Energies

23 pages, 7794 KB

Open AccessArticle

Improving Large Wind Turbine Power Curve by Integrating Lidar-Measured Multiple Wind Parameters: A Coastal Case Study

by Yu Shi, Fei Hu, Xuelin Li, Zhe Zhang and Kang Zhang

Energies 2025, 18(24), 6398; https://doi.org/10.3390/en18246398 (registering DOI) - 7 Dec 2025

A new power curve that is suitable for describing large wind turbines with long blades is proposed in this study. Improving the accuracy of power generation curves for large wind turbines not only involves current wind turbine development trends but also facilitates the [...] Read more.

A new power curve that is suitable for describing large wind turbines with long blades is proposed in this study. Improving the accuracy of power generation curves for large wind turbines not only involves current wind turbine development trends but also facilitates the conversion to low-carbon energy. A large wind turbine in a coastal area (with a hub height of 135 m and a long blade length of 118 m) and multidimensional wind parameters observed by lidar were integrated. Correction factors such as the turbulence intensity (TI), gust factor, and wind shear exponent (WSE) were integrated into the velocity parameter Uc to establish a multi-parameter correction prediction model suitable for describing the power generation of large wind turbines. The daily variation and distribution depending on the atmospheric stability of the correction factor were analyzed. The power generation was closer to the classical power output curve after the correction factor was applied, and the corresponding correction coefficients were proposed. The power output was enhanced with the correction factor for small winds (<4 m s⁻¹), however, the combined effects of turbulence, gust and wind shear mainly weakened the power generation for large winds of the wind turbine. Full article

(This article belongs to the Special Issue Wind Power Generation and Wind Energy Utilization)

27 pages, 2852 KB

Open AccessArticle

Minimalist Deep Learning for Solar Power Forecasting: Transformer-Based Prediction Using Key Meteorological Features

by Duncan Kibet, Min Seop So and Jong-Ho Shin

Energies 2025, 18(24), 6395; https://doi.org/10.3390/en18246395 (registering DOI) - 7 Dec 2025

Abstract

Solar power forecasting is important for energy management and grid stability, yet many deep learning studies use a large set of meteorological and time-based variables because of the belief that more inputs improve model performance. In practice, a large feature set can introduce [...] Read more.

Solar power forecasting is important for energy management and grid stability, yet many deep learning studies use a large set of meteorological and time-based variables because of the belief that more inputs improve model performance. In practice, a large feature set can introduce redundancy, increase computational effort, and reduce clarity in model interpretation. This study examines whether dependable forecasting can be achieved using only the most influential variables, presenting a minimal feature deep learning approach for short term prediction of solar power. The objective is to evaluate a Transformer model that uses only two key variables, solar irradiance and soil temperature at a depth of ten centimetres. These variables were identified through feature importance analysis. A real world solar power dataset was used for model development, and performance was compared with RNN, GRU, LSTM, and Transformer models that use the full set of meteorological inputs. The minimal feature Transformer reached a Mean Absolute Error of 1.1325, which is very close to the result of the multivariate Transformer that uses all available inputs. This outcome shows that essential temporal patterns in solar power generation can be captured using only the strongest predictors, supporting the usefulness of reducing the size of the input space. The findings indicate that selective feature reduction can maintain strong predictive performance while lowering complexity, improving clarity, and reducing data requirements. Future work may explore the adaptability of this minimal feature strategy across different regions and environmental conditions. Full article

(This article belongs to the Special Issue Waste-to-Energy and the Circular Economy: Towards Sustainable Resource Management)

29 pages, 3498 KB

Open AccessArticle

Artificial Intelligence-Driven User Interaction with Smart Homes: Architecture Proposal and Case Study

by João Lemos, João Ramos, Mário Gomes and Paulo Coelho

Energies 2025, 18(24), 6397; https://doi.org/10.3390/en18246397 (registering DOI) - 6 Dec 2025

Abstract

The evolution of Smart Grids enabled the deployment of intelligent and decentralized energy management solutions at the residential level. This work presents a comprehensive Smart Home architecture that integrates real-time energy monitoring, appliance-level consumption analysis, and environmental data acquisition using smart metering technologies [...] Read more.

