Power Generation Research Articles

Analysis of Optical Window for Constant Cooling Heat Flux‐Based Spectral Splitting in Concentrator Photovoltaic System

The solar cells cooled to constant temperature at different concentration ratios (CR) and spectral bands (SB) require the same cooling heat flux (CHF), but the output power varies significantly. Thus, it is necessary to clarify the relationship of CHF‐CR‐SB to provide a reference for photovoltaic systems to select the output power generation of the spectral band under constant cooling heat flux. In this article, a selecting spectra model of a centralized photovoltaic (CPV) system is established and the selection of the spectrum based on CHF and the CR is analyzed. Theory shows that the wider the spectral division of the same CR, the larger the cooling heat flux consumed. The narrower the spectrum division, the higher the CR that the cell receiver can withstand. The experiments show that the higher the photoelectric conversion efficiency (PCE), the lower the cooling heat flux to be consumed. The cooling heat flux of 650 filter consumes 22.74% more than the UV700 filter, which means the demand for CHF is more severe when the heat spectrum distribution is wide. The spectral division should be carried out according to the requirements of high‐energy flux but a small thermal energy proportion to achieve efficient PCE.

A study of Power Generation From a Traffic Tunnel Using Vertical Axis Wind Turbine

A study of Power Generation From a Traffic Tunnel Using Vertical Axis Wind Turbine

Investigation of roll forming process and quality control factors for metal bipolar plates

Metal bipolar plates (BPPs) are crucial components of proton exchange membrane fuel cells (PEMFCs), with the quality of their formation affects the power generation efficiency and lifespan of the fuel cells. The predominant manufacturing method, stamping, often results in localized excessive stretching of metal BPPs, thereby limiting the depth of the flow channels. Roll forming, dominated by bending deformation, is a forming process that ensures uniform thinning and greater depth of BPPs. The multi-directional flow channel roll forming model for BPPs is constructed in this paper for the first time, the characteristics of the roll forming process is investigated. Effects of process parameters on the forming results are discussed. The investigation indicates that during the roll forming process, the channel ports and corners are critical areas where defects are most likely to occur. The average filling ratio of the longitudinal flow channels is 5.6% higher than that of the transverse flow channels. The plastic strain in the longitudinal flow channels is about 36% higher than in the transverse ones, which making the form is more difficult to form, but the thickness is more uniform. Increasing the diameter of the rollers can enhance the filling ratio and flatness, but it will exacerbate thinning at the corners of the flow channels. When the ratio of the flow channel width to the rib width is close to 1, the filling ratio of the BPP is higher, with less thinning, resulting in a better forming result. As the depth of the flow channel increases, resulting in a thinner BPP, when depth-to-width ratio exceeding 0.6 is more likely to lead to failure. Increasing the speed of roll forming can lead to a greater thinning of the BPP and may even cause it to crack. The thinner the sheet metal is, the higher the filling ratio of the BPP and the more uniform its thickness. Those results can provide technical references for the structural design and forming process optimization of BPPs.

Effects of Single‐Axis and Fixed‐Tilt Photovoltaic Array Construction on the Soil Seed Bank Characteristics in Semi‐Arid Grasslands

ABSTRACTWith the rapid global development of photovoltaic power generation, research on its impact on land and ecosystems has become increasingly significant. However, its impact on soil seed bank characteristics has yet to be better assessed. In this study, monitoring plots were established in a semi‐arid grassland undergoing solar energy development. This setup allowed us, for the first time, to investigate how soil seed bank characteristics respond to the construction of two typical photovoltaic array systems: single‐axis and fixed‐tilt systems. This study demonstrated that in both single‐axis and fixed‐tilt systems, the establishment of photovoltaic arrays resulted in a significant increase in soil seed density, with seed counts rising by approximately 47.5% compared with control sites without arrays. The aggregation effect of soil seed density under the photovoltaic array primarily occurred in the 0–10 cm soil layer. The soil seed density under the single‐axis arrays was higher than that under the fixed‐tilt arrays. The construction of photovoltaic arrays resets local soil and directly changes the micro‐environment—including reductions in solar radiation, decreases in average temperature by 0.1°C, and wind speed decreases by 1.5 m/s—which negatively affected the richness and diversity of the soil seed bank, resulting in a 21.1% decrease in species richness and a 10.1% reduction in seed diversity. Furthermore, this study highlights that seed germination in semi‐arid grasslands is under pressure due to environmental changes associated with photovoltaic construction areas. Specifically, soil moisture and organic matter were the key factors affecting the vegetation restoration potential of the entire construction area. We recommend selecting the single‐axis system of photovoltaic components. This selection is crucial, which considers both energy production efficiency and supports the facilitation of future vegetation ecosystem succession. Altogether, this study provides information for future land‐use planning in photovoltaic construction areas and sustainable development of photovoltaic power generation.

