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Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the 'GaAs' layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of 1 × 10 20 (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 (mA / m 2) , respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells. [ABSTRACT FROM AUTHOR]

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions. [ABSTRACT FROM AUTHOR]

This research paper examines how multi-angled façade systems improve and optimise energy performance compared to a flat façade and meet sustainability targets for lower energy use to align with UN SDGs 3, 11, 12, and 13. The multi-angled façade system does not tilt up and down. Instead, it employs two different window orientations on a vertical axis (left and right). The large portion orients more to the north to allow more daylight to penetrate inside the room, and the small part is oriented more to the south to provide passive solar heating. The investigations in this research paper were carried out using version 4.8 of the IDA ICE software, and the researchers evaluated the energy consumption, the energy action through the façade, and the building's inside operative temperature. The results of this paper present the simulation findings for primary energy consumption in different scenarios. For example, the researchers explain that one can save 6.3 kWh/(m2·year) when using a multi-angled façade system compared to a flat façade. This is in addition to improving the thermal indoor climate that results from using the façades. The conclusions of the research show that the façade with multiple angles maximises using daylight and optimises solar power, thus avoiding overheating issues. [ABSTRACT FROM AUTHOR]

Large-scale optimization (LSO) problems among photovoltaic (PV) and concentrated solar power (CSP) systems are attracting increasing attention as they help improve the energy dispatch efficiency of PV and CSP systems to minimize power costs. Therefore, it is necessary and urgent to systematically analyze and summarize various LSO methods to showcase their advantages and disadvantages, ensuring the efficient operation of hybrid energy systems comprising different PV and CSP systems. This paper compares and analyzes the latest LSO methods for PV and CSP systems based on meta-heuristic algorithms (i.e., Particle Swarm Optimization, Genetic Algorithm, Enhanced Gravitational Search Algorithm, and Grey Wolf Optimization), numerical simulation and stochastic optimization methods (i.e., Constraint Programming, Linear Programming, Dynamic Programming Optimization Algorithm, and Derivative-Free Optimization), and machine learning-based AI methods (Double Grid Search Support Vector Machine, Long Short-Term Memory, Kalman Filter, and Random Forest). An in-depth analysis and A comparison of the essence and applications of these algorithms are conducted to explore their characteristics and suitability for PV and CSP or hybrid systems. The research results demonstrate the specificities of different LSO algorithms, providing valuable insights for researchers with diverse interests and guiding the selection of the most appropriate method as the solution algorithm for LSO problems in various PV and CSP systems. This also offers useful references and suggestions for extracting research challenges in LSO problems of PV and CSP systems and proposing corresponding solutions to guide future research development. [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]

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the "hot spot" phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values. [ABSTRACT FROM AUTHOR]

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS. [ABSTRACT FROM AUTHOR]

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system's operating cost, and the introduction of P2G-CCS can effectively help the park's integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system's operating costs and improve the economic benefits of the park. [ABSTRACT FROM AUTHOR]

This paper investigates whether renewable energy influences economic growth and happiness. Using panel data from 25 European Union countries for the period 2012–2022, this study employs a panel model for estimation with fixed and random effects, and robust HAC standard errors. According to the research results, in general, the growing share of renewable energy in the energy mix has a positive impact on economic growth and the happiness of citizens. However, detailed research has shown that this effect depends on the type of energy; a significant positive impact was recorded only in solar share energy, wind share energy and economic growth. However, almost all types of renewable energy were included, i.e., biofuel, hydro, solar and other renewable share energy, and all had a significantly positive impact on the level of happiness. The exception was wind share energy, which showed a significant negative impact. The research findings of this paper provide empirical support for promoting renewable energy, which is positive both for economies and the happiness of citizens. It is one of the main aspects of sustainable economic growth. [ABSTRACT FROM AUTHOR]

In recent decades, the fight against climate change and the commitment to reduce greenhouse gases have shed a light on the production of energy from renewable sources, in particular those derived from solar energy. This has required the involvement of all stakeholders (producers, but also energy operators, authorities, distributors, and final consumers) which has led to the outline of a new scenario characterized by more efficient technologies, dedicated strategies and business models, and the research of alternatives solutions. Within solar technology, great attention has been given in recent years to concentrating solar power (CSP) technologies, both from research studies and technological development sides. This paper provides a theoretical framework based on a CSP literature review to define the state of the art and to identify research gaps and future research steps related to this technology. The work is based on an innovative bibliometric study to explore technical fields related to CSP, providing both a comprehensive framework with reference to the state of the art of the technology investigated, and a detailed analysis on CSP commercial applications, making the review a very useful tool for stakeholders and decision makers The results of the analysis: (1) help to clarify the technological advances of CSP, the strengths and weaknesses of the current technologies used (parabolic and tower systems are the most widespread), and indications of the prospects for dish systems; (2) identify an alternative to the economic problem that represents an obstacle to the diffusion of CSP, for example, by identifying the ability to couple it with thermal storage as a valid method to increase the flexibility of the system and reduce costs; (3) suggested hybrids, both with renewable and non-renewable technologies, identifying strengths and weaknesses for all the proposed proposals; (4) show that it is possible to identify new ongoing research such as that related to hydrogen production. This paper represents the first part of a larger research study developed within the SOLARGRID Project, which promotes and supports the development of innovative solutions for systems and components for CSP and concentrated photovoltaics (CPV) technologies, with the aim to enhance their energetic performances and economic competitiveness in applications for the distributed generation of both electric and thermal energy frameworks. The main findings of our study highlight that, though there is an increasing number of papers on the topic of CSP, several issues remain neglected. [ABSTRACT FROM AUTHOR]

