Search Results (68)

Utilizing the considerable cold energy in liquefied natural gas (LNG) through the organic Rankine cycle is a highly important initiative. A multi-stage Rankine-based power generation system using LNG cold energy for waste heat utilization was proposed in this study. Moreover, a comprehensive assessment method was used to select the working fluid for this proposed system. Not only were thermodynamic and economic indicators considered, but also the environmental impact of candidate working fluids was taken into account in the evaluation process. The optimal operating points of the system were determined using non-dominated sorting genetic algorithm II and TOPSIS methods, while employing Gray Relational Analysis was applied to compute the gray relational coefficients of candidate working fluids at the optimal operating points. In addition, four weighting methods were used to calculate the final gray correlation degree of the candidate working fluids by considering the weighting influence. The stability of the calculated gray correlation degree was observed by performing a standard deviation analysis. The results indicate that R245ca was chosen as the optimal working fluid due to its superior performance based on the entropy weighting method, the independent weighting coefficient method, and the mean weighting method. Simultaneously, R245ca exhibits the best specific net power output and levelized cost of energy values of 0.283 USD/kWh and 106.9 kWh/t, respectively, among all candidate working fluids. The gray correlation degree of R1233zd(E) is 0.948, exceeding that of R245ca under the coefficient of variation method. The gray correlation degree under the mean value method is the most stable, with a standard deviation of only 0.162, while the gray correlation degree under the coefficient of variation method exhibits the greatest fluctuation, with a standard deviation of 0.17, in the stability assessment. Full article

As the energy sector transitions toward decarbonisation, low-to-intermediate temperature geothermal resources in sedimentary basins—particularly repurposed oil and gas fields—have emerged as promising candidates for sustainable heat and power generation. Despite their widespread availability, the development of these systems is hindered by gaps in methodology, oversimplified modelling assumptions, and a lack of integrated analyses accounting for long-term reservoir and wellbore dynamics. This study presents a detailed, simulation-based framework to evaluate geothermal energy extraction from depleted petroleum reservoirs, with a focus on low-enthalpy resources (<150 °C). By examining coupling reservoir behaviour, wellbore heat loss, reinjection cooling, and surface energy conversion, the framework provides dynamic insights into system sustainability and net energy output. Through a series of parametric analyses—including production rate, doublet spacing, reservoir temperature, and field configuration—key performance indicators such as gross power, pumping requirements, and thermal breakthrough are quantified. The findings reveal that: (1) net energy output is maximised at optimal flow rate (~70 kg/s for a 90 °C reservoir), beyond which increased pumping offsets thermal gains; (2) doublet spacing has a non-linear impact on reinjection cooling, with larger distances reducing thermal interference and pumping energy; (3) reservoirs with higher temperatures (<120°C) offer significantly better thermodynamic and hydraulic performance, enabling pump-free or low-duty operations at higher flow rates; and (4) wellbore thermal losses and reinjection effects are critical in determining long-term viability, especially in low-permeability or shallow fields. This work demonstrates the importance of a coupled, site-specific modelling in assessing the geothermal viability of petroleum fields and provides a foundation for future techno-economic and sustainability assessments. The results inform optimal design strategies and highlight scenarios where the geothermal development of oil and gas fields can be both technically and energetically viable. Full article

Solar panels are critical for renewable electricity generation, yet defects significantly reduce power output and risk grid instability, necessitating reliable AI-driven defect detection. We propose the SPHERE (Solar Panel Hidden-Defect Evaluation for Renewable Energy) method for such cases. This study compares deep learning models for classifying solar panel images (broken, clean, and dirty) using a novel, proprietary dataset of 6079 images augmented to enhance performance. The following three models were evaluated: YOLOv8-m, YOLOv9-e, and a custom CNN with 9-fold cross-validation. Pre-trained models (e.g., VGG16 and ResNet) were assessed but outperformed by YOLO variants. Metrics included accuracy, precision–recall, F1-score, sensitivity, and specificity. YOLOv8-m achieved the highest accuracy (97.26%) and specificity (95.94%) with 100% sensitivity, excelling in defect identification. YOLOv9-e showed slightly lower accuracy (95.18%) but maintained high sensitivity. The CNN model demonstrated robust generalization (92.86% accuracy) via cross-validation, though it underperformed relative to YOLO architectures. Results highlight YOLO-based models’ superiority, particularly YOLOv8-m, in balancing precision and robustness for this classification task. This study underscores the potential of YOLO frameworks in automated solar panel inspection systems, offering enhanced maintenance and grid stability reliability. This contributes to advancing AI applications in renewable energy infrastructure, ensuring efficient defect detection and sustained power output. The dataset’s novelty and the models’ comparative analysis provide a foundation for future research in autonomous maintenance solutions. Full article

