Search Results (171)

In recent years, solar energy has emerged as one of the most advanced renewable energy sources, with its production capacity steadily growing. To maximize output and efficiency, choosing the right configuration for a specific location for these installations is crucial. This study uniquely integrates detailed multi-variant fixed-tilt PV system simulations with comprehensive economic evaluation under temperate climate conditions, addressing site-specific spatial constraints and grid integration considerations that have rarely been combined in previous works. In this paper, an energy and economic efficiency analysis for a photovoltaic power plant, located in central Poland, designed in eight variants (10°, 15°, 20°, 25°, 30° PV module inclination angle for a south orientation and 10°, 20°, 30° for an east–west orientation) for a limited building area of approximately 300,000 m² was conducted. In PVSyst computer simulations, PVGIS-SARAH2 solar radiation data were used together with the most common data for describing the Polish local solar climate, called Typical Meteorological Year data (TMY). The most energy-efficient variants were found to be 20° S and 30° S, configurations with the highest surface production coefficient (249.49 and 272.68 kWh/m²) and unit production efficiency values (1123 and 1132 kWh/kW, respectively). These findings highlight potential efficiency gains of up to approximately 9% in surface production coefficient and financial returns exceeding 450% ROI, demonstrating significant economic benefits. In economic terms, the 15° S variant achieved the highest values of financial parameters, such as the return on investment (ROI) (453.2%), the value of the average annual share of profits in total revenues (56.93%), the shortest expected payback period (8.7 years), the value of the levelized cost of energy production (LCOE) (0.1 EUR/kWh), and one of the lowest costs of building 1 MWp of a photovoltaic farm (664,272.7 EUR/MWp). Among the tested variants of photovoltaic farms with an east–west geographical orientation, the most advantageous choice is the 10° EW arrangement. The results provide valuable insights for policymakers and investors aiming to optimize photovoltaic deployment in temperate climates, supporting the broader transition to renewable energy and alignment with national energy policy goals. Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

With the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs), the economic optimization dispatch of EV-integrated virtual power plants (VPPs) faces multiple uncertainties and challenges. This paper first proposes an optimized dispatching model for EV clusters to form large-scale coordinated regulation capabilities. Subsequently, considering diversified resources such as energy storage systems and photovoltaic (PV) generation within VPPs, a low-carbon economic optimization dispatching model is established to minimize the total system operation costs and polluted gas emissions. To address the limitations of traditional algorithms in solving high-dimensional, nonlinear dispatching problems, this paper introduces a plant root-inspired growth optimization algorithm. By simulating the nutrient-adaptive uptake mechanism and branching expansion strategy of plant roots, the algorithm achieves a balance between global optimization and local fine-grained search. Compared with the genetic algorithm, particle swarm optimization algorithm and bat algorithm, simulation results demonstrate that the proposed method can effectively enhance the low-carbon operational economy of VPPs with high PV, ESS, and EV penetration. The research findings provide theoretical support and practical references for optimal dispatch of multi-stakeholder VPPs. Full article

With the large-scale integration of photovoltaic power plants—comprising power electronic devices—into power systems, electromagnetic transient simulation has become a key tool for ensuring power system security and stability. The accuracy of photovoltaic unit controller parameters is crucial for the reliability of such simulations. However, as the issue of sub/super-synchronous oscillations becomes increasingly prominent, existing parameter identification methods are primarily based on high/low voltage ride-through characteristics. This limits the applicability of the identification results to specific scenarios and lacks targeted simulation and parameter identification research for sub/super-synchronous oscillations. To address this gap, this study proposes a mathematical model tailored for sub/super-synchronous oscillations and performs sensitivity analysis of converter control parameters to identify dominant parameters across different frequency bands. A frequency-segmented parameter identification method is introduced, capable of fast convergence without relying on a specific optimization algorithm. Finally, the proposed method’s identification results are compared with actual values, voltage ride-through-based identification, particle swarm optimization results, and results under uncertain conditions. It was found that, compared with traditional identification methods, the proposed method reduced the maximum identification error from 7.67% to 4.3% and the identification time from 2 h to 1 h. The maximum identification error of other intelligent algorithms was 5%, with a difference of less than 1% compared to the proposed method. The identified parameters were applied under conditions of strong irradiation (1000 W/m²), weak irradiation (300 W/m²), rapidly varying oscillation frequency, and constant oscillation frequency, and the output characteristics were all close to those of the original parameters. The effectiveness and superiority of the proposed method have been validated, along with its broad applicability to different intelligent algorithms and its robustness under uncertain conditions such as environmental variations and grid frequency fluctuations. Full article