In the current framework of energy transition, renewable energy production has gained a renewed relevance. A set of 75 papers was selected from the existing literature and critically analyzed to understand the main inputs and tools used to calculate solar energy and derive theoretical photovoltaic production based on geographic information systems (GISs). A heterogeneous scenario for solar energy estimation emerged from the analysis, with a prevalence of 2.5D tools—mainly ArcGIS and QGIS—whose calculation is refined chiefly by inputting weather data from databases. On the other hand, despite some minor changes, the formula for calculating the photovoltaic potential is widely acknowledged and includes solar energy, exploitable surface, performance ratio, and panel efficiency. While sectorial studies—targeting a specific component of the calculation—are sound, the comprehensive ones are generally problematic due to excessive simplification of some parts. Moreover, validation is often lacking or, when present, only partial. The research on the topic is in constant evolution, increasingly moving towards purely 3D models and refining the estimation to include the time component—both in terms of life cycle and variations between days and seasons. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon "3060" plan for the future of China. [ABSTRACT FROM AUTHOR]

This paper explores the role of electricity in the transition to renewable energy to mitigate climate change. A systematic literature review with the Scopus database identified 92 papers relevant to the renewable electricity transition. A PESTLE (Political, Economic, Sociological, Technological, Legal, and Environmental) review of the papers provided a multidisciplinary analysis. The Paris Agreement created a global movement for carbon neutrality to address the threats of climate change, calling for a transition to renewable electricity to lead the way and expand into new sectors and regions. Although smaller renewable technologies are ramping up, complexities thwarting the transition include locked-in assets, high upfront costs, variability of solar and wind energy, infrastructure, difficulty in decarbonizing transportation and industry, material resource constraints, and fossil fuel support. This research found that renewables are not replacing fossil fuels to date but adding further energy demands, so that greenhouse gas emissions rose in 2021 despite an increased renewable electricity share. Without a major shift in the trillions of dollars of subsidies and investment away from fossil fuels to renewables, catastrophic climate change is predicted. This paper found that the Paris Agreement's commitment to net-zero carbon and the transition to renewable electricity are undermined by record-high levels of subsidies and financing for fossil fuel industry expansion. Transitioning to a climate-neutral economy requires an investment away from fossil fuels into renewable energy ecosystems. Renewable electricity provides possibilities to realize sustainable development goals, climate stabilization, job creation, a green economy, and energy security with careful planning. [ABSTRACT FROM AUTHOR]

Access to clean energy remains a major issue in developing countries, particularly Sub-Saharan Africa, despite successive policies and the assistance of international institutions or organizations. The United Nations (UN) launched some of the most ambitious initiatives with the Millennium Development Goals and, more recently, the Sustainable Development Goals and Power Africa, a United States (US) government initiative. Sub-Saharan Africa has an important potential in renewable energy for both biogas and solar photovoltaic energy, but they remain underexploited. This paper presents the challenges of access to clean energy in developing countries and the failure of remedial policies mostly based on public–private partnerships (PPPs) in the context of endemic poverty of rural populations. In addition, the development of modern energy technologies remains very limited. Appropriate reforms should be carried out to change the paradigm and allow universal access to clean energy. This paper also addresses the different structural barriers that hinder access to technology in Sub-Saharan Africa and the consequences of access to clean energy in the context of poverty. [ABSTRACT FROM AUTHOR]

The paper offers innovative cooking utensil designs for remote, isolated, and even peri-urban communities at a low price, with high reliability and simple construction. It can alleviate energy poverty and improve food security. This utensil uses only local solar energy directly and allows comfortable indoor cooking. This paper provides the design principles of a solar cooker/frying pan or generic heater, based on a PV panel or a plurality of them, which are directly connected to a plurality of Positive Thermal Coefficient (PTC) resistors to match the power. PTCs are nowadays produced in massive quantities and are widely available at low cost. The proposed device does not require an electronic controller or a battery for its operation. The aim is for family use, although the design can be easily scaled to a larger size or power, maintaining its simplicity. Electric heating inside or attached to the cooking pot, plus the temperature self-limiting effect of PTCs, allows for thermally insulating the cooking pot from its outside using ordinary materials. Insulation enhances energy efficiency during cooking and keeps cooked food warm for a long time. Clean development would receive a significant impulse with its application. A simple mathematical model describes its functioning and states guidelines for adequate design. Its results indicate a successful proof of concept and high efficiency both for water and oil as representatives of cooking. [ABSTRACT FROM AUTHOR]