A stable electricity supply is a key factor for sustainable development in Vietnam, a rapidly growing developing country with increasing energy consumption. This article delves quantitatively into the economic effects and benefits arising from the construction of a 1.5 GW capacity natural gas-fired power plant (NGPP). Input–output analysis was applied to identifying the economic effects. Specifically, production-inducing effects and value-added creation effects were analyzed separately for the construction and operation of the NGPP. Based on the economic theory, the economic benefits were computed as the sum of the electricity price and consumer surplus resulting from electricity consumption. During the construction period of the NGPP, it is expected to induce USD 2315.60 million of production and USD 414.75 million of value-added for the Vietnamese economy. The production-inducing effects, value-added creation effects, and economic benefits ensuing from the operation of the NGPP in 2030 were estimated to be USD 833.36 million, USD 235.75 million, and USD 1164.33 million, respectively. The cost–benefit analysis revealed a benefit-to-cost ratio of 1.45, which is higher than 1, indicating the economic feasibility of the construction. Therefore, the construction of the NGPP can be implemented with social net benefits. Full article

The total installed geothermal power plant capacity at year-end 2023 was 16,335 MW, while the forecast for the installed capacity in 2025 is 19,331 MW. In Croatia, several medium-temperature geothermal resources (geothermal water) with temperatures from 90 to 200 °C exist, by means of which it is possible to produce electricity in binary plants, with the Organic Rankine Cycle (ORC) or with the Kalina cycle. In earlier studies, the authors presented the results of an energy-exergy analysis of geothermal sources at Velika Ciglena (170 °C), Lunjkovec-Kutnjak (140 °C), Babina Greda (125 °C), and Rečica (120 °C), aiming to determine which binary plant is more suitable for the environmental conditions in Croatia. The calculations indicate that the plant with ORC is thermodynamically superior to the one with the Kalina cycle for all geothermal sources. Taking into account the typical challenges faced by new technologies during their initial implementation, the authors recommend using the ORC plant for all medium-temperature geothermal sources. Literature on ORC applications mainly addresses working fluid selection, unit and plant optimization, and modifications to enhance thermodynamic efficiency or net power output. While many studies on working fluid selection exist, each geothermal source is unique due to its specific temperature and local cooling fluid (water or air). As a result, this paper presents the findings of an analysis on the influence of working fluids on the thermodynamic performance of an ORC system, focusing on the Lunjkovec-Kutnjak Geothermal Power Plant with a geothermal water temperature of 140 °C. As the working fluid, the next are analyzed: isopentane (C₅H₁₂), isobutene (C₄H₁₀), isohexane (C₆H₁₄), R114 (C₂Cl₂F₄), R141B (C₂H₃Cl₂F), and R142B (C₂H₃Cl₂F₂). In respect to cycle efficiency and net power, all working fluids are equally favorable, but R601a (isopentane) with low ALT, ODP, and GWP, favorable upper and lower pressure, is the most suitable fluid for ORC with a medium-temperature geothermal source. Full article

Irrigation plays a crucial role in enhancing food production, increasing land productivity, and improving the livelihoods of smallholder farmers in Sub-Saharan Africa (SSA). Solar pumps and water harvesting ponds have emerged as promising technologies for sustainable agriculture for smallholders in SSA and beyond. The socio-economic impacts of these systems are less studied in the existing literature. This study examined the agricultural productivity of solar pump and water harvesting irrigation technologies and their impacts on income and food security among smallholder farmers in the Central Rift Valley, Lake Hawassa, and Upper Awash sub-basin areas in Ethiopia. Data were collected from 161 farming households that were selected randomly from woredas where solar pump and water harvesting pond irrigation systems had been implemented. The sample size was determined using the power calculation method. Bio-physical observation and measurements were also conducted at field levels. The benefit–cost ratio (BCR) and net water value (NWV) from the use of solar pump and water harvesting pond irrigations were analyzed to assess the viability of these systems. The household food consumption score (HFCS) and household dietary diversity score (HDDS) were calculated to measure food security, while the revenue from crop production was used to measure crop income. An endogenous switching regression model was applied to address the endogeneity nature of the adoption of the irrigation technologies. The counterfactual analysis, specifically the Average Treatment Effect on the Treated (ATT), was used to evaluate the impacts of the irrigation technologies on income and food security. Results indicate that the ATT of crop income, HFCS, and HDDS are positive and statistically significant, illustrating the role of these irrigation systems in enhancing smallholder farmers’ welfare. Moreover, smallholder farmers’ solar pump irrigation systems were found to be economically viable for few crops, with a BCR greater than 1.0 and an NWV ranging from 0.21 to 1.53 USD/m³. It was also found that bundling agricultural technologies with solar pump irrigation systems leads to enhanced agricultural outputs and welfare. The sustainable adoption and scale-up of these irrigation systems demand addressing technical and financial constraints, as well as input and output market challenges. Full article

