Search Results (219)

This study presents a comprehensive numerical simulation and thermal performance analysis of a novel modular floating solar still system, featuring integrated heat-pipe vacuum tube collectors, designed for seawater desalination. This innovative system—subject of International Patent Application WO 2023/062261 A1—not only aims to enhance efficiency and scalability beyond traditional solar stills, but also addresses the significant environmental challenge of concentrated brine discharge inherent in conventional desalination methods. The study evolved from an initial theoretical model to a rigorous dynamic thermal model, validated using real hourly meteorological data from Málaga, Andalusia, Spain. This modelling approach was developed to quantify heat transfer mechanisms and accurately predict system performance. The refined hourly simulation forecasts an annual freshwater production of approximately 174 L per unit. Notably, a preliminary economic assessment estimates the Cost of Produced Water per Litre (CPL) at 0.7509 EUR/litre, establishing a valuable baseline for future optimisation. These findings underscore the critical importance of dynamic hourly simulations for realistic performance prediction and validate the technical and preliminary economic feasibility of this novel approach. The system’s projected output, modular floating design, and significant environmental advantages position it as a promising and sustainable solution for freshwater production, particularly in coastal regions and sensitive marine ecosystems. This work provides a solid foundation for future experimental validation, cost optimisation, and scalable implementation of renewable energy-driven desalination. Full article

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

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

This paper investigates the impact of the transition from manufacturing to tourism on sectoral productivity, output prices, and labor costs. Using panel data econometric models for 15 selected EU countries from 2011 to 2023, the study confirms key dynamics predicted by Baumol’s cost disease (BCD) hypothesis. The findings reveal that higher productivity is positively associated with both implied prices and hourly labor costs across sectors, supporting the wage equalization mechanism central to BCD. However, the relationship between productivity and wages or prices is weaker in labor-intensive sectors like tourism, underscoring their structural vulnerability to wage-driven cost pressures. Additionally, the analysis captures the impact of major external shocks, including the COVID-19 pandemic and the Russo–Ukrainian war, treated as jointly sourced super-shocks. The regression results indicate significant price disruptions following these shocks, whereas no statistically significant trend in labor costs was detected in the post-treatment period. These results highlight the differential effects of external shocks on wages versus prices, emphasizing the challenges faced by low-productivity, labor-intensive sectors in managing cost dynamics. The findings provide valuable insights for policymakers addressing sectoral imbalances in the context of BCD and navigating the economic consequences of global disruptions. Full article

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind). Full article

This article presents an empirical evaluation of the technical and economic performance of a building-integrated photovoltaic (PV) system implemented at the Faculty of Architecture and Urbanism of the University of Cuenca, Ecuador. This study explores both stages of deployment, beginning with a 7.7 kWp pilot system and later scaling to a full 75.6 kWp configuration. This hourly monitoring of power exchanges with utility was conducted over several months using high-resolution instrumentation and cloud-based analytics platforms. A detailed comparison between projected energy output, recorded production, and real energy consumption was carried out, revealing how seasonal variability, cloud cover, and academic schedules influence system behavior. The findings also include a comparison between billed and actual electricity prices, as well as an analysis of the system’s payback period under different cost scenarios, including state-subsidized and real-cost frameworks. The results confirm that energy exports are frequent during weekends and that daily generation often exceeds on-site demand on non-working days. Although the university benefits from low electricity tariffs, the system demonstrates financial feasibility when broader public cost structures are considered. This study highlights operational outcomes under real-use conditions and provides insights for scaling distributed generation in institutional settings, with particular relevance for Andean urban contexts with similar solar profiles and tariff structures. Full article

One of the methods for mitigating the duck curve phenomenon in photovoltaic (PV) energy systems is storing surplus energy in the form of hydrogen. However, there is a lack of studies focused on residential PV systems that assess the impact of hydrogen storage on the reduction of energy flow imbalance to and from the national grid. This study presents an analysis of hydrogen energy storage based on real-world data from a household PV installation. Using simulation methods grounded in actual electricity consumption and hourly PV production data, the research identified the storage requirements, including the required operating hours and the capacity of the hydrogen tank. The analysis was based on a 1 kW electrolyzer and a fuel cell, representing the smallest and most basic commercially available units, and included a sensitivity analysis. At the household level—represented by a single-family home with an annual energy consumption and PV production of approximately 4–5 MWh over a two-year period—hydrogen storage enabled the production of 49.8 kg and 44.6 kg of hydrogen in the first and second years, respectively. This corresponded to the use of 3303 kWh of PV-generated electricity and an increase in self-consumption from 30% to 64%. Hydrogen storage helped to smooth out peak energy flows from the PV system, decreasing the imbalance from 5.73 kWh to 4.42 kWh. However, while it greatly improves self-consumption, its capacity to mitigate power flow imbalance further is constrained; substantial improvements would necessitate a much larger electrolyzer proportional in size to the PV system’s output. Full article