The evolution of Smart Grids enabled the deployment of intelligent and decentralized energy management solutions at the residential level. This work presents a comprehensive Smart Home architecture that integrates real-time energy monitoring, appliance-level consumption analysis, and environmental data acquisition using smart metering technologies and distributed IoT sensors. All collected data are structured into a scalable infrastructure that supports advanced Artificial Intelligence (AI) methods, including Large Language Models (LLMs) and machine learning, enabling predictive analysis, personalized energy recommendations, and natural language interaction. Proposed architecture is experimentally validated through a case study on a domestic refrigerator. Two series of tests were conducted. In the first phase, extreme usage scenarios were evaluated: one with intensive usage and another with highly restricted usage. In the second phase, normal usage scenarios were tested without AI feedback and with AI recommendations following them whenever possible. Under the extreme scenarios, AI-assisted interaction resulted in a reduction in daily energy consumption of about 81.4%. In the normal usage scenarios, AI assistance resulted in a reduction of around 13.6%. These results confirm that integrating AI-driven behavioral optimization within Smart Home environments significantly improves energy efficiency, reduces electrical stress, and promotes more sustainable energy usage. Full article

(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids: 2nd Edition)

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22 pages, 1806 KB

Open AccessArticle

The Impact of Input Data on the Building Energy Performance Gap: A Case Study of Heating a Single-Family Building in Polish Conditions

by Paweł Szałański and Piotr Kowalski

Energies 2025, 18(24), 6396; https://doi.org/10.3390/en18246396 (registering DOI) - 6 Dec 2025

Abstract

This work deals with the performance gap between prognostic models of the energy needed for heating and actual energy consumption based on measurements. An in-depth case study is presented for a real single-family building, taking into account physical calculation models (monthly and simple [...] Read more.

This work deals with the performance gap between prognostic models of the energy needed for heating and actual energy consumption based on measurements. An in-depth case study is presented for a real single-family building, taking into account physical calculation models (monthly and simple hourly method according to the Polish methodology based on standard EN ISO 13790) and relevant input data. The hypothesis is confirmed in that it is possible to achieve high convergence between actual energy consumption and the calculated energy need when taking into account the high-quality input data obtained using relatively simple measurement methods. The results indicate the impact of key input data on the computational energy needed for heating. For the case study considered, the greatest influence is caused by the introduction of actual internal heat gains. Entering only some of the actual data may yield a result that is farther from the actual value than a result based only on standard data. This article provides knowledge that leads to the development of a “new consumption method” using physical models in combination with available high-quality input data, which helps change regulations and increase the significance of energy performance certificates in Poland. Full article

(This article belongs to the Special Issue Smart and Energy Efficient HVAC Systems Towards Building Decarbonizations)

24 pages, 51221 KB

Open AccessArticle

Estimation of Power-Coefficient Curve from SCADA Data for Digital-Twin Applications

by Minseok Song, Minho Kim, Jeongtaek Lim, Kyung Sun Ham and Taehyoung Kim

Energies 2025, 18(24), 6394; https://doi.org/10.3390/en18246394 (registering DOI) - 6 Dec 2025

Abstract

Digital twins are emerging as a pivotal technology for the performance optimization, predictive maintenance, and real-time monitoring of wind turbines. However, the accuracy of these virtual representations critically depends on the availability of the power coefficient (

C_{p}

) curve, a key [...] Read more.