Ensemble learning and deep learning-based defect detection in power generation plants

Abstract One of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses, including the telecommunications industry, are constantly emerging. Therefore, they must rely on generators to ensure their full functionality. These generators rely on fuel to function, and consumption is usually high if not properly monitored. Monitoring is usually done by a (non-expert) human. This can sometimes be a tedious process, as some companies have reported excessively high consumption rates. For anomaly detection in power generating plants, the studies by Mulongo et al. and Atemkeng and Jimoh used the same dataset to train a multilayer perceptron (MLP) and generative adversarial networks (GANs), respectively, achieving an accuracy of 96.1% with MLP and 98.9% with GAN. Through comparative analysis and the use of ensemble learning techniques, we found that ensemble learning models outperform both MLP and GAN as proposed by Mulongo et al. and Atemkeng and Jimoh using the same dataset. Furthermore, we investigated the potential of autoencoders to outperform MLPs, GANs, and ensemble learning models. To this end, we have introduced a label-assisted autoencoder approach for detecting anomalies in power-generating plants. This model includes a labelling assistance module that adjusts the thresholds. Our results indicate that the label-assisted autoencoder outperforms the MLP. However, GANs and all ensemble learning models outperformed the label-assisted autoencoder. Nevertheless, the use of a label-assisted autoencoder offers a distinct advantage in categorizing anomalies based on their severity, a capability not present in ensemble learning models and GANs.

An Experimental Study on Behavior of Power Conditioning System for Photovoltaic Power Generation System Connected to End of Distribution Line

An Experimental Study on Behavior of Power Conditioning System for Photovoltaic Power Generation System Connected to End of Distribution Line

Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm

AbstractDistributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate.

Structural Health Monitoring and Failure Analysis of Large-Scale Hydro-Steel Structures, Based on Multi-Sensor Information Fusion

Large-scale hydro-steel structures (LS-HSSs) are vital to hydraulic engineering, supporting critical functions such as water resource management, flood control, power generation, and navigation. However, due to prolonged exposure to severe environmental conditions and complex operational loads, these structures progressively degrade, posing increased risks over time. The absence of effective structural health monitoring (SHM) systems exacerbates these risks, as undetected damage and wear can compromise safety. This paper presents an advanced SHM framework designed to enhance the real-time monitoring and safety evaluation of LS-HSSs. The framework integrates the finite element method (FEM), multi-sensor data fusion, and Internet of Things (IoT) technologies into a closed-loop system for real-time perception, analysis, decision-making, and optimization. The system was deployed and validated at the Luhun Reservoir spillway, where it demonstrated stable and reliable performance for real-time anomaly detection and decision-making. Monitoring results over time were consistent, with stress values remaining below allowable thresholds and meeting safety standards. Specifically, stress monitoring during radial gate operations (with a current water level of 1.4 m) indicated that the dynamic stress values induced by flow vibrations at various points increased by approximately 2 MPa, with no significant impact loads. Moreover, the vibration amplitude during gate operation was below 0.03 mm, confirming the absence of critical structural damage and deformation. These results underscore the SHM system’s capacity to enhance operational safety and maintenance efficiency, highlighting its potential for broader application across water conservancy infrastructure.