As penetration of EVs in the transportation sector is increasing, the demand for the mandatory installation of charging infrastructure also is increasing. In addition, renewable energy and energy storage systems (ESSs) are being reviewed for use in electric vehicle charging stations (EVCSs). In this paper, we present an optimal electricity trading volume and an optimal installation capacity of ESSs to maximize the daily profit of the EVCSs equipped with solar power generation when the EVCSs are licensed to sell energy to the power supplier during a specific time period. By formulating and solving the optimization problem of the EVCSs, this paper analyzes validation results for the different useful lives of ESSs, the peak power of a PV generator, and weather conditions at the Yangjae Solar Station and the Suseo Station public parking lot, Seoul, Republic of Korea. Furthermore, this paper validates that the daily expected profit of EVCSs with the proposed method outperforms the profit of conventional EVCSs which do not utilize ESSs. [ABSTRACT FROM AUTHOR]

Reduction or elimination of reliance on traditional fossil fuels and of the emission of greenhouse gases and pollutants into the environment are affecting energy technologies, systems, and applications. In this context, one potential approach to achieving sustainability, decarbonization, and ensuring the energy and economic viability of existing and future energy systems involves adopting one or more renewable sources. The presented paper concentrates on examining the performance of a small-scale hybrid renewable polygeneration system. This system utilizes biogas produced through anaerobic digestion, which is then supplied to an internal combustion engine, along with solar energy converted into electrical energy by photovoltaic modules and wind energy harnessed through a wind turbine. A small-scale user, represented by residential buildings and a zootechnical farm with heating, cooling, and electrical energy demands, serves as the case study. TRNSYS software is employed to design and model the system, considering realistic assumptions about technical aspects and user energy requirements. The investigation involves analyzing the system's operation, considering both energy and economic perspectives. The paper discusses the pros and cons of combining biogas, solar, and wind energy in the proposed hybrid system under the considered case study. Despite non-satisfactory economic profitability without incentives, the proposed system allows one to save significant amounts of primary energy and carbon dioxide equivalent emissions. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system's adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system's adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system's net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels. [ABSTRACT FROM AUTHOR]

With the gradual depletion of fossil energy sources and the diversification of users' energy demand, combined cooling, heating and power (CCHP) microgrids have become a hot technology to improve energy efficiency and promote efficient and synergistic energy operation. However, the uncertainty and correlation of wind power and photovoltaic (PV) outputs have posed a great challenge to the reliability of CCHP system operation, so CCHP systems are often equipped with energy storage devices to improve system flexibility to ensure the reliability of energy supply. However, system-owned reserves still have shortcomings such as high investment O&M costs and large space requirements. As an emerging model, "shared energy storage" can reduce the investment pressure of users and open up new ways for the economic and stable operation of CCHP systems. Therefore, based on the scenario of wind and solar power correlation and considering different types of load flexibility, this paper proposes to construct a shared energy storage station (SESS)-CCHP double-layer synergistic optimal allocation model. The model incorporates the consideration of the actual operation strategy of the CCHP system in the planning stage of energy storage. An example analysis shows that SESS reduces the total operating cost of the CCHP system by 25.96% and improves the new energy consumption rate by 10.46% compared with no energy storage. Compared with the system independently configured with energy storage, the cost saving is 2.14%, thus validating the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In the field of renewable energies, the logistical intricacies of production, as well as the use and storage of photovoltaic energy, have become critical issues. In addition to sheer quantity, the stability of this type of energy is a crucial factor in ensuring the reliability and consistency of power generation. This paper defines Solar Irradiance Stability Factors (SISFs) as indicators complementing electricity production. When planning solar energy production in each geographical area, both the quantity and stability of solar irradiance are necessary for exploitation and determining the quality of solar irradiance. While the average production of solar energy per unit area in each time interval is a widely used parameter in daily practice, the observation of the amplitude of solar irradiance and its influence on energy production in the observed time interval is currently still rare. The SISFs defined in this article are new metrics that mainly depend on the meteorological variability in an area, and the observed time intervals should be in the range of seconds, minutes, or even hours. Larger time intervals are not helpful for the stability of solar irradiance in energy production and logistics from the source to the destination. They provide a complementary and more accurate measure of how suitable a particular geographical area is for producing solar energy. [ABSTRACT FROM AUTHOR]

Concentrating solar power (CSP) technologies have the potential to reduce the carbon emissions in the economy and energy sector. The growing significance of solar energy sources in addressing climate change highlights the necessity for thorough assessments of their environmental impacts. This paper explores two different life-cycle impact assessment methods, ReCiPe and Product Environmental Footprint, using CSP plants with various receiver systems and heat-transfer fluids as a case study. In terms of the overall life-cycle impact, solar towers are shown to have advantages over parabolic troughs. Most of the life-cycle impacts of solar towers are lower than those of parabolic troughs, ranging from 8% to 112%, except for human toxicity and land use impacts. However, there is not much difference between the studied heat-transfer fluids, with the variance of most impacts being less than around 1%. The single-score results indicates that the ReCiPe method assigns significance to human health impacts, while the product environmental footprint method gives equal attention to all aspects. Meanwhile the comparison of components' contributions quantified by the two methods shows the same results for more than half of the impact categories. [ABSTRACT FROM AUTHOR]

