Search Results (37)

The deployment of utility-scale hybrid wind–solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from the available data, addressing key challenges such as (1) the spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) the cost dependencies of the different components and system characteristics. When conducting such analyses at the regional or national scale, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, identifying the lack of national-scale product cost models for HPPs as the main gap and establishing a replicable methodology for hybrid plant mapping. A simplified example is shown using this methodology for a country-level analysis. Full article

The earth’s surface has an uneven solar energy density that is sufficient to stimulate solar photovoltaic (PV) production. This causes variations in a solar plant’s output, which are impacted by geometrical elements and atmospheric conditions that prevent it from passing. Motivated by the focus on encouraging increased PV production efficiency, the goal was to use machine learning models (MLM) to map the distribution of solar energy in Mozambique in a regressive literal and geospatial–temporal manner on a short measurement scale. The clear-sky index $\left(K_t^{*}\right)$ theoretical approach was applied in conjunction with MLM that emphasized random forest (RF) and artificial neural networks (ANNs). Solar energy mapping was the result of the methodology, which involved statistically calculating $K_t^{*}$ for the analysis of solar energy in correlational and causal terms of the space-time distribution. Utilizing data from PVGIS, NOAA, NASA, and Meteonorm, a sample of solar energy was gathered at 11 measurement stations in Mozambique over a period of 1 to 10 min between 2012 and 2014 as part of the FUNAE and INAM measurement programs. The statistical findings show a high degree of solar energy incidence, with increments $\left(∆K_t^{*}\right)$ in the average order of −0.05 and $K_t^{*}$ mostly ranging between 0.4 and 0.9. In 2012 and 2014, $K_t^{*}$ was 0.8956 and 0.6986, respectively, because clear days had a higher incident flux and intermediate days have a higher frequency of ${∆K}_t^{*}$ on clear days and a higher occurrence density. There are more cloudy days now 0.5214 as opposed to 0.3569. Clear days are found to be influenced by atmospheric transmittance because of their high incident flux, whereas intermediate days exhibit significant variations in the region’s solar energy. Full article

Recurrent catastrophic inverter failures significantly undermine the reliability and economic viability of utility-scale photovoltaic (PV) power plants. This paper presents a comprehensive investigation of severe inverter destruction incidents at the Kopli Solar Power Plant, Estonia, by integrating controlled laboratory simulations with extensive field monitoring. Initially, detailed laboratory experiments were conducted to replicate critical DC-side short-circuit scenarios, particularly focusing on negative DC input terminal faults. The results consistently showed these faults rapidly escalating into multi-phase short-circuits and sustained ground-fault arcs due to inadequate internal protection mechanisms, semiconductor breakdown, and delayed relay response. Subsequently, extensive field-based waveform analyses of multiple inverter failure events captured identical fault signatures, thereby conclusively validating laboratory-identified failure mechanisms. Critical vulnerabilities were explicitly identified, including insufficient isolation relay responsiveness, inadequate semiconductor transient ratings, and ineffective internal insulation leading to prolonged arc conditions. Based on the validated findings, the paper proposes targeted inverter design enhancements—particularly advanced DC-side protective schemes, rapid fault-isolation mechanisms, and improved internal insulation practices. Additionally, robust operational and monitoring guidelines are recommended for industry-wide adoption to proactively mitigate future inverter failures. The presented integrated methodological framework and actionable recommendations significantly contribute toward enhancing inverter reliability standards and operational stability within grid-connected photovoltaic installations. Full article

The development of the building sector to the use of renewable energy, more so in photovoltaic (PV) systems, is a great step toward enhanced environmental sustainability and improved energy efficiency. This study seeks to determine the economic, environmental, and operational effects of integrating a PV system into a Polish production plant for buildings. Case study methodology was followed with the help of actual operating histories and simulation modeling to present the estimates of carbon emission savings, cost savings, and power efficiency. Key findings illustrate that 31.8% of the business’s full-year supply of electricity is through the utilization of solar energy and that it saves as much as 10,366 kg CO₂ of emissions every year. The economic rationale of the system is provided in the form of a 3.6-year payback period against long-term savings of over EUR 128,000 in 26 years. This work also addresses the broader implications of energy storage and management systems on the basis of scalability and reproducibility of intervention at the building construction scale. This study provides evidence towards the requirement of informing decision-making by business managers and policy decisionmakers as a step towards the solution of issues of interest to the utilization of renewable energy at industrial levels towards world agenda harmonization for sustainability and business practice. Full article

