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Search Results (947)

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Keywords = photovoltaic response

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30 pages, 4444 KiB  
Article
Unveiling the Potential of Novel Ternary Chalcogenide SrHfSe3 for Eco-Friendly, Self-Powered, Near-Infrared Photodetectors: A SCAPS-1D Simulation Study
by Salah Abdo, Ambali Alade Odebowale, Amer Abdulghani, Khalil As’ham, Sanjida Akter, Haroldo Hattori, Nicholas Kanizaj and Andrey E. Miroshnichenko
Sci 2025, 7(3), 113; https://doi.org/10.3390/sci7030113 - 6 Aug 2025
Abstract
Ternary chalcogenide-based sulfide materials with distorted morphologies such as BaZrS3, CaZrS3, and SrZrS3, have recently gained much attention in optoelectronics and photovoltaics due to their high structural and thermal stability and compatibility with low-cost, earth-abundant synthesis routes. [...] Read more.
Ternary chalcogenide-based sulfide materials with distorted morphologies such as BaZrS3, CaZrS3, and SrZrS3, have recently gained much attention in optoelectronics and photovoltaics due to their high structural and thermal stability and compatibility with low-cost, earth-abundant synthesis routes. However, their relatively large bandgaps often limit their suitability for near-infrared (NIR) photodetectors. Here, we conducted a comprehensive investigation of SrHfSe3, a ternary chalcogenide with an orthorhombic crystal structure and distinctive needle-like morphology, as a promising candidate for NIR photodetection. SrHfSe3 exhibits a direct bandgap of 1.02 eV, placing it well within the NIR range. Its robust structure, high temperature stability, phase stability and natural abundance make it a compelling material for next-generation, self-powered NIR photodetectors. An in-depth analysis of the SrHfSe3-based photodetector was performed using SCAPS-1D simulations, focusing on key performance metrics such as J–V behavior, photoresponsivity, and specific detectivity. Device optimization was achieved by thoroughly altering each layer thickness, doping concentrations, and defect densities. Additionally, the influence of interface defects, absorber bandgap, and operating temperature was assessed to enhance the photoresponse. Under optimal conditions, the device achieved a short-circuit current density (Jsc) of 45.88 mA/cm2, an open-circuit voltage (Voc) of 0.7152 V, a peak photoresponsivity of 0.85 AW−1, and a detectivity of 2.26 × 1014 Jones at 1100 nm. A broad spectral response spanning 700–1200 nm confirms its efficacy in the NIR region. These results position SrHfSe3 as a strong contender for future NIR photodetectors and provide a foundation for experimental validation in advanced optoelectronic applications. Full article
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30 pages, 2504 KiB  
Article
Battery Energy Storage Systems: Energy Market Review, Challenges, and Opportunities in Frequency Control Ancillary Services
by Gian Garttan, Sanath Alahakoon, Kianoush Emami and Shantha Gamini Jayasinghe
Energies 2025, 18(15), 4174; https://doi.org/10.3390/en18154174 - 6 Aug 2025
Abstract
Battery energy storage systems (BESS) are considered a good energy source to maintain supply and demand, mitigate intermittency, and ensure grid stability. The primary contribution of this paper is to provide a comprehensive overview of global energy markets and a critical analysis of [...] Read more.
