Search Results (7,280)

Project management often overlooks the consideration of long-term effects that may impact sustainability transition and innovation. This paper addresses this gap by presenting an analysis that extends the traditional project life cycle model through the incorporation of a new phase, labelled “system impact”, which integrates innovation and sustainability into project management using a system dynamics methodology. To explore this extension, a simulation model is developed to analyse a solar photovoltaic (PV) power project, providing valuable insights into the systemic and dynamic impacts required for successful project outcomes, including effective benefits management and value creation. The results provide a sustainability-focused assessment of project success. Process innovation efficiency reaches its peak at 140 completed tasks, shortening the project duration from 18 to 13.25 months. This study highlights CO₂ emission avoidance over 25 years compared to fossil fuel generators. Economically, despite an initial cost three times higher, the solar PV alternative proves more cost-effective in the long run, amounting to only 19% of the total cost of the fossil fuel option. Full article

Large-scale penetration of renewable energy generation brings various challenges to the power system in terms of safety, reliability, economy and flexibility. The development of large-scale, high-security energy-storage technology can effectively address these challenges and improve the capabilities of power systems in power-supply guarantee and flexible adjustment. This paper proposes a novel distributed risk-averse optimization scheduling model of a hybrid wind–solar–storage system based on the adjustability of the storage system and the complementarity of renewable energy generation. The correlation of wind power and photovoltaic generation is quantified based on a Copula function. A risk-averse operation optimization model is proposed using conditional value at risk to quantify the uncertainty of renewable energy generation. A linear formulation of conditional value at risk under typical scenarios is developed by Gibbs sampling the joint distribution and Fuzzy C-Means clustering algorithm. A distributed solution algorithm based on an alternating-direction method of multipliers is developed to derive the optimal scheduling of hybrid wind–solar–storage system in a distributed manner. Numerical case studies based on IEEE 34-bus distribution network verify the effectiveness of the proposed model in reducing the uncertainty impact of renewable energy generation on an upstream grid (the overall amount of renewable energy generation sent back to the upstream grid has decreased about 80.6%) and ensuring the operational security of hybrid wind–solar–storage system (overall voltage deviation within 5.6%). Full article

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges. Full article

Sol-gel coatings are commonly used to prevent corrosion from molten salt mixtures in CSP plants. Until now, they have been driven primarily by cost considerations, without integrating environmental criteria into the modeling and decision-making process. The novelty of this study lies in the development of an evaluation framework that incorporates environmental impact alongside technical and economic factors, providing a more sustainable approach. This work assesses porosity, thermal shock resistance, and thickness to determine the optimal sol-gel coating. For this purpose, the multi-criteria decision-making technique “Analytic Hierarchy Process” (AHP) and the Life Cycle Assessment (LCA) methodology are implemented. The results show that the scores obtained for the 3YSZ-5A (5% mol) coating are higher than those of the 3YSZ (3% mol) and 3YSZ-20A (20% mol) coatings, between 1.52 and 1.69, respectively. The 3YSZ-5A coating (5% mol) is the optimal solution among all the systems analyzed, with a score of 0.61 AHP pt. The coating of the same type and higher molar concentration (20%) achieved 0.55 AHP pt. Finally, the 3YSZ type coating received the lowest rating, with a score of 0.36 AHP pt. The insights generated in this research will support decision-making in the design and maintenance of CSP plants. Full article

Semitransparent perovskite solar cells (PSCs) that possess a high-power conversion efficiency (PCE) and high average visible light transmittance (AVT) can be employed in applications such as photovoltaic windows. In this study, a bifacial modification comprising a buried layer of [4-(3,6-Dimethyl-9H-carbazol-9-yl) butyl] phosphonic acid (Me-4PACz) and a surface passivator of 2-(2-Thienyl) ethylamine hydroiodide (2-TEAI) was proposed to enhance device performance. When the concentrations of Me-4PACz and 2-TEAI were 0.3 mg/mL and 3 mg/mL, opaque PSCs with a 1.57 eV perovskite absorber achieved a PCE of 22.62% (with a V_OC of 1.18 V) and retained 88% of their original value after being stored in air for 1000 h. By substituting a metal electrode with an indium zinc oxide electrode, the resulting semitransparent PSCs showed a PCE of over 20% and an AVT of 9.45%. It was, therefore, suggested that the synergistic effect of Me-4PACz and 2-TEAI improved the crystal quality of perovskites and the carrier transport in devices. When employing an absorber with a wider bandgap (1.67 eV), the corresponding PSC obtained a higher AVT of 20.71% and maintained a PCE of 18.73%; these values show that a superior overall performance is observed compared to that in similar studies. This work is conductive to the future application of semitransparent PSCs. Full article