This article is devoted to the study of a 20 MW large-scale photovoltaic power plant (LS-PVPP), connected to the grid and located in Naàma, Algeria. The power plant is included in the National Program for the Development of Renewable Energies 2015–2030. Among the parameters analyzed in detail in this work, the performance ratio recorded an average value of 67.55%, the capacity factor had an average of 17.10%, the total losses had an average of 2.10 kWh/kWp/day, the system efficiency had an average of 4.10 kWh/kWp/day and an annual average of 9.84% of the efficiency. A linear regression equation with a coefficient of determination R² of 0.91 confirms the importance of irradiation impact in the region; less significant linearity for the effect of temperature with a coefficient of determination R² = 0.28 is recorded for production. A comparative study conducted with the Adrar plant (Algeria) with an extremely hot desert climate and the Saida plant (Algeria) with a semi-arid climate demonstrated that the efficiency of the Naàma station is equal to 91.22% of the efficiency of Adrar and 73.47% of the efficiency of Saida. Naàma is known for its semi-arid climate; it is very cold in winter and hot in summer, with sandstorms becoming more frequent due to climate change. PVsyst software (Version 7.4.8) is used to validate the results. Full article

Around the world, large-scale bifacial photovoltaics (BPV) modules are increasingly being used to generate clean electricity, given the cost of manufacturing is becoming comparable to conventional monofacial PV modules. BPV, when installed vertically, can still produce high levels of electricity by collecting radiation on the front as well as on the rear side. This paper assessed the renewable energy generation potential of vertical BPV plants along the central reservation of UK motorways. These installations maximize the utility of road space while minimizing land consumption. The feasibility of BPV systems for different segments of a motorway case study in the UK were modelled to calculate energy yield, the levelized cost of electricity (LCOE), payback period, and net present value. The LCOE of a medium to large-scale system was 10–11 p/kWh, 60% less than that of a small-scale system. The payback period for medium to large-scale systems was found to be 6 years, whereas for small systems, it was 10 years. The paper further discussed the challenges and opportunities associated with installing BPV panels on motorways with guidance on the types of locations which are likely to be most successful for future full-scale installations. Full article

Hybrid pumped hydro storage plants, by integrating pump stations between cascade hydropower stations, have overcome the challenges associated with site selection and construction of pure pumped hydro storage systems, thereby becoming the optimal large-scale energy storage solution for enhancing the absorption of renewable energy. However, the multi-energy conversion between pump stations, hydropower, wind power, and photovoltaic plants poses challenges to both their planning schemes and operational performance. This study proposes a multistage stochastic coordinated planning model for cascade hydropower-wind-solar-thermal-pumped hydro storage (CHWS-PHS) systems. First, a Hybrid Pumped Hydro Storage Adaptive Initial Reservoir Capacity (HPHS-AIRC) strategy is developed to enhance the system’s regulation capability by optimizing initial reservoir levels that are synchronized with renewable generation patterns. Then, Non-anticipativity Constraints (NACs) are incorporated into this model to ensure the dynamic adaptation of investment decisions under multi-timescale uncertainties, including inter-annual natural water inflow (NWI) variations and hourly fluctuations in wind and solar power. Simulation results on the IEEE 118-bus system show that the proposed MSSP model reduces total costs by 6% compared with the traditional two-stage approach (TSSP). Moreover, the HPHS-AIRC strategy improves pumped hydro utilization by 33.8%, particularly benefiting scenarios with drought conditions or operational constraints. Full article

The integration of renewable energy sources, specifically photovoltaic generation, into the grid at a large scale has significantly heightened the volatility and unpredictability of the power system. Consequently, this presents formidable challenges to ensuring the reliable operation of the grid. This study introduces a novel stacked model for photovoltaic power prediction, integrating multiple conventional data processing methods as base learners, including Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), and Emotional Neural Network (ENN). A Backpropagation Neural Network (BPNN) serves as the meta-learner, utilizing the outputs of the base learners as input features to enhance overall prediction accuracy by mitigating individual model errors. To assess the model’s effectiveness, five evaluation metrics are employed: Bayesian Information Criterion (BIC), Percent Mean Average Relative Error (PMARE), Legates and McCabe Index (LM), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE), ensuring long-term stability in photovoltaic power output forecasting. Additionally, the model’s effectiveness and accuracy are validated using operational data from photovoltaic power plants in a particular province of China. The results indicate that the stacked model, after training, testing, and validation on multiple performance metrics, surpasses baseline single models in performance. Full article

In Europe, renewable energy sources such as photovoltaic panels and wind power plants are developing dynamically. The growth of renewable energy is driven by rising energy prices, greenhouse gas emission restrictions, the European Union’s Green Deal policy, and decarbonization efforts. Photovoltaic farms generate energy intermittently, depending on weather conditions. Given the increasing number of new installations, ensuring the power balance and transmission capacity of the electrical grid has become a major challenge. To address this issue, the authors propose a technical solution that allows the energy generated by photovoltaic systems to be stored in the form of heat. Thermal energy from solar power and wind energy offers significant potential for energy storage. It can be accumulated during summer in specially designed sand-based heat storage systems and then used for heating purposes in winter. This approach not only reduces heating costs but also decreases greenhouse gas emissions and helps balance the power grid during sunny periods. Post-industrial areas, often located near city centers, are suitable locations for large-scale heat storage facilities supplying, among others, public utility buildings. Therefore, this article presents a concept for utilizing high-temperature sand-based heat storage systems built in decommissioned underground mining excavations. Full article