Among the various renewable energy-based technologies, photovoltaic panels are characterized by a high rate of development and application worldwide. Many efforts have been made to study innovative materials to improve the performance of photovoltaic cells. However, the most commonly used crystalline panels also have significant potential to enhance their energy yield by providing cooling and cleaning solutions. This paper discusses the possibility of introducing a dedicated direct-water cooling and cleaning system. As assumed, detailed schedules of the operation of the developed direct water cooling and cleaning system should be fitted to actual weather conditions. In this context, different cooling strategies were proposed and tested, including different intervals of opening and closing water flow. All tests were conducted using a dedicated experimental rig. 70 Wp monocrystalline panels were tested under laboratory conditions and 160 Wp polycrystalline panels were tested under real conditions. The results showed that introducing a scenario with a 1-min cooling and a 5-min break allowed for proving the panel's surface temperature lower than 40 °C. In comparison, the temperature of the uncooled panel under the same operating conditions was close to 60 °C. Consequently, an increase in power generation was observed. The maximum power increase was observed in July and amounted to 15.3%. On the other hand, considering selected weeks in May, July, and September, the average increase in power generation was 3.63%, 7.48%, and 2.51%, respectively. It was concluded that the division of photovoltaic installation allows reasonable operating conditions for photovoltaic panels with a lower amount of energy consumed to power water pumps. [ABSTRACT FROM AUTHOR]

Since the advent of smart agriculture, technological advancements in solar energy have significantly improved farming practices, resulting in a substantial revival of different crop yields. However, the smart agriculture industry is currently facing challenges posed by climate change. This involves multi-timeframe forecasts for greenhouse operators covering short-, medium-, and long-term intervals. Solar energy not only reduces our reliance on non-renewable electricity but also plays a pivotal role in addressing climate change by lowering carbon emissions. This study aims to find a method to support consistently optimal solar energy use regardless of changes in greenhouse conditions by predicting solar energy (kWh) usage on various time steps. In this paper, we conducted solar energy usage prediction experiments on time steps using traditional Tensorflow Keras models (TF Keras), including a linear model (LM), Convolutional Neural Network (CNN), stacked—Long Short Term Memory (LSTM), stacked-Gated recurrent unit (GRU), and stacked-Bidirectional—Long Short —Term Memory (Bi-LSTM), as well as Tensor-Flow-based models for solar energy usage data from a smart farm. The stacked-Bi-LSTM outperformed the other DL models with Root Mean Squared Error (RMSE) of 0.0048, a Mean Absolute Error (MAE) of 0.0431, and R-Squared ( R 2) of 0.9243 in short-term prediction (2-h intervals). For mid-term (2-day) and long-term (2-week) forecasting, the stacked Bi-LSTM model also exhibited superior performance compared to other deep learning models, with RMSE values of 0.0257 and 0.0382, MAE values of 0.1103 and 0.1490, and R 2 values of 0.5980 and 0.3974, respectively. The integration of multi-timeframe forecasting is expected to avoid conventional solar energy use forecasting, reduce the complexity of greenhouse energy management, and increase energy use efficiency compared to single-timeframe forecasting models. [ABSTRACT FROM AUTHOR]

Floating photovoltaic (FPV) systems are gaining popularity as a valuable means of harnessing solar energy on unused water surfaces. However, a significant gap persists in our comprehension of their thermal dynamics and the purported cooling benefits they provide. The lack of comprehensive monitoring data across different climatic regions and topographies aggravates this uncertainty. This paper reviews the applicability of established module temperature prediction models, originally developed for land-based PV systems, to FPVs. It then details the refinement of these models using FPV-specific data and their subsequent validation through large-scale, ongoing FPV projects. The result is a significant improvement in the accuracy of temperature predictions, as evidenced by the reduced Mean Absolute Error (MAE) and improved R-squared ( R 2 ) after parameter optimisation. This reduction means that the tailored models better reflect the distinct environmental influences and cooling processes characteristic of FPV systems. The results not only confirm the success of the proposed method in refining the accuracy of current models, but also indicate significant post-tuning changes in the parameters representing wind and convective effects. These adjustments highlight the increased responsiveness of FPVs to convective actions, especially when compared to ground-based systems, possibly due to the evaporative cooling effect of bodies of water. Through this research, we address a critical gap in our understanding of heat transfer in FPV systems and aim to enrich the knowledge surrounding the acknowledged cooling effect of FPVs. [ABSTRACT FROM AUTHOR]

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications. [ABSTRACT FROM AUTHOR]