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article

The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. Full article

Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispatched, curtailed, or stored. However, high fluctuations in EWP, often resulting from demand–supply imbalances typically caused by sudden surges in electricity usage and the intermittency of renewable energy generation, and unforeseen external events, pose a challenge for accurate forecasting. Incorporating local temporal information (LTI) in time series, such as hourly price changes, is essential for accurate EWP forecasting, as it helps detect rapid market shifts. However, existing methods remain limited in capturing LTI, either relying on point-wise input sequences or, for fixed-length, non-overlapping segmentation methods, failing to effectively model dependencies within and across segments. This paper proposes the Local-Temporal Convolutional Transformer (LT-Conformer) model for day-ahead EWP forecasting, which addresses the challenge of capturing fine-grained LTI using Local-Temporal 1D Convolution and incorporates two attention modules to capture global temporal dependencies (e.g., daily price trends) and cross-feature dependencies (e.g., solar output influencing price). An initial evaluation in the Australian market demonstrates that LT-Conformer outperforms existing state-of-the-art methods and exhibits adaptability in forecasting EWP under volatile market conditions. Full article

For energy management systems, it is crucial to determine, in advance, the available energy from renewable sources to be dispatched in the next hours or days, in order to meet their generation and consumption goals. Predicting the photovoltaic power output strongly depends on accurate weather forecasting data and properly photovoltaic panel models. In this context, several traditional thermal models are expected to become obsolete due to the removal of the widely used Nominal Module Operating Temperature parameter, stated in the IEC 61215-2:2021 standard, according to reports of longer time periods in test data processing. The main contribution of the photovoltaic power estimation algorithm developed in this paper is the integration of an accurate procedure to calculate the hourly day-ahead power output of a photovoltaic plant, based on three simplified thermal models in steady state. These models are proposed and evaluated as remedial alternatives to the removal of the Nominal Module Operating Temperature parameter—a subject that has not been widely addressed in the related literature. The proposed estimation algorithm converts specific Numerical Weather Prediction data and solar module specifications into photovoltaic power output, which can be used in energy management applications to provide economic and ecological benefits. This approach focuses on rooftop-mounted mono-crystalline silicon photovoltaic panel arrays and incorporates a nonlinear translation of Standard Test Conditions parameters to real operating conditions. All necessary input data are provided for the analysis, and the accuracy of experimental results is validated using appropriate error metrics. Full article

The reliance on fossil fuels poses significant challenges to the environment and sustainable development. To address the heating requirements of the pyrolysis process in a biomass gasification-based multi-generation system, this study explored the use of low-grade solar energy across the full solar spectrum to supply the necessary energy for biomass pyrolysis while leveraging high-grade solar energy in the short-wavelength spectrum for power generation. The proposed multi-generation system integrates the full solar spectrum, biomass gasification, gas turbine, and waste heat recovery unit to produce power, cooling, and heating. A detailed thermodynamic model of this integrated system was developed, and the energy and exergy efficiencies of each subsystem were evaluated. Furthermore, the system’s performance was assessed on both monthly and annual timescales by employing the hourly weather data for Hohhot in 2023. The results showed that the solar subsystem achieved its highest power output of around 2.5 MWh in July and the lowest of 0.7 MWh in December. The annual electrical output peaked at 10 MWh, occurring around noon in July and August, while the winter peak was typically 2–3 MWh. For the wind power subsystem, the power output was maximized in April at 5.17 MWh and minimized in August at 0.7 MWh. Additionally, considering the overall multi-generation system performance, the highest power output of 14.9 MWh was observed in April, with lower outputs of 10.9, 11.3, and 11.4 MWh from August to October, respectively. Overall, the system demonstrated impressive annual average energy and exergy efficiencies of 74.05% and 52.13%, respectively. Full article