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT) controllers to optimize the efficiency of PV systems by extracting accessible maximum power. This research investigates the performance and comparison of various MPPT control algorithms for a standalone PV system. Several cases involving individual MPPT controllers, as well as hybrid combinations using two and three controllers, have been simulated in MATLAB/SIMULINK. The sensed parameters, i.e., output power, voltage, and current, specify that though individual controllers effectively track the maximum power point, hybrid controllers achieve superior performance by utilizing the combined strengths of each algorithm. The results indicate that individual MPPT controllers, such as perturb and observe (P&O), particle swarm optimization (PSO), and fuzzy logic (FL), achieved tracking efficiencies of 97.6%, 90.3%, and 90.1%, respectively. In contrast, hybrid dual controllers such as P&O-PSO, PSO-FL, and P&O-FL demonstrated improved performance, with tracking efficiencies of 96.8%, 96.4%, and 96.5%, respectively. This research also proposes a new hybrid triple-MPPT controller combining P&O-PSO-FL, which surpassed both individual and dual-hybrid controllers, achieving an impressive efficiency of 99.5%. Finally, a comparison of all seven cases of MPPT control algorithms is presented, highlighting the advantages and disadvantages of individual as well as hybrid approaches. Full article

Solar thermal concentrating solar power (CSP) plants have attracted growing interest in the field of renewable energy generation due to their capability for large-scale electricity generation, high photoelectric conversion efficiency, and enhanced reliability and flexibility. Meanwhile, driven by the rapid advancement of power electronics technology, extensive wind farms (WFs) and large-scale battery energy storage systems (BESSs) are being increasingly integrated into the power grid. From these points of view, grid-connected CSP–BESS–wind hybrid energy systems are expected to emerge in the future. Currently, most studies focus solely on the stability of renewable energy generation systems connected to the grid via power converters. In fact, within CSP–BESS–wind hybrid energy systems, interactions between the CSP, collection grid, and the converter controllers can also arise, potentially triggering system oscillations. To fill this gap, this paper investigated the interaction mechanism and oscillation characteristics of a grid-connected CSP–BESS–wind hybrid energy system. Firstly, by considering the dynamics of CSP, BESSs, and wind turbines, a comprehensive model of a grid-connected CSP–BESS–wind hybrid energy system was developed. With this model, the Nyquist stability criterion was utilized to analyze the potential interaction mechanism within the hybrid system. Subsequently, the oscillation characteristics were examined in detail, providing insights to inform the design of the damping controller. Finally, the analytical results were validated through MATLAB/Simulink simulations. Full article

This paper presents an analysis of the flight endurance of solar-powered unmanned aerial vehicles (UAVs). Flight endurance is usually only analyzed under the operating conditions for the location where the UAV was constructed. The fact that these conditions change in a different environment of its operation has been missed. This can be disastrous for those looking to operate such a system under different geographical conditions. This work provides critical insights into the design and operation of solar-powered UAVs for various latitudes, highlighting strategies to maximize their performance and energy efficiency. This work analyzes the endurance of small UAVs designed for practical applications such as shoreline monitoring, agricultural pest detection, and search and rescue operations. The study uses TRNSYS 18 software to employ solar radiation in the power system performance at different latitudes. The results show that flight endurance is highly dependent on solar irradiance. This study confirms that the differences between low latitudes in summer and high latitudes in winter are significant, and this parameter cannot be ignored in terms of planning the use of such vehicles. The findings emphasize the importance of optimizing the balance between UAV mass, solar energy harvesting, and endurance. While the addition of battery mass can enhance endurance, the structural reinforcements required for increased weight may impose practical limitations. The scientific contribution of this work may be useful for both future designers and stakeholders in the operation of such unmanned systems. Full article