Digital twins are emerging as a pivotal technology for the performance optimization, predictive maintenance, and real-time monitoring of wind turbines. However, the accuracy of these virtual representations critically depends on the availability of the power coefficient (

C_{p}

) curve, a key descriptor of a turbine’s aerodynamic efficiency. This information is often proprietary and not disclosed by manufacturers, posing a significant barrier to the development of high-fidelity digital twins. This study addresses this critical gap by proposing a novel framework for estimate

C_{p}

curves using operational Supervisory Control and Data Acquisition (SCADA) data. The proposed methodology utilizes a parameterized mathematical formulation to model the

C_{p}

curve and employs the Adam optimizer to robustly tune the model’s parameters against real-world operational data. The framework was evaluated through a two-pronged process. First, the model’s accuracy was assessed using synthetic SCADA data from a high-fidelity simulator under ideal conditions, demonstrating excellent agreement with an

R^{2}

exceeding 0.99 and a normalized Mean Absolute Percentage Error (nMAPE) ranging from 4.38% to 6.03%. Second, its practical performance was evaluated using real SCADA data from a commercial wind turbine, where it maintained high accuracy with an

R^{2}

ranging from 0.89 to 0.98 and an nMAPE of 3.27% to 5.97%. The findings demonstrate that the proposed methodology can effectively reconstruct a turbine’s aerodynamic characteristics without proprietary manufacturer data. This research offers a viable pathway for operators and researchers to create accurate, turbine-specific digital twins, thereby enabling enhanced performance monitoring, advanced control optimization, and predictive maintenance for more efficient and reliable wind energy production. Full article

(This article belongs to the Special Issue Advanced Wind Energy Systems: Comprehensive Insights into Analysis, Design, Control, and Optimization)

13 pages, 1618 KB

Open AccessArticle

Pressurized Chemical Looping Flue Gas Polishing via Novel Integrated Heat Exchanger Reactor

by Hongtian Ge, Matthew Perry, Jan Haelssig and Arturo Macchi

Energies 2025, 18(24), 6393; https://doi.org/10.3390/en18246393 (registering DOI) - 6 Dec 2025

Abstract

Pressurized chemical looping combustion (PCLC) provides the benefit of simplifying the carbon capture process by generating a flue gas stream with high CO₂ concentration. However, flue gas polishing is required to remove the residual impurities for pipeline transport. The intensified heat exchanger [...] Read more.

Pressurized chemical looping combustion (PCLC) provides the benefit of simplifying the carbon capture process by generating a flue gas stream with high CO₂ concentration. However, flue gas polishing is required to remove the residual impurities for pipeline transport. The intensified heat exchanger reactor (IHXR) is a promising method for flue gas polishing while maximizing useful heat recovery that incorporates alternating catalytic packed beds with interstage cooling via printed circuit heat exchangers (PCHE). This work offers a design process for an IHXR capable of polishing a flue gas stream from a 100 MWth natural gas-fired PCLC unit while recovering 1.6 MW of useful heat in the form of saturated steam at 180 °C. Simulation work performed in Aspen HYSYS was used to determine the polished flue gas outlet species concentrations as well as the required number and size of the packed bed sections. The PCHEs for interstage cooling were sized via a thermal circuit approach. The final IHXR consists of six packed beds at 0.06 m in length and five PCHEs at 0.265 m in length, combining to a total IHXR length of 1.685 m. The height and width of the IHXR is shared between the packed beds and PCHEs at 0.91 m and 0.45 m, respectively. The resulting IHXR is capable of recovering heat at a rate of approximately 2.3 MW/m³. Full article

(This article belongs to the Special Issue New Advances in Carbon Capture and Clean Energy Technologies)

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16 pages, 1727 KB

Open AccessArticle

Optimal Sizing of PV-Storage Systems Based on Multi-Scenario Simulation and Marginal Analysis

by Ying Yang, Yingai Jin and Firoz Alam

Energies 2025, 18(24), 6392; https://doi.org/10.3390/en18246392 (registering DOI) - 6 Dec 2025

Abstract

The issue of solar curtailment and generation limitations, driven by insufficient grid absorption capacity, is becoming increasingly severe, significantly reducing the capacity factor and economic returns of photovoltaic (PV) power plants. The present study proposes a scenario-based simulation framework, developed using PVsyst software [...] Read more.