Investigation of an Ammonia-Fueled SOFC-mGT Hybrid System: Performances Analysis and Comparison With Natural Gas-Based System

Abstract Hybrid systems (HS) are highly attractive in power generation due to their potential for higher power outputs and efficiencies by integrating different technologies. Among solid oxide fuel cell (SOFC) plants, the SOFC-Micro-Gas Turbine (mGT) system is particularly innovative. In this setup, the SOFC replaces the mGT combustion chamber, with an afterburner completing fuel combustion before the expander. Various configurations and control systems have been explored over the years, and prototypes have demonstrated satisfactory efficiency. However, these systems have predominantly used NG and Biogas, which do not achieve zero carbon emissions. The growing global demand for carbon-free energy production is increasing, highlighting the importance of alternative fuels in the power generation sector: among them, thanks to its chemical and physical properties, ammonia is gaining more and more interest. This work investigates an innovative ammonia-to-power system based on an SOFC-mGT HS, focusing on thermodynamic parameters, system features, and technical and environmental challenges. A MATLAB/Simulink model, built on a validated HS model fueled by NG, was developed to analyze the new system configuration. This configuration replaces the SOFC prereformer with an ammonia cracker, allowing for the exploration of anode gas recirculation effects and optimal system design. Key operating parameters such as anodic recirculation factor, fuel utilization, performance, and gas turbine characteristics are discussed. Finally, a comparison is made between hybrid systems and mGTs powered by methane, biogas, and ammonia.

Trends and opportunities in renewable energy investment in Saudi Arabia: Insights for achieving vision 2030 and enhancing environmental sustainability

Renewable energy has emerged as a critical alternative to traditional energy sources due to growing environmental concerns and the need for sustainable energy development. Saudi Arabia, driven by its Vision 2030, has strongly committed to shifting from fossil fuels to renewable energy sources such as solar, wind, and hydrogen. Despite significant progress, existing investment methods often focus narrowly on financial returns and do not adequately address the risks and uncertainties associated with renewable energy projects, such as policy changes, technological challenges, and fluctuating energy prices. This paper aims to highlight and compare the trends in renewable energy investments and incorporate a risk analysis matrix to address the economic, environmental, and social aspects of Saudi Arabia’s environment. The details used in the study are obtained from Kaggle, while the adopted analysis technique involves multi-criteria decision-making (MCDM). It provides a fresh qualitative approach that allows an all-sided assessment of the investment prospects by focusing on appropriate weight for different factors. The analysis results show that using the proposed framework can help effectively prioritize investment opportunities identifying major trends and risks in developing renewable energy sources in Saudi Arabia. Current investment trends have been reviewed systematically; experts have been interviewed; MCDM techniques have been employed, as the methodology effectiveness improves the comprehension of investment tendencies and contributes to strategic management in the renewable energy market stakeholder’s activity. The MCDM analysis results show that the solar energy system has the highest investment index in the Saudi Arabian cities; the first place belongs to Abha with an investment index of 0.93, the second place belongs to Jeddah with an investment index of 0.85, and the third place belongs to Dammam with an investment index of 0.75. From these outcomes, we can easily conclude that Abha is the best place to place a solar energy project, probably because of environmental and legal factors. The analysis underscores geographic factors in deploying renewable energy, directing investors and policymakers to cities with favourable solar power generation about sustainability and economic objectives. The results are expected to enhance investment awareness among local and international stakeholders, support the strategic goals of Vision 2030, and contribute to a more sustainable and diversified energy future for Saudi Arabia. This study provides a strategic framework for optimizing renewable energy investments, promoting sustainable development, and achieving long-term economic growth. Data AvailabilityData sharing is not applicable to this article as no data sets were generated during the current study.

Optimal spatial arrangement of modules for large‐scale photovoltaic farms in complex topography

AbstractThe existing methods for determining the module arrangement in photovoltaic (PV) farms are considered insufficient as they are generally limited to the environment of flat ground without considering both physical and electrical factors. The orientations of PV modules may be very diverse when installed in places with complex topography, e.g. mountains and abandoned mine sites. Thus, the received irradiance by the modules is inconsistent directly results in current differences and hence leads to significant mismatch loss in the PV arrays. This paper proposes a solution to determine the most appropriate combination of tilts and orientations of PV modules as well as the arrangement of PV arrays. The complex topographies are fully considered to minimize the mismatch loss phenomenon, and hence the power generation degradation. The solution adopts a set of models, i.e. the irradiation model for the calculation of optimal module tilts and orientations, the shadow model for shadow effect analysis, and the mismatch model for mismatch condition analysis. A two‐layer multi‐objective optimization is implemented for the optimal arrangement. The proposed solution is assessed through the case study of a 30 MW PV farm. The result confirms the effectiveness of the proposed solution for the optimal spatial module placement.