The critical consequence of climate change resulting from global warming is the increase in temperature. In combined cycle power plants (CCPPs), the Electric Power Output (PE) is affected by changes in both Ambient Temperature (AT) and Sea Surface Temperature (SST), particularly in plants utilizing seawater cooling systems. As AT increases, air density decreases, leading to a reduction in the mass of air absorbed by the gas turbine. This change alters the fuel–air mixture in the combustion chamber, resulting in decreased turbine power. Similarly, as SST increases, cooling efficiency declines, causing a loss of vacuum in the condenser. A lower vacuum reduces the steam expansion ratio, thereby decreasing the Steam Turbine Power Output. In this study, the effects of increases in these two parameters (AT and SST) due to global warming on the PE of CCPPs are investigated using various regression analysis techniques, Artificial Neural Networks (ANNs) and a hybrid model. The target variables are condenser vacuum (V), Steam Turbine Power Output (ST Power Output), and PE. The relationship of V with three input variables—SST, AT, and ST Power Output—was examined. ST Power Output was analyzed with four input variables: V, SST, AT, and relative humidity (RH). PE was analyzed with five input variables: V, SST, AT, RH, and atmospheric pressure (AP) using regression methods on an hourly basis. These models were compared based on the Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The best results for V, ST Power Output, and PE were obtained using the hybrid (LightGBM + DNN) model, with MAE values of 0.00051, 1.0490, and 2.1942, respectively. As a result, a 1 °C increase in AT leads to a decrease of 4.04681 MWh in the total electricity production of the plant. Furthermore, it was determined that a 1 °C increase in SST leads to a vacuum loss of up to 0.001836 bar_a. Due to this vacuum loss, the steam turbine experiences a power loss of 0.6426 MWh. Considering other associated losses (such as generator efficiency loss due to cooling), the decreases in ST Power Output and PE are calculated as 0.7269 MWh and 0.7642 MWh, respectively. Consequently, the combined effect of a 1 °C increase in both AT and SST results in a 4.8110 MWh production loss in the CCPP. As a result of a 1 °C increase in both AT and SST due to global warming, if the lost energy is to be compensated by an average-efficiency natural gas power plant, an imported coal power plant, or a lignite power plant, then an additional 610 tCO₂e, 11,184 tCO₂e, and 19,913 tCO₂e of greenhouse gases, respectively, would be released into the atmosphere. Full article

The optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) principle, implemented and validated on the integrated energy experimental platform. The experimental system simulates load generation and dissipation processes using a water tank, where hourly varying heating power output emulates the dynamic cooling loads of buildings. By regulating the chilled water system through different algorithms, the temperature tracking control performance and cooling supply regulation accuracy were rigorously validated. The control module was written in the Python 3.8 environment, and Niagara 4 software was used as an intermediate software to achieve data interaction and logical control with the laboratory system. The experimental results show that this algorithm can follow the hourly optimized parameters with a low overshoot in the short-term domain. Meanwhile, it can achieve the optimal control of cooling capacity and energy consumption in the long-term domain. Compared with the PID strategy, the temperature following control accuracy can be improved by 9.64%, and the cooling capacity can be saved by 6.24%. Compared with the day-ahead MPC algorithm, the temperature following control accuracy can be relatively improved by 16.52%, and the cooling capacity can be saved by 1.24%. Full article

Power systems’ increased running costs and overuse of fossil fuels have resulted in continuing energy scarcity and momentous energy gap challenges worldwide. Renewable energy sources can meet exponential energy growth, lower reliance on fossil fuels, and mitigate global warming. An MINLP optimization method to solve the RES-hybrid system economic dispatch of electric vehicle charging stations is proposed in this paper. This technique bridges the gap between theoretical models and real-world implementation by balancing technical optimization with practical deployment constraints, making a timely and meaningful contribution. These contributions extend the practical application of MINLP in modern grid operations by aligning optimization outputs with the stochastic character of renewable energy, which is still a gap in the existing literature. The proposed economic dispatch simulation results over 24 h at an hourly resolution show that all generation units contributed proportionately to meeting EVCS demand: solar PV (51.29%), ESS (13.5%), grid (29.92%), and wind generator (8.29%). The RES-hybrid energy management systems at charging stations are designed to make the best use of solar PV power during the EVCS charging cycle. The supply–demand load profile problem dynamic in EVCS are designed to reduce reliance on grid electricity supplies while increasing renewable energy usage and reducing carbon impact. Full article