The issue of solar curtailment and generation limitations, driven by insufficient grid absorption capacity, is becoming increasingly severe, significantly reducing the capacity factor and economic returns of photovoltaic (PV) power plants. The present study proposes a scenario-based simulation framework, developed using PVsyst software (version 7.4), with a view to investigating the impact of collaborative optimisation using different energy storage capacities on PV integration. The construction of an optimisation model is undertaken with the dual objectives of minimising curtailment losses and maximising the capacity factor. Through the implementation of scenario simulations, a coordinated control strategy is devised for divergent storage capacities, incorporating a charging approach during periods of photovoltaic over-generation and a discharging approach during instances of under-generation. Such an approach is coupled with marginal benefit analysis to simulate system performance under a range of technical conditions. The findings of the present study demonstrate that the implementation of storage coordination optimisation has the potential to result in a substantial reduction in curtailment losses and enhancement of the capacity factor. As energy storage capacity increases from 0 MWh to 10 MWh, curtailment losses decrease by approximately 52%, and capacity factors improve by about 11%. However, as storage capacity increases, the marginal benefits decrease. When storage capacity reaches 9 MWh, and the marginal rate of return exhibits a distinct critical point with increasing storage capacity per unit. The most critical parameter influencing the MRR (Marginal Rate of Return) is the Power Curtailment, which is the primary source of losses, and increasing the Power curtailment can immediately liberate substantial amounts of high-value, otherwise-curtailed solar energy. Full article

(This article belongs to the Special Issue Advances in Solar Thermal Energy Harvesting, Storage and Conversion—2nd Edition)

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32 pages, 1525 KB

Open AccessReview

Energy Efficiency Strategies in Latin American University Buildings: A Critical Review of Simulation Models, Technologies, and Implementation Pathways for Highland Climates

by Luis Contreras-Vásquez, Rubén Nogales-Portero, Jorge Guevara-Robalino, José Cabrera-Escobar and Alberto Ríos-Villacorta

Energies 2025, 18(24), 6391; https://doi.org/10.3390/en18246391 (registering DOI) - 6 Dec 2025

Abstract

This systematic review analyzed energy efficiency strategies in Latin American university buildings, with emphasis on highland climates. Following PRISMA guidelines, 225 documents were screened from Scopus, Web of Science, and Google Scholar, yielding 36 studies published between 2015 and 2025. Reported interventions achieved [...] Read more.

This systematic review analyzed energy efficiency strategies in Latin American university buildings, with emphasis on highland climates. Following PRISMA guidelines, 225 documents were screened from Scopus, Web of Science, and Google Scholar, yielding 36 studies published between 2015 and 2025. Reported interventions achieved 10–40% energy savings (median 18.5%), annual cost savings of USD 5672–USD 218,426 per building, with substantial variation reflecting differences in building size, intervention scope, and technology selection and carbon mitigation of 79–497 tons CO₂e annually. Common measures included LED retrofits, building automation, and solar photovoltaics, while integrated approaches reached up to 60% savings but required longer payback periods. Only six studies validated simulations with field data, and six addressed highland climates, limiting regional applicability. Free modeling tools such as EnergyPlus and OpenStudio increased accessibility but faced adoption barriers due to steep learning curves and scarce documentation in Spanish and Portuguese. Key barriers included inadequate metering (53%), limited funding (61%), and policy gaps (53%), while enablers involved ISO 50001 adoption and strong institutional leadership. Overall, evidence remains fragmented, highlighting the need for integrated frameworks linking validated models, technology, governance, and regional collaboration. Full article

(This article belongs to the Special Issue Smart Optimization and Renewable Integrated Energy System)

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25 pages, 4986 KB

Open AccessArticle

A Deep Hybrid CNNDBiLSTM Model for Short-Term Wind Speed Forecasting in Wind-Rich Regions of Tasmania, Australia

by Ananta Neupane, Nawin Raj and Ravinesh Deo

Energies 2025, 18(24), 6390; https://doi.org/10.3390/en18246390 - 5 Dec 2025

Abstract

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks [...] Read more.