Multi-agent based optimal sizing of hybrid renewable energy systems and their significance in sustainable energy development

This paper delves into the enhancement and optimization of on-grid renewable energy systems using a variety of renewable energy sources, with a particular focus on large-scale applications designed to meet the energy demand of a certain load. As global concerns surrounding climate change continue to mount, the urgency of replacing traditional fossil fuel-based power generation with cleaner, more cost-effective and dependable alternatives becomes increasingly apparent. In this context, a comprehensive investigation is conducted on grid connected hybrid energy system that combines photovoltaic, wind, and fuel cell technologies. The study employs three state-of-the-art optimization algorithms, namely Walrus Optimization Algorithm (WaOA), Coati Optimization Algorithm (COA), and Osprey Optimization Algorithm (OOA) to determine the optimal system size and energy management strategies, all aimed at minimizing the cost of energy (COE) for grid-based electricity. The results of the optimization process are compared with the results obtained from the utilization of the Particle swarm optimization (PSO) and Grey Wolf optimizer (GWO). The findings of this study underscore both the practical feasibility and the critical importance of adopting on-grid renewable energy systems to decrease the dependence on traditional energy sources within the grid. The proposed WaOA succeeded to reach the optimal solution of the optimal design process with a COE of 0.51758129611 $//kwh while keeping the loss of power supply probability (LPSP), the reliability index, at 7.303681e-19. The practical recommendations and forward-looking insights provided within this research hold the potential to foster sustainable development and effectively mitigate carbon emissions in the future.

Performance of PSO-GWO, VAGWO, and GWO optimization techniques for economic dispatch problem: studied case of the southeast algerian electrical network

Economic dispatch (ED) aims to identify the most cost-effective strategy for allocating power generation while meeting demand and adhering to the physical constraints of the power system. In this paper, three algorithms are proposed to solve the ED problem in power systems, including a hybrid Particle Swarm Optimization with Grey Wolf Optimizer (PSO-GWO), modified Velocity Aided Grey Wolf Optimizer (VAGWO), and Grey Wolf Optimizer (GWO). PSO is a meta-heuristic optimization technique designed to find the optimal solution to a problem by guiding the movement of particles within a defined exploration space. GWO is also a meta-heuristic optimization algorithm based on the natural behavior of grey wolves. In this paper, to enhance the performance of GWO, it is first combined with the PSO method. VAGWO is employed to refine the GWO formulation by optimizing the convergence steps. At the beginning of the iterations, the distance between the solutions (wolves) is increased to promote exploration of the search space. As the iterations progress, the step size is reduced, allowing for a more precise and efficient convergence toward the optimal solution. These algorithms will be implemented in Matlab software to minimize the fuel cost production of the following test networks: IEEE 30-bus network, Algerian 114-bus network, and Southeast Algerian network. These methods show that the PSO-GWO algorithm offers better results when compared through VAGWO and GWO.

Application of deep forest algorithm incorporating seasonality and temporal correlation for wind speed prediction in offshore wind farm

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation.

Short-term forecast of solar irradiance components using an alternative mathematical approach for the identification of cloud features

Solar energy technologies require precise solar forecasting to reduce power generation losses and protect equipment from irradiance fluctuations. This study introduces an alternative methodology for short-term forecasting of direct normal irradiance (DNI) and global horizontal irradiance (GHI) utilizing ground-based sky images captured by a single device. A low-cost all-sky imager (ASI) was developed, which implements an angular transformation and an optical flow technique to extract cloud features such as shape and velocity. A mathematical model calculates cloud transmittance based on pixel intensity, eliminating complex training steps. Results from a 30-day experimental campaign, incorporating diverse meteorological conditions, were compared against a secondary standard solarimetric station, a smart persistence model, and state-of-the-art approaches. The DNI forecast achieved an RMSE (relative error) of 46.79 W/m2 (11.99%) for 1-min intervals and 90.21 W/m2 (17.54%) for 10-min intervals, while GHI ranged from 31.73 W/m2 (4.68%) to 75.02 W/m2 (13.63%). Pearson correlation coefficients exceeded 0.9 overall, reaching 0.98 and 0.99 for the 1-min DNI and GHI forecasts, and 0.91 and 0.96 for the 10-min DNI and GHI forecasts, respectively, underscoring the system’s accuracy and robustness in complex meteorological scenarios.