Recently, convolutional neural networks (CNNs) have received a massive amount of interest due to their ability to achieve high accuracy in various artificial intelligence tasks. With the development of complex CNN models, a significant drawback is their high computational burden and memory requirements. The performance of a typical CNN model can be enhanced by the improvement of hardware accelerators. Practical implementations on field-programmable gate arrays (FPGA) have the potential to reduce resource utilization while maintaining low power consumption. Nevertheless, when implementing complex CNN models on FPGAs, these may may require further computational and memory capacities, exceeding the available capacity provided by many current FPGAs. An effective solution to this issue is to use quantized neural network (QNN) models to remove the burden of full-precision weights and activations. This article proposes an accelerator design framework for FPGAs, called FPGA-QNN, with a particular value in reducing high computational burden and memory requirements when implementing CNNs. To approach this goal, FPGA-QNN exploits the basics of quantized neural network (QNN) models by converting the high burden of full-precision weights and activations into integer operations. The FPGA-QNN framework comes up with 12 accelerators based on multi-layer perceptron (MLP) and LeNet CNN models, each of which is associated with a specific combination of quantization and folding. The outputs from the performance evaluations on Xilinx PYNQ Z1 development board proved the superiority of FPGA-QNN in terms of resource utilization and energy efficiency in comparison to several recent approaches. The proposed MLP model classified the FashionMNIST dataset at a speed of 953 kFPS with 1019 GOPs while consuming 2.05 W. Full article

This research study addresses the challenges of extended charging times and limited ranges in electric vehicles by conducting a techno-economic analysis of integrating renewable energy technologies—solar modules, wind turbines, and piezoelectric materials—into double-decker electric buses in London, UK. Consequently, the empirical study evaluates the power output of renewable energy technologies through simulation modelling based on vehicle specifications and energy requirements, which is followed by numerical modelling to assess economic viability. Furthermore, CFD (computational fluid dynamics) modelling is undertaken to examine the performance levels of vehicular-mounted wind turbines. The solar modules are placed on the rooftop and sides of the bus, generating 15.9 kWh/day, and the wind turbine in the front bumper of the bus generates 8.3 kWh/day. However, the piezoelectric material generated only 22.6 Wh/day, thereby rendering this technology impractical for further analysis. Therefore, both the solar modules and wind turbines combined generate 24.2 kWh/day, which can increase the driving range by 16.3 km per day, resulting in savings of 19.36 min for charging at the stations. Investing in such projects would have a positive return as the internal rate of return (IRR) and net present value (NPV) are 2.8% and £11,175, respectively. The annual revenue would be £6712, and the greenhouse gas (GHG) reduction would be two metric tons annually. Electricity generation, the electricity generation rate, and the initial investment are identified as key factors influencing power outages in a sensitivity analysis. In conclusion, this numerical modelling study paves the way for experimental validation toward the implementation of renewable energy technologies on electric bus fleets. Full article

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model’s overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models. Full article

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints. Full article

The thermoeconomic performance of geothermal power plants is influenced by a variety of site-specific factors, major economic variables, and the type of the involved technology. In addition to those, ambient conditions also play a role in geothermal power generation by acting on the cooling towers. This study focuses on the performance analysis of a binary cycle with isobutane for geothermal power generation under the impact of climate change. Long-term temperature variations in ambient air are described by temperature anomalies under two shared socioeconomic pathways (SSP). These are the intermediate SSP2-4.5 scenario and the extreme SSP5-8.5 scenario, over the period from 2021 to 2100. Different climate models from the most recent Climate Model Intercomparison Project (CMIP6) are compared against each other and against the observed temperature data. The predictive power of the CMIP6 climate models is evaluated using the root mean square error (RMSE) and the Kullback–Leibler (KL) criteria. The thermoeconomic performance of the geothermal power plant is expressed in terms of net power output, annual electricity generation (AEG), and levelized cost of electricity (LCOE). The geothermal power plant achieves a net power output of 10 MW and an LCOE of 79.2 USD/MWh for an ambient air temperature of 12 °C. This temperature is the average temperature over the reference period of 1991–2020 in Bjelovar, Croatia (45.8988° N, 16.8423° E). Under the impact of climate change, the same geothermal power plant will have the AEG reduced by between 0.5% and 2.9% in the intermediate (SSP2-4.5) scenario and by between 2.0% and 8.7% in the extreme (SSP5-8.5) scenario. The LCOE will increase between 0.4% and 1.8% in the intermediate scenario and from 1.3% to 5.6% in the extreme scenario. Full article