The efficiency of solar systems, in particular photovoltaic panels, is typically low. Various environmental parameters affect solar panels, including sunlight, the ambient and module surface temperatures, the wind speed, humidity, shading, dust, the installation height, etc. Among others, the key players are indeed solar irradiance and temperature. The higher the temperature is, the higher the short-circuit current is, and the lower the open-circuit voltage is. The negative effect of lowering the open-circuit voltage is dominant, consequently lowering the power of the photovoltaic panels. Passive or active cooling systems can be provided to avoid the negative effect of temperature. This paper presents a prototype of an active cooling system dedicated to photovoltaics. The prototype of such a system was developed at the AGH University of Kraków and tested under laboratory conditions. The proposed system is equipped with air fans mounted on a plate connected to the rear part of a 70 Wp photovoltaic panel. Different configurations of the system were tested, including different numbers of fans and different locations of the fans. The artificial light source generated a irradiation value of 770 W/m2. This value was present for every variant tested in the experiment. As observed, the maximum power generated in the photovoltaic panel under laboratory conditions was approx. 47.31 W. Due to the temperature increase, this power was reduced to 40.09 W (when the temperature of the uncooled panel surface reached 60 °C). On the other hand, the power generated in the photovoltaic panel equipped with the developed cooling system was approx. 44.37 W in the same conditions (i.e., it was higher by 10.7% compared to that of the uncooled one). A mathematical model was developed based on the results obtained, and simulations were carried out using the ANSYS Workbench software. After the validation procedure, several configurations of the air cooling system were developed and analyzed. The most prominent case was chosen for additional parametrical analysis. The optimum fan orientation was recognized: a vertical tilt of 7° and a horizontal tilt of 10°. For the tested module, this modification resulted in a cost-effective system (a net power increase of ~3.1%). [ABSTRACT FROM AUTHOR]

Ecuador, like every country in the world, urgently requires a conversion of transportation to electric power, both for economic and environmental reasons. This paper focuses on the technical and economic feasibility of a solar-powered electric charging station equipped with battery storage in Cuenca, Ecuador. By reviewing current literature, we assess the environmental impact of electric mobility and its potential to reduce fossil fuel dependence and generate energy savings. The analysis encompasses various factors, including EV energy consumption, solar energy system sizing, energy production, and battery storage capacity. Key findings indicate that integrating solar PV systems with EV charging stations efficiently supports a reliable and sustainable energy supply. Simulation results reveal seasonal variations in solar generation, highlighting the importance of proper system sizing to maintain charging supply reliability and manage surplus generation. The economic analysis of three scenarios underscores the financial viability of implementing PV systems without battery storage, yielding a positive Internal Rate of Return (IRR) and Net Present Value (NPV). However, scenarios with battery storage present negative NPV and long investment return periods, impacting economic viability negatively. These insights underscore the need for a balanced design to ensure sustainability and economic feasibility in the transition to electric mobility. [ABSTRACT FROM AUTHOR]

Under the action of waves, a small unmanned surface vehicle (USV) will experience continuous oscillation, significantly impacting its photovoltaic power generation system. This paper proposes a USV photovoltaic power generation simulation model, and the efficiency of photovoltaic MPPT control under wave action is studied. A simulation model for solar irradiance on solar panels of USV under wave action is established based on CFD and solar irradiation models. The dynamic changes in irradiance of USV solar panels under typical wave conditions are analyzed. The MPPT efficiency of USV photovoltaic power generation devices under continuously changing irradiance conditions is studied on this basis. The simulation research results indicate that waves and solar altitude angles significantly impact the instantaneous irradiation energy of USV photovoltaic devices. However, the impact of waves on the average irradiance is relatively tiny. The sustained oscillation of irradiance poses certain requirements for the Maximum Power Point Tracking (MPPT) control frequency of USV photovoltaic systems; a disturbance control frequency of no less than 50 Hz is proposed. [ABSTRACT FROM AUTHOR]

Hydrogen produced from renewable energy sources is a valuable energy carrier for linking growing renewable electricity generation with the hard-to-abate sectors, such as cement, steel, glass, chemical, and ceramics industries. In this context, this paper presents a new model of hydrogen production based on solar photovoltaics and wind energy with application to a real-world ceramics factory. For this task, a novel multipurpose profit-maximizing model is implemented using GAMS. The developed model explores hydrogen production with multiple value streams that enable technical and economical informed decisions under specific scenarios. Our results show that it is profitable to sell the hydrogen produced to the gas grid rather than using it for self-consumption for low-gas-price scenarios. On the other hand, when the price of gas is significantly high, it is more profitable to use as much hydrogen as possible for self-consumption to supply the factory and reduce the internal use of natural gas. The role of electricity self-consumption has proven to be key for the project's profitability as, without this revenue stream, the project would not be profitable in any analysed scenario. [ABSTRACT FROM AUTHOR]