To address the challenges of cross-city travel for different types of electric vehicles (EV) and to tackle the issue of rapid charging in regions with weak power grids, this paper presents a strategic approach for locating and sizing highway charging stations tailored to such grid limitations. Initially, considering the initial EV state of charge, a path-demand-based model for EV charging station location–allocation is proposed to optimize station numbers and enhance vehicle flow, which indicates the passing rate of vehicles. Subsequently, a capacity configuration model is formulated, integrating wind, photovoltaic, storage, and diesel generators to manage the stations’ load. This model introduces a new objective function, the annual comprehensive cost, encompassing installation, operation, maintenance, wind and solar curtailment, and diesel generation costs. Simulation examples on north-western cross-city highways validate the efficacy of this approach, showing that the proposed wind–solar storage fast-charging station site selection and capacity optimization model can effectively cater to diverse electric vehicle charging demands. Moreover, it achieves a 90% self-consistency rate during operation across various typical daily scenarios, ensuring a secure and economically viable operational performance. Full article

Interface-passivated graphene/silicon Schottky junction solar cells have demonstrated promising features with improved stability and power conversion efficiency (PCE). However, there are some misunderstandings in the literature regarding some of the working mechanisms and the impacts of the silicon/insulator interface. Specifically, attributing performance improvement to oxygen vacancies and characterizing performance using Schottky barrier height and ideality factor might not be the most accurate or appropriate. This work uses Al₂O₃ as an example to provide a detailed discussion on the interface ALD growth of Al₂O₃ on silicon and its impact on graphene electrode metal–insulator–semiconductor (MIS) solar cells. We further suggest that the current conduction in MIS solar cells with an insulating layer of 2 to 3 nm thickness is better described by direct tunneling, Poole–Frenkel emission, and Fowler–Nordheim tunneling, as the junction voltage sweeps from negative to a larger forward bias. The dielectric film thickness, its band offset with Si, and the interface roughness, are key factors to consider for process optimization. Full article

The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization framework to design an HRES in Kern County, USA. The proposed system integrates wind turbines (WTS), photovoltaic (PV) panels, Biomass Gasifiers (BMGs), batteries, electrolyzers (ELs), and fuel cells (FCs), aiming to minimize Annual System Cost (ASC), minimize Loss of Power Supply Probability (LPSP), and maximize renewable energy fraction (REF). Results demonstrate that the PSO-optimized system achieves an ASC of USD6,336,303, an LPSP of 0.01%, and a REF of 90.01%, all of which are reached after 25 iterations. When compared to the Genetic Algorithm (GA) and hybrid GA-PSO, PSO improved cost-effectiveness by 3.4% over GA and reduced ASC by 1.09% compared to GAPSO. In terms of REF, PSO outperformed GA by 1.22% and GAPSO by 0.99%. The PSO-optimized configuration includes WT (4669 kW), solar PV (10,623 kW), BMG (2174 kW), battery (8000 kWh), FC (2305 kW), and EL (6806 kW). Sensitivity analysis highlights the flexibility of the optimization framework under varying weight distributions. These results highlight the dependability, cost-effectiveness, and sustainability for the proposed system, offering valuable insights for policymakers and practitioners transitioning to renewable energy systems. Full article

The global energy landscape is experiencing growing challenges, with energy crises in regions such as South Africa underscoring the drive to accelerate the shift toward renewable energy solutions. This paper presents an approach for improving solar energy planning, specifically focusing on leveraging the capabilities of the ATlite software in conjunction with custom data. Using mathematical models, ATlite (which was initially developed by the Renewable Energy Group at the Frankfurt Institute for Advances Studies) is a Python software package that converts historical weather data into power generation potentials and time series for renewable energy technologies such as solar photovoltaic (PV) panels and wind turbines. The software efficiently combines atmospheric and terrain data from large regions using user-defined weights based on land use or energy yield. In this study, European Centre for Medium-Range Weather Forecasts reanalysis data (ERA5) data was modified using Kriging to enhance the resolution of each data field. This refined data was applied in ATlite, instead of utilizing the standard built-in data download and processing tools, to generate solar capacity factor maps and solar generation time series. This was utilized to identify specific PV technologies as well as optimal sites for solar power. Thereafter, a simulated power generation time series was compared with measured solar generation data, resulting in a root mean square error (RMSE) of 19.6 kW for a 250 kWp installation. This approach’s flexibility and versatility in the inclusion of custom data, led to the conclusion that it could be a suitable option for renewable energy planning and decision making in South Africa and globally, providing value to solar installers and planners. Full article