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks to enhance wind speed forecasting accuracy in Australia. Thirteen years of hourly wind speed data were collected from two wind-rich potential sites in Tasmania, Australia. The CNN component effectively captures local temporal patterns, while the DBiLSTM layers model long-range dependencies in both forward and backward directions. The proposed CNNDBiLSTM model was compared against three traditional benchmark models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Categorical Boosting (CatBoost). The proposed framework can effectively support wind farm planning, operational reliability, and grid integration strategies within the renewable energy sector. A comprehensive evaluation framework across both Australian study sites (Flinders Island Airport, Scottsdale) showed that the CNNDBiLSTM consistently outperformed the baseline models. It achieved the highest correlation coefficients (r = 0.987–0.988), the lowest error rates (RMSE = 0.392–0.402, MAE = 0.294–0.310), and superior scores across multiple efficiency metrics (ENS, WI, LM). The CNNDBiLSTM demonstrated strong adaptability across coastal and inland environments, showing potential for real-world use in renewable-energy resource forecasting. The wind speed analysis and forecasting show Flinders with higher and consistent wind speed as a more viable option for large-scale wind energy generation than Scottsdale in Tasmania. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

23 pages, 2846 KB

Open AccessArticle

Policy-Driven Spatiotemporal Evolution of New Energy Technological Correlation Networks in China

by Sufeng Wang, Yuqing Nie, Hongling Xu and Yinan Sun

Energies 2025, 18(24), 6389; https://doi.org/10.3390/en18246389 - 5 Dec 2025

Abstract

The global shift towards low-carbon economies underscores the critical role of new energy (NE) technologies in addressing climate change and ensuring energy security. China’s renewable energy sector serves as a prime example of this transition. However, the sector faces significant challenges, including technological [...] Read more.

The global shift towards low-carbon economies underscores the critical role of new energy (NE) technologies in addressing climate change and ensuring energy security. China’s renewable energy sector serves as a prime example of this transition. However, the sector faces significant challenges, including technological fragmentation characterized by isolated R&D efforts that impede knowledge diffusion, and regional disparities that marginalize firms in inland and western regions within innovation networks. This study examines the spatiotemporal evolution of China’s new energy technological correlation networks across 208 firms (2006–2023) using social network analysis. The findings reveal a four-stage progression from fragmentation (2006–2010) to regional clustering (2011–2015), followed by core–periphery differentiation (2016–2020), culminating in multipolar synergy (2021–2023). Policy cycles are closely associated with structural shifts, with coastal hubs leveraging policy-industrial advantages whilst inland areas grow via technology diffusion. This study proposes the policy-driven effect, where subsidies anchor scale expansion, whereas phase-outs are linked to quality enhancement. Phase-adaptive strategies are recommended to transition from scale-driven to innovation-quality paradigms. Full article

(This article belongs to the Section C: Energy Economics and Policy)

24 pages, 4242 KB

Open AccessArticle

Simulation and Sensitivity Analysis of Energy Consumption in Floating Structures Under Typical and Typhoon Meteorological Conditions

by Wei Zheng, Yufei Wu, Wenchao Chen, Maolin Chen and Lixiao Li

Energies 2025, 18(24), 6388; https://doi.org/10.3390/en18246388 - 5 Dec 2025

Abstract

Floating structures are increasingly recognized as crucial infrastructure for deep-sea energy exploitation, offshore communities, and maritime hub facilities in recent years. Understanding their energy consumption characteristics under varying meteorological conditions is essential for ensuring operational efficiency and resilience. This study investigates the influencing [...] Read more.