Battery energy storage systems (BESS) are considered a good energy source to maintain supply and demand, mitigate intermittency, and ensure grid stability. The primary contribution of this paper is to provide a comprehensive overview of global energy markets and a critical analysis of BESS’ participation in frequency control ancillary service (FCAS) markets. This review synthesises the current state of knowledge on the evolution of the energy market and the role of battery energy storage systems in providing grid stability, particularly frequency control services, with a focus on their integration into evolving high-renewable-energy-source (RES) market structures. Specifically, solar PV and wind energy are emerging as the main drivers of RES expansion, accounting for approximately 61% of the global market share. A BESS offers greater flexibility in storage capacity, scalability and rapid response capabilities, making it an effective solution to address emerging security risks of the system. Moreover, a BESS is able to provide active power support through power smoothing when coupled with solar photovoltaic (PV) and wind generation. In this paper, we provide an overview of the current status of energy markets, the contribution of battery storage systems to grid stability and flexibility, as well as the challenges that BESS face in evolving electricity markets. Full article
20 pages, 1895 KiB  
Article
Distributed Low-Carbon Demand Response in Distribution Networks Incorporating Day-Ahead and Intraday Flexibilities
by Bin Hu, Xianen Zong, Hongbin Wu and Yue Yang
Processes 2025, 13(8), 2460; https://doi.org/10.3390/pr13082460 - 4 Aug 2025
Viewed by 25
Abstract
In this paper, we present a distributed low-carbon demand response method in distribution networks incorporating day-ahead and intraday flexibilities on the demand side. This two-stage demand dispatch scheme, including day-ahead schedule and intraday adjustment, is proposed to facilitate the coordination between power demand [...] Read more.
In this paper, we present a distributed low-carbon demand response method in distribution networks incorporating day-ahead and intraday flexibilities on the demand side. This two-stage demand dispatch scheme, including day-ahead schedule and intraday adjustment, is proposed to facilitate the coordination between power demand and local photovoltaic (PV) generation. We employ the alternating direction method of multipliers (ADMM) to solve the dispatch problem in a distributed manner. Demand response in a 141-bus test system serves as our case study, demonstrating the effectiveness of our approach in shifting power loads to periods of high PV generation. Our results indicate remarkable reductions in the total carbon emission by utilizing more distributed PV generation. Full article
(This article belongs to the Special Issue Modeling, Operation and Control in Renewable Energy Systems)
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23 pages, 1146 KiB  
Review
A Review of Optimization Scheduling for Active Distribution Networks with High-Penetration Distributed Generation Access
by Kewei Wang, Yonghong Huang, Yanbo Liu, Tao Huang and Shijia Zang
Energies 2025, 18(15), 4119; https://doi.org/10.3390/en18154119 - 3 Aug 2025
Viewed by 239
Abstract
The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations [...] Read more.
The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch. Full article
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14 pages, 5172 KiB  
Article
Sustainable Metal Recovery from Photovoltaic Waste: A Nitric Acid-Free Leaching Approach Using Sulfuric Acid and Ferric Sulfate
by Payam Ghorbanpour, Pietro Romano, Hossein Shalchian, Francesco Vegliò and Nicolò Maria Ippolito
Minerals 2025, 15(8), 806; https://doi.org/10.3390/min15080806 - 30 Jul 2025
Viewed by 225
Abstract
In recent years, recovering precious and base metals such as silver and copper from end-of-life products has become a fundamental factor in the sustainable development of many countries. This not only supports environmental goals but is also a profitable economic activity. Therefore, in [...] Read more.
In recent years, recovering precious and base metals such as silver and copper from end-of-life products has become a fundamental factor in the sustainable development of many countries. This not only supports environmental goals but is also a profitable economic activity. Therefore, in this study, we investigate the recovery of silver and copper from an end-of-life photovoltaic panel powder using an alternative leaching system containing sulfuric acid and ferric sulfate instead of nitric acid-based leaching systems, which are susceptible to producing hazardous gases such as NOx. To obtain this goal, a series of experiments were designed with the Central Composite Design (CCD) approach using Response Surface Methodology (RSM) to evaluate the effect of reagent concentrations on the leaching rate. The leaching results showed that high recovery rates of silver (>85%) and copper (>96%) were achieved at room temperature using a solution containing only 0.2 M sulfuric acid and 0.15 M ferric sulfate. Analysis of variance was applied to the leaching data for silver and copper recovery, resulting in two statistical models that predict the leaching efficiency based on reagent concentrations. Results indicate that the models are statistically significant due to their high R2 (0.9988 and 0.9911 for Ag and Cu, respectively) and the low p-value of 0.0043 and 0.0003 for Ag and Cu, respectively. The models were optimized to maximize the dissolution of silver and copper using Design Expert software. Full article
(This article belongs to the Special Issue Recycling of Mining and Solid Wastes)
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21 pages, 1456 KiB  
Article
Life Cycle Assessment of Land Use Trade-Offs in Indoor Vertical Farming
by Ana C. Cavallo, Michael Parkes, Ricardo F. M. Teixeira and Serena Righi
Appl. Sci. 2025, 15(15), 8429; https://doi.org/10.3390/app15158429 - 29 Jul 2025
Viewed by 230
Abstract
Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. [...] Read more.
Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article
(This article belongs to the Special Issue Innovative Engineering Technologies for the Agri-Food Sector)
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27 pages, 910 KiB  
Article
QES Model Aggregating Quality, Environmental Impact, and Social Responsibility: Designing Product Dedicated to Renewable Energy Source
by Dominika Siwiec and Andrzej Pacana
Energies 2025, 18(15), 4029; https://doi.org/10.3390/en18154029 - 29 Jul 2025
Viewed by 206
Abstract
The complexity of assessment is a significant problem in designing renewable energy source (RES) products, especially when one wants to take into account their various aspects, e.g., technical, environmental, or social. Hence, the aim of the research is to develop a model supporting [...] Read more.
The complexity of assessment is a significant problem in designing renewable energy source (RES) products, especially when one wants to take into account their various aspects, e.g., technical, environmental, or social. Hence, the aim of the research is to develop a model supporting the decision-making process of RES product development based on meeting the criteria of quality, environmental impact, and social responsibility (QES). The model was developed in four main stages, implementing multi-criteria decision support methods such as DEMATEL (decision-making trial and evaluation laboratory) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), as well as criteria for social responsibility and environmental impact from the ISO 26000 standard. The model was tested and illustrated using the example of photovoltaic panels (PVs): (i) five prototypes were developed, (ii) 30 PV criteria were identified from the qualitative, environmental, and social groups, (iii) the criteria were reduced to 13 key (strongly intercorrelated) criteria according to DEMATEL, (iv) the PV prototypes were assessed taking into account the importance and fulfilment of their key criteria according to TOPSIS, and (v) a PV ranking was created, where the fifth prototype turned out to be the most advantageous (QES = 0.79). The main advantage of the model is its simple form and transparency of application through a systematic analysis and evaluation of many different criteria, after which a ranking of design solutions is obtained. QES ensures precise decision-making in terms of sustainability of new or already available products on the market, also those belonging to RES. Therefore, QES will find application in various companies, especially those looking for low-cost decision-making support techniques at early stages of product development (design and conceptualization). Full article
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19 pages, 6937 KiB  
Article
Optimal Placement of Distributed Solar PV Adapting to Electricity Real-Time Market Operation
by Xi Chen and Hai Long
Sustainability 2025, 17(15), 6879; https://doi.org/10.3390/su17156879 - 29 Jul 2025
Viewed by 274
Abstract
Distributed photovoltaic (PV) generation is increasingly important for urban energy systems amid global climate change and the shift to renewable energy. Traditional PV deployment prioritizes maximizing energy output, often neglecting electricity price variability caused by time-of-use tariffs. This study develops a high-resolution planning [...] Read more.