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery. Full article

As new energy integration increases, power grid load curves become steeper. Large logistics parks, with their substantial cooling load, show great peak shaving potential. Leveraging this load while maintaining staff comfort, product quality, and operational costs is a major challenge. This paper proposes a two-stage robust optimization method for large logistics parks to participate in grid peak shaving. First, a Cooling Load’s Economic Contribution (CLEC) index is introduced, integrating the Predicted Mean Vote (PMV) and Sales Pressure Index (SPI). Then, an optimization model is established, accounting for renewable energy uncertainties and maximizing large logistics parks’ participation in peak shaving. Results illustrate that the proposed method leads to a reduction in the peak shaving pressure on the distribution network. Specifically, under the scenario tolerating the maximum potential uncertainty in renewable energy output, the absolute peak-to-valley difference and fluctuation variance of the park’s net load are decreased by 45.82% and 54.59%, respectively. Furthermore, the PMV and the SPI indexes are reduced by 39.12% and 26.36%, respectively. In comparison with the determined optimization method, despite a slight cost increase of 20.06%, the proposed method significantly reduces EDR load shedding by 98.1%. Full article

Carbon dioxide (CO₂) has been recognized as one of the potential working fluids to operate power generation cycles, either in supercritical or transcritical configuration. However, a small concentration of some of the additives to CO₂ have shown promising improvements in the overall performance of the cycle. The current study is motivated by the newly proposed additive silicon tetrachloride (SiCl₄), and so we perform a detailed investigation of SiCl₄ along with a few well-known additives to CO₂-based binary mixtures as a working fluid in transcritical organic Rankine cycle setup with internal heat regeneration. The additives selected for the study are pentane, cyclopentane, cyclohexane, and silicon tetrachloride (SiCl₄). A comprehensive study on the energy and exergy performance of the cycle for warm regions is conducted at a turbine inlet temperature of 250 °C. The performance of the heat recovery unit is also assessed to highlight its importance in comparison to a simple configuration of the cycle. This study shows that the cycle operating with binary mixtures performs significantly better than with pure CO₂, which is mainly due to its better heat recovery in the heat recovery unit. The results show that the optimal molar concentration of the additives is in between 20% and 25%. Besides having better thermal stability, SiCl₄ shows an improvement in the cycle thermal efficiency by 6% points which is comparable to cyclopentane (7.3% points) and cyclohexane (7.8% points). The optimal cycle pressure ratio for SiCl₄ is also relatively lower than for other additives. The energy efficiency of the cycle with pure CO₂ is around 45% which is also increased to 58%, 63%, 64%, 60% with pentane, cyclopentane, cyclohexane, and SiCl₄, respectively. These results suggest that additives like SiCl₄ could make CO₂-based cycles more viable for power generation in warm regions. Full article

The interest in combined heat and solar power (CHP) systems has increased due to the growing demand for sustainable energy with low carbon emissions. An effective technical solution to address this requirement is using a parabolic trough solar collector (PTC) in conjunction with a Rankine cycle (RC) heat engine. The solar-powered Rankine cycle (SPRC) system is a renewable energy technology that can be relied upon for its high efficiency and produces clean energy output. This study describes developing a SPRC system specifically for electricity generation in Aden, Yemen. The system comprises parabolic trough collectors, a thermal storage tank, and a Rankine cycle. A 4E analysis of this system was theoretically investigated, and the effects of various design conditions, namely the boiler’s pinch point temperature and steam extraction from the high-pressure turbine, steam extraction from the intermediate-pressure turbine, and condenser temperature, were studied. Numerical simulations showed that the system produces a 50 MW net. The system’s exergetic and energy efficiencies are 30.7% and 32.4%. The planned system costs 2509 USD/h, the exergoeconomic factor is 79.43%, and the system’s energy cost is 50.19 USD/MWh. The system has a 22.47 kg/MWh environmental carbon footprint. It is also observed that the performance of the cycle is greatly influenced by climatic circumstances. Raising the boiler’s pinch point temperature decreases the system’s performance and raises the environmental impact. Full article