Maintenance and monitoring of solar photovoltaic (PV) systems are essential for enhancing reliability, extending lifespan, and maintaining efficiency. Some defects in PV cells cannot be detected through output measurements due to the string configuration of interconnected cells. Inspection methods such as thermal imaging, electroluminescence, and photoluminescence are commonly used for fault detection. Among these, thermal imaging is widely adopted for diagnosing PV modules due to its rapid procedure, affordability, and reliability in identifying defects. Similarly, current–voltage (I-V) curve analysis provides valuable insights into the electrical performance of solar cells, offering critical information on potential defects and operational inconsistencies. Different data types can be effectively managed and analyzed using artificial intelligence (AI) algorithms, enabling accurate predictions and automated processing. This paper presents the development of a machine learning algorithm utilizing transfer learning, with thermal imaging and I-V curves as dual and single inputs, to validate its effectiveness in detecting faults in PV cells at King Saud University, Riyadh. Findings demonstrate that integrating thermal images with I-V curve data significantly enhances defect detection by capturing both surface-level and performance-based information, achieving an accuracy and recall of more than 98% for both dual and single inputs. The approach reduces resource requirements while improving fault detection accuracy. With further development, this hybrid method holds the potential to provide a more comprehensive diagnostic solution, improving system performance assessments and enabling the adoption of proactive maintenance strategies, with promising prospects for large-scale solar plant implementation. Full article

With the surge in energy demand worldwide, renewable energy is becoming increasingly important. Solar power, in particular, is positioning itself as a sustainable and environmentally friendly alternative, and is increasingly playing a role not only in large-scale power plants but also in small-scale home power generation systems. However, small-scale power generation systems face challenges in the development of efficient prediction models because of the lack of data and variability in power generation owing to weather conditions. In this study, we propose a novel forecasting framework that combines transfer learning and dynamic time warping (DTW) to address these issues. We present a transfer learning-based prediction system design that can maintain high prediction performance even in data-poor environments. In the process of developing a prediction model suitable for the target domain by utilizing multi-source data, we propose a data similarity evaluation method using DTW, which demonstrates excellent performance with low error rates in the MSE and MAE metrics compared with conventional long short-term memory (LSTM) and Transformer models. This research not only contributes to maximizing the energy efficiency of small-scale PV power generation systems and improving energy independence but also provides a methodology that can maintain high reliability in data-poor environments. Full article

Harnessing solar energy offers a sustainable alternative for powering electrolysis for green hydrogen production as well as wastewater treatment. The high costs and logistical challenges of electrolysis have resulted in limited widespread investigation and implementation of electrochemical technologies on an industrial scale. To overcome these challenges, this study designs and tests a new approach to chemical experiments and wastewater treatment research using a portable standalone open-source solar photovoltaic (PV)-powered station that can be located onsite at a wastewater treatment plant with unreliable electrical power. The experimental system is equipped with an energy monitoring data acquisition system. In addition, sensors enable real-time monitoring of gases—CO, CO₂, CH₄, H₂, H₂S, and NH₃—along with temperature, humidity, and volatile organic compounds, enhancing safety during electrochemical experiments on wastewater, which may release hazardous gases. SAMA software was used to evaluate energy-sharing scenarios under different grid-connected conditions, and the system can operate off the power grid for 98% of the year in Ontario, Canada. The complete system was tested utilizing a laboratory-scale electrolyzer (electrodes of SS316L, Duplex 2205, titanium grade II and graphite) with electrolyte solutions of potassium hydroxide, sulfuric acid, and secondary wastewater effluent. The electrolytic cell specifically developed for testing electrode materials and wastewater showed a Faraday efficiency up to 95% and an energy efficiency of 55% at STP, demonstrating the potential for use of this technology in future work. Full article