Distributed photovoltaic (PV) generation is increasingly important for urban energy systems amid global climate change and the shift to renewable energy. Traditional PV deployment prioritizes maximizing energy output, often neglecting electricity price variability caused by time-of-use tariffs. This study develops a high-resolution planning and economic assessment model for building-integrated PV (BIPV) systems, incorporating hourly electricity real-time market prices, solar geometry, and submeter building spatial data. Wuhan (30.60° N, 114.05° E) serves as the case study to evaluate optimal PV placement and tilt angles on rooftops and façades, focusing on maximizing economic returns rather than energy production alone. The results indicate that adjusting rooftop PV tilt from a maximum generation angle (30°) to a maximum revenue angle (15°) slightly lowers generation but increases revenue, with west-facing orientations further improving returns by aligning output with peak electricity prices. For façades, south-facing panels yielded the highest output, while north-facing panels with tilt angles above 20° also showed significant potential. Façade PV systems demonstrated substantially higher generation potential—about 5 to 15 times that of rooftop PV systems under certain conditions. This model provides a spatially detailed, market-responsive framework supporting sustainable urban energy planning, quantifying economic and environmental benefits, and aligning with integrated approaches to urban sustainability. Full article
(This article belongs to the Special Issue Sustainable Energy Planning and Environmental Assessment)
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23 pages, 5813 KiB  
Article
Integrated Lighting and Solar Shading Strategies for Energy Efficiency, Daylighting and User Comfort in a Library Design Proposal
by Egemen Kaymaz and Banu Manav
Buildings 2025, 15(15), 2669; https://doi.org/10.3390/buildings15152669 - 28 Jul 2025
Viewed by 189
Abstract
This research proposes an integrated lighting and solar shading strategy to improve energy efficiency and user comfort in a retrofit project in a temperate-humid climate. The study examines a future library addition to an existing faculty building in Bursa, featuring highly glazed façades [...] Read more.
This research proposes an integrated lighting and solar shading strategy to improve energy efficiency and user comfort in a retrofit project in a temperate-humid climate. The study examines a future library addition to an existing faculty building in Bursa, featuring highly glazed façades (77% southwest, 81% northeast window-to-wall ratio), an open-plan layout, and situated within an unobstructed low-rise campus environment. Trade-offs between daylight availability, heating, cooling, lighting energy use, and visual and thermal comfort are evaluated through integrated lighting (DIALux Evo), climate-based daylight (CBDM), and energy simulations (DesignBuilder, EnergyPlus, Radiance). Fifteen solar shading configurations—including brise soleil, overhangs, side fins, egg crates, and louvres—are evaluated alongside a daylight-responsive LED lighting system that meets BS EN 12464-1:2021. Compared to the reference case’s unshaded glazing, optimal design significantly improves building performance: a brise soleil with 0.4 m slats at 30° reduces annual primary energy use by 28.3% and operational carbon emissions by 29.1% and maintains thermal comfort per ASHRAE 55:2023 Category II (±0.7 PMV; PPD < 15%). Daylight performance achieves 91.5% UDI and 2.1% aSE, with integrated photovoltaics offsetting 129.7 kWh/m2 of grid energy. This integrated strategy elevates the building’s energy class under national benchmarks while addressing glare and overheating in the original design. Full article
(This article belongs to the Special Issue Lighting in Buildings—2nd Edition)
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38 pages, 5939 KiB  
Article
Decentralized Energy Management for Microgrids Using Multilayer Perceptron Neural Networks and Modified Cheetah Optimizer
by Zulfiqar Ali Memon, Ahmed Bilal Awan, Hasan Abdel Rahim A. Zidan and Mohana Alanazi
Processes 2025, 13(8), 2385; https://doi.org/10.3390/pr13082385 - 27 Jul 2025
Viewed by 460
Abstract
This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The proposed framework applies an MLP-ANN with Levenberg–Marquardt (LM) training [...] Read more.