This paper reviews and assesses India's energy policy in the context of its commitment to achieve the target of net-zero carbon emissions by 2070. It emphasizes the central role of green electrification, particularly through the expansion of solar power capacity. It discusses policies outside the energy sector that need to be part of a strategy of achieving this target, including the use of information technology, infrastructure development, and transportation. Furthermore, it examines possible policy options for accelerating the target date to 2050, especially with the use of carbon capture to manage the transition from the current heavy use of coal. The paper also discusses the possible financial and growth implications of various strategy options, arguing that feasible external financing commitments can allow India to achieve net-zero goals without sacrificing economic growth. [ABSTRACT FROM AUTHOR]

The demand for a reliable, cheap and environmentally friendly source of energy makes the integration of renewable energy into power networks a global challenge. Furthermore, reliability, as one of the core elements of efficient and cost-effective energy management options, is still among the dominant factors/techniques that receive more attention for realistic penetrations of renewable energy into the electricity grid. This paper proposes an efficient way of energy management for a grid-connected microgrid. The grid-connected microgrid used in the analysis consists of solar photovoltaic (P.V.) and battery. In this microgrid configuration, oftentimes, the output power might not be equal to the system demand; in this regard, it is expected that the mismatch between these output powers is not zero. However, to reduce the mismatch between demand and supply to be close to zero, this paper proposes strategies of increasing the rated power of solar, battery and grid separately and combining them with a view of finding the cheapest option among these strategies. The results have shown that the cost increment for different options is USD 280.792, 84.48 and 48.204 for storage, P.V. and grid, respectively. These have shown that the storage option is the most expansive option for improving P.V. grid-connected microgrids. This is followed immediately by the P.V. option, which is weather dependent. On the other hand, the grid option is the cheapest option for system reliability improvement. This paper is expected to be useful to both new researchers and experts who are working in energy management with an emphasis on the reliability aspect. [ABSTRACT FROM AUTHOR]

Aiming to mitigate the impact of power fluctuation caused by large-scale renewable energy integration, coupled with a high rate of wind and solar power abandonment, the multi-objective optimal dispatching of a cascade hydro–wind–solar–thermal hybrid generation system with pumped storage hydropower (PSH) is proposed in this paper. Based on the proposed system, the scheduling operation strategy takes into account the complex restrictions of cascade hydropower as well as the flexibility of the PSH. According to various scenarios, the NSGA-II approach is adopted to address the optimization problem, minimizing the system's residual load variation and operation cost. The Pareto solution sets are contrasted and evaluated, applying the TOPSIS with CRITIC weighting. Additionally, the scheduling output of thermal power, cascade hydropower, and PSH is given in terms of different scenarios. The results demonstrate that the allocation of PSH to a hybrid energy system can significantly reduce the operation cost and the fluctuation in the residual load. [ABSTRACT FROM AUTHOR]

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]

To propel us toward a greener and more resilient future, it is imperative that we adopt renewable sources and implement innovative sustainable solutions in response to the escalating energy crisis. Thus, renewable energies have emerged as a viable solution to the global energy crisis, with photovoltaic energy being one of the prominent sources in this regard. This paper represents a significant step in the desired direction by focusing on detailed, comprehensive dynamic modeling and efficient control of photovoltaic (PV) systems as grid-connected energy sources. The ultimate goal is to enhance system reliability and ensure high power quality. The behavior of the suggested photovoltaic system is tested under varying sun radiation conditions. The PV system is complemented by a boost converter and a three-phase pulse width modulation (PWM) inverter, with MATLAB software employed for system investigation. This research paper enhances photovoltaic (PV) system performance through the integration of model-predictive control (MPC) with a high-gain DC–DC converter. It improves maximum power point tracking (MPPT) efficiency in response to the variability of solar energy by combining MPC with the traditional incremental conductance (IN-C) method. Additionally, the system incorporates a DC–AC converter for three-phase pulse width modulation, which is also controlled by predictive control technology supported by Particle Swarm Optimization (PSO) to further enhance performance. PSO was selected due to its capability to optimize complex systems and its proficiency in handling nonlinear functions and multiple variables, making it an ideal choice for improving MPC control performance. The simulation results demonstrate the system's ability to maintain stable energy production despite variations in solar irradiation levels, thus highlighting its effectiveness. [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]

There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production. [ABSTRACT FROM AUTHOR]

A solution to increase the electrical output of the photovoltaic systems relies on solar tracking mechanisms that increase the amount of received solar energy. The experimental results obtained during a monitoring period of one year are comparatively presented in the paper for five types of photovoltaic modules installed on a fixed platform (as reference) and on a dual-axis solar tracking platform in the Renewable Energy Systems and Recycling R&D Centre of the Transilvania University of Brasov, Romania. The influence of the solar-tracking mechanism and the meteorological conditions specific to the four seasons during the monitoring period on the output of the analysed photovoltaic technologies are discussed in the paper. The solar tracking increases by 28% the amount of the yearly received solar energy and by 29.6% the electrical energy output of the entire PV platform. The solar conversion efficiency of the tracked PV platform is slightly increased (14.34%) when compared with the fixed one (14.17%). When assessing the influence of solar tracking on each type of PV, the results show that the CIGS PV module has the highest relative energy gain (34%) followed by CIS (30.8%), m-Si (30.6%), p-Si (27.3%) and CdTe (23.4%) PV modules. [ABSTRACT FROM AUTHOR]