First-principle’s calculations to investigate structural, electronic, mechanical, optical, and thermoelectric properties of halide double perovskites K2InScX6 (X = Cl, Br, and I) for thermoelectric and optoelectronic applications

Physical properties of double perovskites K2InScX6 (X = Cl, Br, and I) are investigated using density functional theory (DFT) with generalized gradient approximation (GGA) and perdew-burke-ernerhof (PBE) exchange co-relational functional. Structural stability is confirmed through geometry optimization. Band gaps are calculated using Trans Blaha-modified Becke-Johnson (TB-mBJ) potential and GGA-PBE methods. The obtained band gaps have direct nature. The structural stability has also been confirmed by calculating enthalpy formation (ΔHf), cohesive energy (Ecoh), stiffness coefficients (Cij), tolerance (τG) and octahedral factor (µ). Two materials K2InScCl6, and K2InScBr6 are ductile and one is K2InScI6 brittle. The optical parameters inferred that the maximum absorption of photon energy lies within the visible and ultraviolet regions enabling their use in UV photodetectors and optoelectronics, where light absorption is important for performance. The thermoelectric (TE) parameters like Seebeck coefficient, thermal and electrical conductivity, carrier concentration, power factor and figure of merit ZT parameters are calculated and ZT values are 0.75, 0.94, and 0.91 for K2InScCl6, K2InScBr6, and K2InScI6 at room temperature. These results enable the development of more stable, and high-conversion efficiency devices for TE applications such as thermo-electric power generators.

Operation study of the photovoltaic thermal heat pump integrated energy system under trigeneration condition and harsh conditions

In recent years, the photovoltaic thermal heat pump has gained significant attention as a near zero-carbon energy technology, due to the comprehensive ability to provide heating in winter, cooling in summer, photovoltaic power generation and domestic hot water throughout the year. However, owing to the incomplete research on the system operating characteristics in trigeneration condition and harsh conditions, the production performance and operational stability of the photovoltaic thermal heat pump were not widely recognized. In view of the above problems, the following studies were conducted in this paper: First, the integrated energy system based on the modular photovoltaic thermal heat pump was designed and constructed. Then, the system operation in severe hot and cold season was carried out and the operational stability of the system under harsh operating conditions was demonstrated sufficiently. Afterwards, the operation of the system under the continuous trigeneration operation were analyzed. During the 7-day trigeneration operation, the average hot water output was 12.12 tons and the average photovoltaic power generation was 88.5 kWh in the daytime heating operation. Besides, the average coefficient of cooling performance was 2.32 in the nighttime cooling operation and the effective cooling-supply time of the system was approximately 4 h.

Multi‐timescale optimal scheduling of microgrids for generating new energy output scenarios based on correction error sampling intervals

AbstractMicrogrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation.

An overview of Applied Artificial Intelligence in Power Grids

Power grids are networks of lines and equipment used for electrical power generation, transmission, distribution, and consumption. Power grids are undergoing a process of digital transformation based on emerging information and communication technologies, with the aim of optimising the operation of power systems, increasing efficiency, reliability, sustainability, security, and quality of service, while reducing CO2 emissions. In this process of digital transformation, Artificial Intelligence (AI) has become a strategic element within power grids. This paper provides an overview of AI applications in power grids and how they can improve the efficiency, reliability, and availability of their processes. It analyses the main challenges and prospects for the future application of AI in power grids. AI applications help process large volumes of data and generate insights, with a focus on two primary functions: supporting operations and aiding strategic decision-making. These factors highlight the significant potential for AI application in power grids and solidify AI as a strategic key to the development of smart grids.

Defect Engineering with Rational Dopants Modulation for High-Temperature Energy Harvesting in Lead-Free Piezoceramics

HighlightsThe solution limit of manganese ion in BiFeO3–BaTiO3 (BF–BT) was determined by combining multiple advanced characterization methods.The defect engineering associated with fine doping can realize the co-modulation of polarization configuration, iron oxidation state and domain orientation.The BF–BT–0.2Mn piezoelectric energy harvester shows excellent power generation capacity at 250 °C, which is an important breakthrough for lead-free piezoelectric devices.