give optimum output. The optimised design configurations in the considered case study, Ejioku, Okuru-Ama, Damare-Polo, Agbalaenyi, Kadassaka and Doso, produce very low energy costs of of 0.0791 $/kWh, 0.115 $/kWh, 0.0874 $/kWh, 0.0754 $/kWh, 0.0667 $/kWh and 0.0588 $/kWh, respectively, leveraging solar and wind energy sources which make higher percentage contributions at all sites. The load-following-dispatch strategy is adopted at all sites, ensuring that at every point in time, there is sufficient power to meet the needs of the healthcare centres. Further works on this topic could consider other strategies to optimise general energy usage on the demand side. [ABSTRACT FROM AUTHOR]

The environment changes rapidly due to climate change. This situation has a great influence on the agricultural sector. In particular, prices of agricultural products rise as space for producing agricultural products around the world decreases. To solve this problem, smart farm is recognized as major technologies in the future society. In particular, in the era of the 4th Industrial Revolution, new technologies such as the Internet of Things (IOT) and artificial intelligence will be installed in smart farm. In addition, with the development of the Internet of Things (IOT) technology, temperature, humidity, and concentration of fluids in plant factories can be monitored and controlled with smart-phone through various sensors. This IoT technology is recognized as a very important technology for smart farm. In addition, a sustainable smart farm model by converging with eco-friendly IT technology is also important. Therefore, in this paper, a study was conducted on the system design of eco-friendly smart farm using the Internet of Thing. Such smart farm technology can remotely manage plants through the Internet of Things. The research methods of this study are as follows. First, a literature study was conducted in this paper. Based on this literature study, the researcher conducted a field survey on smart farm. The researcher visited this space in person and conducted in-depth interviews with operators and researchers. The researcher analyzed the results derived through this survey and suggested the design principle for the eco-friendly smart farm system. In addition, this study proposes a method for using the Internet of Things and renewable energy for smart farms in preparation for the era of climate change. The results of this study can be theoretical basic data for changes in the agricultural field in the era of the Fourth Industrial Revolution. [ABSTRACT FROM AUTHOR]

This paper proposes a model of variable rate automatic microcontroller-based irrigation system. Solar power is used as only the source of power to control the overall system. The sensor is placed on paddy field and these sensors continuously sense the water level and give the message to the farmer informing the water level without visiting the paddy fields. Thus, low-cost solar power can be the answer for all our energy needs. Solar powered smart irrigation systems are the answer to the Indian farmer. This system consists of solar powered water pump along with an automatic water flow control using a moisture sensor. It is the convenient solution for the present energy crisis for the farmers. This paper reveals a model of automatic micro controller-based irrigation system. This paper proposes a model of automatic microcontrollerbased irrigation system. Here solar energy act as the sole supplier of energy throughout. Sensors are placed on the field and these sensors continuously sense the water level and give the message to the farmer informing the water level without visiting the paddy fields. This paper proposes a model of automatic microcontroller-based irrigation system. This system reduces the cost and increases productivity. This system conserves electricity by reducing the usage of grid power and conserves water by reducing water losses. [ABSTRACT FROM AUTHOR]

A new methodology is presented in this paper to encourage the growth of renewable energy technologies in hot and arid countries. PV solar panels are characterized by a decrease in efficiency with the increase in temperatures. This means in hot sunny countries, the actual output will decrease, affecting the power output despite the high availability of sun irradiation. In order to address this issue, a new methodology has been developed and presented in this paper to support system's designers and manufacturers; which allows rapid testing and assessment of the design in consistent way within a short period of time. The approach, named Rapid Evaluation of Solar panels Cooling (RESC), is novel as it combines rapid laboratory testing, with in-situ experimental data to evaluate the cooling technologies that are integrated into solar panels. Modular and scalable designs of passive (chimney effect) and active (fan) cooling methods were tested. The results show that the suggested approach is successful in comparing between the cooling technologies to assess their performance and the payback period within a short period of time. Carbon savings are also calculated for the suggested cooling technologies. The results show that the best energy performance was found to be for the fan-cooled system with overall 12.3% improvement in annual energy output. However, when compared to the payback period on financial investment, the passive cooling is found to more appealing. The key advantage of cooling technologies is found to be in producing an additional significant level of power during summer days when the surface temperature of the panel is at 70 °C or above. Hence, in such conditions, the cooling process could result in an increase in power output of about 53.15% relative to the uncooled standard panels. [ABSTRACT FROM AUTHOR]

Introduction: High Alpine regions show a great potential for solar photovoltaic electricity production in winter due to the reflective properties of snow and the larger number of sun hours compared to lower urban and peri-urban regions. In countries with a pronounced deficit in domestic electricity production such as Switzerland, utility-scale projects of Alpine-PV power plants are now flourishing. The harsh environmental conditions and the complexity of the high-Alpine topography however challenge the conventional design practices. In this context, the main question the article addresses is the representation of test-scale measurement in the performances of Alpine-PV, utility-scale power plants. Methods: This article focuses on the comparison between long term tests and the first Alpine-PV utility-scale power plant in Switzerland. Results: The results show that the main performance indicators such as the Alpine-to-Midlands final yield ratio or the winter fraction, do scale. However, absolute final yield values are consistently lower at the utility scale. Discussion: The paper discusses the possible impacts of the complex Alpine topography as constraint for the design of the larger extent of utility-scale power plants as well as the influence of scale on the snow induced losses. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement. [ABSTRACT FROM AUTHOR]

Among renewable resources, solar energy is abundant and cost effective. However, the efficiency and performance of photovoltaic panels (PVs) are adversely affected by the rise in the surface temperature of solar cells. This paper analyzes the idea of utilizing thermoelectric modules (TEMs) to enhance the efficiency and performance of PV panels. The proposed hybrid solar thermoelectric generation (HSTEG) system employs TEMs as thermoelectric coolers (TECs) to enhance panel efficiency and as thermoelectric generators (TEGs) to convert excess heat into additional electricity. This study includes an extensive evaluation of the proposed idea using MATLAB Simulink and experimental validation in indoor as well as outdoor environments. The use of TECs for the active cooling of the PV system leads to an increase in its efficiency by 9.54%. Similarly, the passive cooling by TECs along with the additional power generated by the TEGs from the excessive heat led to an increase in the efficiency of the PV system of 15.50%. The results demonstrate the HSTEG system's potential to significantly improve PV panel efficiency and energy generation, offering a promising avenue for advancing solar energy technology. [ABSTRACT FROM AUTHOR]

Due to environmental concerns about the use of fossil fuels, renewable energy, especially solar energy, is increasingly sought after for its ease of installation, cost-effectiveness, and versatile capacity. However, the variability in environmental factors poses a significant challenge to photovoltaic (PV) power generation forecasting, which is crucial for maintaining power system stability and economic efficiency. In this paper, a novel muti-step-ahead PV power generation forecasting model by integrating single-step and multi-step forecasts from various time resolutions was developed. One-dimensional convolutional neural network (CNN) layers were used for single-step forecasting to capture specific temporal patterns, with the transformer model improving multi-step forecasting by leveraging the combined outputs of the CNN. This combination can provide accurate and immediate forecasts as well as the ability to identify longer-term generation trends. Using the DKASC-ASA-1A and 1B datasets for empirical validation, several preprocessing methods were applied and a series of experiments were conducted to compare the performance of the model with other widely used deep learning models. The framework proved to be capable of accurately predicting multi-step-ahead PV power generation at multiple time resolutions. [ABSTRACT FROM AUTHOR]