The large-scale integration of wind, solar, and battery energy storage is a key feature of the new power system based on renewable energy sources. The optimization results of wind turbine (WT)–photovoltaic (PV)–battery energy storage (BES) hybrid energy systems (HESs) can influence the economic performance and stability of the electric power system (EPS). However, most existing studies have overlooked the effect of power electronic converter (PEC) efficiency on capacity configuration optimization, leading to a significant difference between theoretical optimal and actual results. This paper introduces an accurate efficiency model applicable to different types of PECs, and establishes an enhanced mathematical model along with constraint conditions for WT–PV–BES–grid–load systems, based on precise converter efficiency models. In two typical application scenarios, the capacity configurations of WT–PV–BES are optimized with optimal cost as the objective function. The different configuration results among ignoring PEC loss, using fixed PEC efficiency models, and using accurate PEC efficiency models are compared. The results show that in the DC system, the total efficiency of the system with the precise converter efficiency model is approximately 96.63%, and the cost increases by CNY 49,420, about 8.56%, compared to the system with 100% efficiency. In the AC system, the total efficiency with the precise converter efficiency model is approximately 97.64%, and the cost increases by CNY 4517, about 2.02%, compared to the system with 100% efficiency. The analysis clearly reveals that the lack of an accurate efficiency model for PECs will greatly affect the precision and effectiveness of configuration optimization. Full article

The purpose of this study article is to provide a detailed examination of the performance of exergy electric panels, exergy efficiency panels and exergy solar panels under the climatic circumstances of the Utrecht region in the Netherlands. The study explores the performance of these solar panels in terms of both their energy efficiency and their exergy efficiency. Additionally, the study investigates critical factors such as solar radiation, module internal temperature, air temperature, maximum power, and solar energy efficiency. Environmental factors have a considerable impact on panel performance; temperature has a negative impact on efficiency, whereas an increase in solar radiation leads to an increase in energy and exergy output. These findings offer significant insights that can be used to increase the utilization of solar energy in locations that have a temperate oceanic climate, particularly in the context of the climatic conditions of the Utrecht region. The usefulness of the linear regression model in machine learning was validated by performance measures such as R², RMSE, MAE, and MAPE. Furthermore, an R² value of 0.94889 was found for the parameters that were utilized. Policy makers, researchers, and industry stakeholders who seek to successfully utilize solar energy in the face of changing climatic conditions may find this research to be an important reference. Full article

Smart grids have enormous potential in terms of reliability and sustainability, but with the large-scale integration of distributed energy like solar energy, the network security risks of smart grids have also increased. In response to the physical and information network threats faced in the network security risk assessment of solar powered smart grids, this study develops a smart grid theft detection model based on TimesNet and a smart grid intrusion detection model based on bidirectional long short-term memory networks. The results indicated that when the proportion of electricity theft data was 25%, the false detection rate of the proposed model was 3.52. The area under the curve of the proposed model was 0.98, and the detection rate, false negative rate, F1 value, and accuracy were 97.04%, 1.21%, 92.69%, and 97.15%, respectively. The loss value of the proposed intrusion detection model was stable at around 0.012 in the NSL-KDD dataset and around 0.02 in the CICIDS2017 dataset, with a detection accuracy of 97.54% and a false positive rate of 1.21%. The experiment demonstrated the electricity theft behavior and network intrusion detection performance of the proposed model, which can effectively detect security threats faced by solar smart grids and provide practical basis for network security risk assessment. The research results can help reduce the economic losses of power companies, maintain a good order of electricity consumption, and ensure the safe and stable operation of solar smart grids. Full article