The accurate prediction of global horizontal irradiance (GHI) is crucial for optimizing solar power generation systems, particularly in mountainous areas with complex topography and unique microclimates. These regions face significant challenges due to limited reliable data and the dynamic nature of local weather conditions, which complicate accurate GHI measurement. The scarcity of precise data impedes the development of reliable solar energy prediction models, impacting both economic and environmental outcomes. To address these data scarcity challenges in solar energy prediction, this paper focuses on various locations in Europe and Asia Minor, predominantly in mountainous regions. Advanced machine learning techniques, including random forest (RF) and extreme gradient boosting (XGBoost) regressors, are employed to effectively predict GHI. Additionally, optimizing training data distribution based on cloud opacity values and integrating synthetic data significantly enhance predictive accuracy, with R2 scores ranging from 0.91 to 0.97 across multiple locations. Furthermore, substantial reductions in root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE) underscore the improved reliability of the predictions. Future research should refine synthetic data generation, optimize additional meteorological and environmental parameter integration, extend methodology to new regions, and test for predicting global tilted irradiance (GTI). The studies should expand training data considerations beyond cloud opacity, incorporating sky cover and sunshine duration to enhance prediction accuracy and reliability. [ABSTRACT FROM AUTHOR]

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan. [ABSTRACT FROM AUTHOR]

The reduction of energy demand in buildings is one of the key challenges in contemporary construction. To this end, the application of structural and material partitioning solutions that provide a high level of thermal insulation and the employment of technical installations with high energy performance have become widespread. However, there are a number of other factors that can reduce energy demand. These include the optimal use of heat gains from solar radiation. An aspect that is often discussed in the literature is the overheating of buildings due to excessive heat gains from solar radiation. This article is a case study showing the impact of the orientation of a single-family passive house on its heating energy demand. The building under consideration is located in Central Europe. External climate parameters measured directly at the site during experimental examinations were used for the calculations. This paper adopts six calculation options, considering the different orientations of the glazed façade. As the simulations showed, the effect of solar radiation on the energy demand between two extreme options of glazing orientation, that is south and north-facing orientation, reached 4.7% of the annual energy demand for heating, while for the option corresponding to the actual location of the building and the option involving south-facing windows, the difference was 0.3%, respectively. [ABSTRACT FROM AUTHOR]

The integration of renewable energy is rapidly leading the existing grid systems toward modern hybrid power systems. These hybrid power systems are more complex due to the random and intermittent nature of RE and involve numerous operational challenges. This paper presents the operational model for solar integrated power systems to address the issues of economical operation, reliable solar share, energy deficit in case of contingency events, and the allocation of system spinning reserve. A mixed-integer optimization is formulated to minimize the overall cost of the system operation and to maximize the solar share under robust system spinning reserve limits as well as various other practical constraints. A Pareto-optimal solution for the maximization of the number of solar power plants and minimization of the solar cost is also presented for reliable solar share. Further, a decomposition framework is proposed to split the original problem into two sub-problems. The solution of joint optimization is obtained by exploiting a Lagrange relaxation method, a binary search Lambda iteration method, system spinning reserve analysis, and binary integer programming. The proposed model was implemented on an IEEE-RTS 26 units system and 40 solar plants. [ABSTRACT FROM AUTHOR]

The massive grid integration of renewable energy necessitates frequent and rapid response of hydropower output, which has brought enormous challenges to the hydropower operation and new opportunities for hydropower development. To investigate feasible solutions for complementary systems to cope with the energy transition in the context of the constantly changing role of the hydropower plant and the rapid evolution of wind and solar power, the short-term coordinated scheduling model is developed for the wind–solar–hydro hybrid pumped storage (WSHPS) system with peak shaving operation. The effects of different reservoir inflow conditions, different wind and solar power forecast output, and installed capacity of pumping station on the performance of WSHPS system are analyzed. The results show that compared with the wind–solar–hydro hybrid (WSH) system, the total power generation of the WSHPS system in the dry, normal, and wet year increased by 10.69%, 11.40%, and 11.27% respectively. The solar curtailment decreased by 68.97%, 61.61%, and 48.43%, respectively, and the wind curtailment decreased by 76.14%, 58.48%, and 50.91%, respectively. The high proportion of wind and solar energy connected to the grid in summer leads to large net load fluctuations and serious energy curtailment. The increase in the installed capacity of the pumping station will promote the consumption of wind and solar energy in the WSHPS system. The model proposed in this paper can improve the operational flexibility of hydropower station and promote the consumption of wind and solar energy, which provides a reference for the research of cascade hydropower energy storage system. [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]

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses' microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation. [ABSTRACT FROM AUTHOR]