Large-scale renewable energy plants such as solar photovoltaic (PV) farms are vital to the global transition to a green energy economy. They reduce greenhouse gas emissions, mitigate climate change, and promote sustainable and resilient energy. However, large-scale solar PV farms need adequate planning and site selection for optimal performance. This study presents a geographic information system (GIS)-based multi-criteria decision-making (MCDM) framework utilizing the analytic hierarchy process (AHP) to identify optimal sites for utility-scale photovoltaic (PV) farms in Ikorodu, Lagos State, Nigeria. By integrating critical environmental, technical, economic, and social factors, the model evaluates land suitability for solar energy projects across the study area. The finding indicates that 68.77% of the land is unsuitable for development, with only 17.78% classified as highly suitable and 12.67% as moderately suitable. Marginally suitable and most appropriate areas are minimal, at 0.73% and 0.04%, respectively. This study provides a replicable approach for stakeholders and policymakers aiming to implement sustainable energy solutions, aligning with national renewable energy targets. Future research could integrate dynamic factors such as community engagement, land use changes, and evolving environmental policies to enhance decision-making models. This framework offers valuable insights into renewable energy planning and contributes to advancing Nigeria’s transition to sustainable energy systems. Full article

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots. Full article

Concentrated photovoltaic (CPV) technology is based on the principle of concentrating direct sunlight onto small but very efficient photovoltaic (PV) cells. This approach allows the realization of PV modules with conversion efficiencies exceeding 30%, which is significantly higher than that of the flat panels. However, to achieve optimal performance, these modules must always be perpendicular to solar radiation; hence, they are mounted on high-precision solar trackers. This requirement has led to the predominant use of CPV technology in the construction of solar power plants in open and large fields for utility scale applications. In this paper, the authors present a novel approach allowing the use of this technology for residential installations, mounting the system both on flat and sloped roofs. Therefore, the main components of cell and primary lens have been chosen to contain the dimensions and, in particular, the thickness of the module. This paper describes the main design steps: thermal analysis allowed the housing construction material to be defined to contain cell working temperature, while with deep optical studies, experimentally validated main geometrical and functional characteristics of the CPV have been identified. The design of a whole CPV system includes thermal storage for domestic hot water and a 1 kWh electrical battery. The main design results indicate an estimated electrical conversion efficiency of 30%, based on a cell efficiency of approximately 42% under operational conditions and a measured optical efficiency of 74%. The CPV system has a nominal electric output of 550 W_p and can simultaneously generate 630 W of thermal power, resulting in an overall system efficiency of 65.5%. The system also boasts high optical acceptance angles (±0.6°) and broad assembly tolerances (±1 mm). Cost analysis reveals higher unit costs compared to conventional PV and CPV systems, but these become competitive when considering the benefit of excess thermal energy recovery and use by the end user. Full article

The decaying prices and improving efficiency of bifacial solar photovoltaic (PV) technologies make them most promising for harnessing solar radiation. Deserts have a high solar potential, but harsh conditions like high temperatures and dust negatively affect the performance of any proposed solar system. The most attractive aspect of deserts is their long-term sustainability, as they are free from urban and agricultural expansion. In this work, the System Advisor Model (SAM) software version 2023.12.17 was used to model a 100 MW PV plant and evaluate the techno-economic performance of fixed, 1-axis, and 2-axis bifacial systems under the climatic conditions of six deserts from around the world. This study explores technical parameters such as the performance ratio, specific yield, and capacity factor. Additionally, the levelized cost of energy (LCOE) indicator was used to compare the economic performance of the different systems. Results showed high specific yield: the averages for the three systems in six deserts were 2040, 2372, and 2555 kWh/kWp, respectively. Economic analysis found that an LCOE below 4 ¢/kWh is achievable in all deserts, reaching a minimum of 2.45 ¢/kWh under favorable conditions. These results emphasize the high potential of utility-scale PV projects in deserts to advance a green, sustainable energy future. Full article

In this study, a low-cost, scalable and robust process is proposed as an innovative method for coating solar mirrors with a self-cleaning, transparent in the full solar range and versatile material based on auxetic aluminum nitrides, previously obtained at the laboratory scale. This work presents the scaling-up of the fabrication process from the laboratory to prototypal scale and the preliminary results of outdoor self-cleaning solar mirror field tests in the demonstrative concentrating solar power (CSP) plant ENEASHIP located in Casaccia (Rome) ENEA Research Center. Prototypes with a size of 50 × 40 cm have shown stability in external conditions: no coating degradation occurred during the test campaign. Their washing restores the initial reflectance affected by soiling and the self-cleaning performance allows for the utilization of a reduced quantity of water for cleaning operations with respect to the uncoated glass of back surface mirrors. A similar self-cleaning AlN coating could be utilized on other solar components affected by soiling, such as the glass envelopes in heat-collecting elements, PV panels and other parts where a self-cleaning performance combined with an optical one is required. Full article