This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The proposed framework applies an MLP-ANN with Levenberg–Marquardt (LM) training for high-precision forecasts of photovoltaic/wind generation, ambient temperature, and load demand, greatly outperforming traditional statistical methods (e.g., time-series analysis) and resilient backpropagation (RP) in precision. The new MCO algorithm eliminates local trapping and premature convergence issues in classical optimization methods like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). Simulations on a test microgrid verily demonstrate the advantages of the framework, achieving a 26.8% cost-of-operation reduction against rule-based EMSs and classical PSO/GA, and a 15% improvement in forecast accuracy using an LM-trained MLP-ANN. Moreover, demand response programs embodied in the system reduce peak loads by 7.5% further enhancing grid stability. The MLP-ANN forecasting–MCO optimization duet is an effective and cost-competitive decentralized microgrid management solution under uncertainty. Full article
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21 pages, 2568 KiB  
Article
Research on the Data-Driven Identification of Control Parameters for Voltage Ride-Through in Energy Storage Systems
by Liming Bo, Jiangtao Wang, Xu Zhang, Yimeng Su, Xueting Cheng, Zhixuan Zhang, Shenbing Ma, Jiyu Wang and Xiaoyu Fang
Appl. Sci. 2025, 15(15), 8249; https://doi.org/10.3390/app15158249 - 24 Jul 2025
Viewed by 222
Abstract
The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter [...] Read more.
The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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18 pages, 687 KiB  
Article
A Low-Carbon and Economic Optimal Dispatching Strategy for Virtual Power Plants Considering the Aggregation of Diverse Flexible and Adjustable Resources with the Integration of Wind and Solar Power
by Xiaoqing Cao, He Li, Di Chen, Qingrui Yang, Qinyuan Wang and Hongbo Zou
Processes 2025, 13(8), 2361; https://doi.org/10.3390/pr13082361 - 24 Jul 2025
Viewed by 245
Abstract
Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need [...] Read more.
Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article
(This article belongs to the Section Energy Systems)
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19 pages, 2311 KiB  
Article
Stochastic Optimization of Quality Assurance Systems in Manufacturing: Integrating Robust and Probabilistic Models for Enhanced Process Performance and Product Reliability
by Kehinde Afolabi, Busola Akintayo, Olubayo Babatunde, Uthman Abiola Kareem, John Ogbemhe, Desmond Ighravwe and Olanrewaju Oludolapo
J. Manuf. Mater. Process. 2025, 9(8), 250; https://doi.org/10.3390/jmmp9080250 - 23 Jul 2025
Viewed by 379
Abstract
This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently [...] Read more.
This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. Full article
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30 pages, 1981 KiB  
Article
Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems
by Mohamed Aatabe, Wissam Jenkal, Mohamed I. Mosaad and Shimaa A. Hussien
Energies 2025, 18(15), 3899; https://doi.org/10.3390/en18153899 - 22 Jul 2025
Viewed by 386
Abstract
Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green [...] Read more.
Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a 99.9% average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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19 pages, 1406 KiB  
Article
A Comparative Study of Dimensionality Reduction Methods for Accurate and Efficient Inverter Fault Detection in Grid-Connected Solar Photovoltaic Systems
by Shahid Tufail and Arif I. Sarwat
Electronics 2025, 14(14), 2916; https://doi.org/10.3390/electronics14142916 - 21 Jul 2025
Viewed by 271
Abstract
The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection [...] Read more.
The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection presents interesting prospects in accuracy and responsiveness. By streamlining data complexity and allowing faster and more effective fault diagnosis, dimensionality reduction methods play vital role. Using dimensionality reduction and ML techniques, this work explores inverter fault detection in GCPV systems. Photovoltaic inverter operational data was normalized and preprocessed. In the next step, dimensionality reduction using Principal Component Analysis (PCA) and autoencoder-based feature extraction were explored. For ML training four classifiers which include Random Forest (RF), logistic regression (LR), decision tree (DT), and K-Nearest Neighbors (KNN) were used. Trained on the whole standardized dataset, the RF model routinely produced the greatest accuracy of 99.87%, so efficiently capturing complicated feature interactions but requiring large processing resources and time of 36.47sec. LR model showed reduction in accuracy, but very fast training time compared to other models. Further, PCA greatly lowered computing demands, especially improving inference speed for LR and KNN. High accuracy of 99.23% across all models was maintained by autoencoder-derived features. Full article
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