To maximize photovoltaic (PV) production, it is necessary to estimate the amount of solar radiation that is available on Earth’s surface, as it can occasionally vary. This study aimed to systematize the parametric forecast (PF) of solar energy over time, adopting the validation of estimates by machine learning models (MLMs), with highly complex analyses as inclusion criteria and studies not validated in the short or long term as exclusion criteria. A total of 145 scholarly sources were examined, with a value of 0.17 for bias risk. Four components were analyzed: atmospheric, temporal, geographic, and spatial components. These quantify dispersed, absorbed, and reflected solar energy, causing energy to fluctuate when it arrives at the surface of a PV plant. The results revealed strong trends towards the adoption of artificial neural network (ANN), random forest (RF), and simple linear regression (SLR) models for a sample taken from the Nipepe station in Niassa, validated by a PF model with errors of 0.10, 0.11, and 0.15. The included studies’ statistically measured parameters showed high trends of dependence on the variability in transmittances. The synthesis of the results, hence, improved the accuracy of the estimations produced by MLMs, making the model applicable to any reality, with a very low margin of error for the calculated energy. Most studies adopted large time intervals of atmospheric parameters. Applying interpolation models can help extrapolate short scales, as their inference and treatment still require a high investment cost. Due to the need to access the forecasted energy over land, this study was funded by CS–OGET. Full article

In the context of rapid decarbonization, photovoltaics (PV) has played a key role. Traditionally, PV installations require large land areas, leading to competition between PV and agriculture for land use. This conflict must be addressed as the demand for both energy and food continues to rise. Additionally, it poses broader challenges, potentially leading local communities to perceive PV energy production as a threat to their economic activities and food security. An emerging and promising solution is agrivoltaics (AV), a combination of agriculture and PV. AV comes in many different forms, ranging from the simple coexistence of crops and PV installations on the same patch of land to a full synergy of the two, producing better crops while also harvesting energy from the sun. This paper paints a complete picture of the scientific work produced so far throughout the field, with special attention to the use of third-generation PV and luminescent solar concentrators (LSCs). Both technologies minimize shading and enable wavelength selection and enrichment (when functionalized with fluorescent materials) to better align with the photosynthetic needs of plants. The viability of AV has also been evaluated from an economic standpoint. This work aims to assess the current landscape of AV research and to point out possible future developments. It also seeks to evaluate whether the advantages of semi-transparent devices are substantial enough to justify their development and employment on a scale comparable to traditional PV. Full article

Shared energy storage (SES) and some photovoltaic prosumers (PVPs) are difficult to aggregate by the virtual power plant (VPP) in the short term. In order to realize the optimal operation of the VPP in the incomplete resource aggregation environment and to promote the mutual benefit of multiple market entities, the energy trading strategy based on the hybrid game of SES–VPP–PVP is proposed. Firstly, the whole system configuration with incomplete resource aggregation is proposed, as well as the preconfigured market rules and the general problem for the optimal energy trading strategy of VPP. Secondly, the novel hybrid game theory-based optimization for the energy trading strategy of VPP is proposed based on the multi-level game theory model. And, the corresponding solving process using Karush–Kuhn–Tucker (KKT), dichotomy, and alternating direction method of multipliers (ADMM) algorithms are also constructed to solve nonconvex nonlinear models. The effectiveness of the proposed strategy is verified through the comparison of a large number of simulation results. The results show that our proposed energy trading strategy can be used for optimal low-carbon operation of VPPs with large-scale renewable energy and some unaggregated electricity consumers and distributed photovoltaic stations, while SES participates as an independent market entity. 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

This study explores the potential for co-locating floating photovoltaics (FPVs) with existing hydropower plants (HPPs) in Ecuador. Ecuador’s heavy reliance on hydropower for electricity generation, combined with recent blackouts caused by prolonged dry seasons, underscores the importance of diversifying energy sources. The integration of FPVs with HPPs offers a promising opportunity to enhance energy security by reducing dependency on a single energy source and improving economic, electrical, and environmental outcomes. In this paper, we assess all HPPs in Ecuador and quantify the potential performance of FPV systems when installed at their sites. Our results show that FPV systems can not only contribute additional electricity to the grid but also improve HPP performance by reducing water evaporation from reservoirs and maintaining generation capacity during dry seasons, when solar irradiation is typically higher. To model the energy production, yield, and performance of the FPV systems, we applied RINA’s methodology to estimate representative weather conditions for each site and simulate FPV performance, accounting for system design loss factors. Additionally, we calculated the water savings resulting from FPV installation. Our findings reveal that, out of approximately 70 HPPs in Ecuador, 11 present favorable conditions for large-scale FPV deployment. Among these, Cumbayá HPP (40 MW) exhibited the most suitable conditions, supporting a maximum FPV capacity of 17 MWp. Marcel Laniado de Wind HPP (213 MW) and Mazar HPP (170 MW) were also identified as optimal candidates, each with potential FPV capacities equal to their installed HPP capacities. While this study primarily aims to provide scientific evidence on the potential of FPV-HPP co-location, the results and methodology can also guide Ecuadorian government authorities and investors in adopting FPV technology to strengthen the country’s energy infrastructure. Full article