Egypt has the potential to generate a significant amount of energy from renewable technologies, in particular solar PV, concentrated solar power (CSP), and onshore and offshore wind. The energy sector is reliant on fossil fuels, particularly natural gas, for electricity production and is at risk of locking itself into a high carbon pathway. Globally, reducing greenhouse gas (GHG) emissions associated with national energy sectors is a target outlined in the UN's Paris Agreement. To reduce carbon dioxide (CO2) emissions associated with a higher dependence on fossil fuels, Egypt must consider upscaling renewable energy technologies (RETs) to achieve a clean energy transition (CET). This research modelled six scenarios using clicSAND for OSeMOSYS to identify the technologies and policy target improvements that are needed to upscale RETs within Egypt's energy sector. The results showed that solar PV and onshore wind are key technologies to be upscaled to contribute towards Egypt's CET. The optimal renewable target is the International Renewable Energy Agency's (IRENA) target of 53% of electricity being sourced from RETs by 2030, which will cost USD 16.4 billion more up to 2035 than Egypt's current Integrated Sustainable Energy Strategy (ISES) target of 42% by 2035; it also saves 732.0 MtCO2 over the entire modelling period to 2070. Socio-economic barriers to this transition are considered, such as recent discoveries of natural gas reserves combined with a history of energy insecurity, political instability impacting investor confidence, and a lack of international climate funding. The paper concludes with policy recommendations that would enable Egypt to progress towards achieving a CET. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic analyses, including the payback period and levelized cost of energy (LCOE), alongside an investigation of net metering and net billing scenarios, we delineated a pathway toward achieving net zero billing for the mosque's energy requirements. This study examined two scenarios: Scenario I involved net metering, while Scenario II explored net billing. Our theoretical and simulation results, derived from detailed analyses conducted using PVsyst software, unequivocally demonstrated the superiority of net metering for this specific application. With net metering, the mosque's energy needs can be efficiently met using minimal infrastructure—comprising only 34 photovoltaic modules and a single inverter. In contrast, net billing requires significantly higher resource demands, underscoring the economic and spatial advantages of net metering. Additionally, the payback period for Scenario I is 7.9 years, while for Scenario II, it extends to 87 years. Through rigorous simulations, this study reaffirmed the practicality and feasibility of the net metering approach within the context of Saudi Arabia. Furthermore, our research provides actionable insights for implementing sustainable solutions at specific sites, such as the Al-Abrar Mosque, and contributes to advancing renewable energy knowledge in the region. [ABSTRACT FROM AUTHOR]

As offline control photovoltaic (PV) plants are not equipped with online communication and remote control systems, they cannot adjust their power in real-time. Therefore, in a distribution network saturated with offline control PVs, the distribution system operator (DSO) should schedule the distributed energy resources (DERs) considering the uncertainty of renewable energy to prevent curtailment due to overvoltage. This paper presents a day-ahead network operation strategy using a mobile energy storage system (MESS) and offline control PVs to minimize power curtailment. The MESS model efficiently considers the transportation time and power loss of the MESS, and models various operating modes, such as the charging, discharging, idle, and moving modes. The optimization problem is formulated based on mixed-integer linear programming (MILP) considering the spatial and temporal operation constraints of MESSs and is performed using chanced constrained optimal power flow (CC-OPF). The upper limits for offline control PVs are set based on the probabilistic approach, thus mitigating overvoltage due to forecasting errors. The proposed operation strategy was tested in the IEEE 33-node distribution system coupled with a 15-node transportation system. The test results show the effectiveness of the proposed method for minimizing curtailment in offline control PVs. [ABSTRACT FROM AUTHOR]

The integration of renewable energy systems in buildings leads to a reduction in energy bills for end users and a reduction in the carbon footprint of such buildings, usually referred to as prosumers. In addition, the installation of charging points for the electric vehicles of people working or living in these buildings can further improve the energy efficiency of the whole system if innovative technologies, such as vehicle-to-building (V2B) technologies, are implemented. The aim of this paper is to present an Energy Management System (EMS) based on mathematical programming that has been developed to optimally manage a prosumer building equipped with photovoltaics, a micro wind turbine and several charging points for electric vehicles. Capabilities curves of renewable power plant inverters are modelled within the EMS, as well as the possibility to apply power curtailment and V2B. The use of V2B technology reduces the amount of electricity purchased from the public grid, while the use of smart inverters for the power plants allows zero reactive power to be drawn from the grid. Levelized cost of electricity (LCOE) is used to quantify curtailment costs, while penalties on reactive power absorption from the distribution network are evaluated in accordance with the current regulatory framework. Specifically, the model is applied to a prosumer building owned by the postal service in a large city in Italy. The paper reports the main results of the study and proposes a sensitivity analysis on the number of charging stations and vehicles, as well as on the consideration of different typical days characterized by different load and generation profiles. This paper also investigates how errors in forecasting energy production from renewable sources impact the optimal operation of the whole system. [ABSTRACT FROM AUTHOR]