Among the existing solar technologies, Concentrating Solar Power (CSP) stands out as the most efficient and adaptable option for base load applications, primarily due to its thermal storage capabilities. However, despite its potential, the implementation of this technology still lacks competitiveness compared to Photovoltaic (PV) systems. Therefore, optimizing the plant components and operational factors becomes crucial for its cost-effective utilization, particularly in the desert regions of Morocco. Hence, the objective of this study comprised two main aspects: first, to conduct a parametric analysis aimed at selecting the optimal configuration for a parabolic trough collector (PTC)-based power plant suitable for the Moroccan context. Subsequently, an environmental analysis was performed to assess the impact of soiling on the plant operation. This step aimed to refine the precision of the techno-economic analysis and enhance the project’s bankability. High-quality in situ meteorological data and soiling measurements were utilized for these analyses. Furthermore, to ensure the reliability of the results, the results from the employed simulation tool were validated against real data obtained from an operational power plant. The results indicate that Morocco holds significant potential for the integration of large-scale CSP plants. A capacity of 1 MW utilizing PTC technology could yield an annual electricity production of up to 33 GWhe, with a levelized cost of electricity (LCOE) estimated at 0.1465 EUR/kWh. However, accounting for soiling effects in the yield analysis, which is recommended for precise yield calculations, revealed a decrease in the annual production to 28 GWhe for the same 1 MW capacity. This reduction represented a 20% loss from the nominal conditions, resulting in a corresponding increase in electricity cost by 30.6 €/MWh. Full article

While technical optimization focuses on maximizing the annual energy yield of utility-scale PV parks, the ultimate goal for power plant owners is to maximize investment profit. This paper aims to bridge the gap between technical and economic approaches by using simulation data from a real-case utility-scale PV park. It analyzes how changes in configuration parameters such as the DC–AC ratio and string length and PV technologies like solar tracking systems and bifacial modules impact the economic metrics of the project, i.e., net present value (NPV) and internal rate of return (IRR). PVSyst software was utilized as a simulation tool, while in-house developed software implementing appropriate technical and economic models served as a comparison platform and was used to validate the outputs generated through PVSyst. Results indicate that the commonly used horizontal single-axis tracking configuration may economically underperform compared with fixed-tilt setups. The optimal DC–AC ratio fell within the range of 1.30 to 1.35. Extending the string length from 25 to 28 modules improved economic indexes. Additionally, fixed-tilt bifacial modules can enhance project economics if a 10% cost premium compared with standard monofacial PV modules is considered. Full article

The Kingdom of Saudi Arabia is experiencing a surge in electricity demand, with power generation increasing 4 times in 25 years from 1990 to 2014. Despite the abundant primary renewable energy sources, the country has overlooked them in the past in national energy policies. However, in recent years, renewable energy has become a part of the Kingdom of Saudi Arabia’s energy conservation policy due to climate changes, technological progress, economies of scale, and increased competitiveness in supply chains. The Saudi government has created the King Abdullah City for Atomic and Renewable Energy (KACARE) to develop national strategies for effectively utilizing renewable and nuclear energy. This paper reviews the current state of the art of the renewable energy technologies available on the market and evaluates the installation of renewable energy plants near Saudi Arabia’s East Coast for a new town, focusing on technical rather than economic aspects. The paper provides a wide review of the possible technical solutions to exploit the producibility of different renewable sources, considering the challenging climate conditions typical of desert areas. The analysis of a real case study shows a high availability of wind and solar irradiance that allow a net energy production of 354 and 129 GWh, respectively. In addition, the comparison between a typical ground-mounted photovoltaic (PV) system and an emerging floating PV reveals that for the same installed power, occupied area, and environmental conditions, the latter has a 4% greater performance ratio due to the cooling effect of water. Full article