Floating structures are increasingly recognized as crucial infrastructure for deep-sea energy exploitation, offshore communities, and maritime hub facilities in recent years. Understanding their energy consumption characteristics under varying meteorological conditions is essential for ensuring operational efficiency and resilience. This study investigates the influencing factors and variation patterns of energy use in floating structures under normal and typhoon environments. Three representative scenarios with different scales and functions were developed based on a bionic hexagon-shaped floating unit, and their respective energy demands were defined. A systematic sensitivity analysis was conducted using DeST with Typical Meteorological Year data and field observations from Super Typhoon Yagi (No. 2411) at Qionghai Station. Results indicate that, according to sensitivity analysis using the dynamic “intraday fluctuation + daily quantile” threshold, dry-bulb temperature and specific humidity are the dominant factors influencing floating-structure energy consumption, contributing 31.1% and 7.8% increases, respectively—significantly higher than other parameters. Under typhoon conditions, total energy consumption rose slightly relative to the TMY baseline, by 0.12%, 0.49%, and 0.95% across the three scenarios, with diurnal variations within ±5%. This study provides a quantitative basis for optimizing energy storage design and enhancing the resilience of floating structures to extreme meteorological events. Full article

(This article belongs to the Section G: Energy and Buildings)

20 pages, 5293 KB

Open AccessArticle

Numerical Simulations of a Motion-Based Latching Control Strategy for Enhanced Wave Energy Conversion in a Point Absorber

by Sabrina Galbo and Stefano Malavasi

Energies 2025, 18(24), 6387; https://doi.org/10.3390/en18246387 - 5 Dec 2025

Abstract

The power take-off (PTO) system is central to wave energy converter (WEC) performance, and therefore control strategies are essential to effectively enhance energy absorption and device response. However, many existing controls often rely on predictive or mechanically complex approaches that limit their practical [...] Read more.

The power take-off (PTO) system is central to wave energy converter (WEC) performance, and therefore control strategies are essential to effectively enhance energy absorption and device response. However, many existing controls often rely on predictive or mechanically complex approaches that limit their practical and numerical implementation. This work proposes a passive, non-predictive, sub-optimal PTO control strategy suitable for CFD modeling. This study focuses on latching control, which temporarily restrains the device, introducing a novel release mechanism based solely on the float’s angular velocity and providing a simple motion-based criterion. A nearshore point absorber serves as the reference device, featuring a single degree of oscillation achieved through a heaving float. CFD simulations are conducted using a FLOW-3D (HYDRO) model previously developed at Politecnico di Milano, in which the PTO is modeled as a torsional spring object. Software customization enables damping modulation, and the latching strategy is refined by optimizing the threshold angular velocity under two monochromatic wave conditions. Results show an approximate 20% increase in absorbed energy, improved phase alignment, and a clear operational threshold-velocity window, indicating that the proposed motion-based strategy can effectively enhance WEC performance. Further assessments under additional wave conditions will help establish its robustness and validate its broader applicability. Full article

(This article belongs to the Special Issue Leading the Way in Offshore Renewable Energy and Wave Energy Conversion)

19 pages, 11470 KB

Open AccessArticle

A Large Eddy Simulation-Based Power Forecast Approach for Offshore Wind Farms

by Yongjie Lu, Tasnim Zaman, Bin Ma, Marina Astitha and Georgios Matheou

Energies 2025, 18(24), 6386; https://doi.org/10.3390/en18246386 - 5 Dec 2025

Abstract

Reliable power forecasts are essential for the grid integration of offshore wind. This work presents a physics-based forecasting framework that couples mesoscale numerical weather prediction with large-eddy simulation (LES) and an actuator-disk turbine representation to predict farm-scale flows and power under realistic atmospheric [...] Read more.

Reliable power forecasts are essential for the grid integration of offshore wind. This work presents a physics-based forecasting framework that couples mesoscale numerical weather prediction with large-eddy simulation (LES) and an actuator-disk turbine representation to predict farm-scale flows and power under realistic atmospheric conditions. Mean meteorological profiles from the Weather Research and Forecasting model drive a concurrent–precursor LES generating turbulent inflow consistent with the evolving boundary layer, while a main LES resolves turbulence and wake formation within the wind farm. The LES configuration and turbine-forcing implementation are validated against canonical single- and multi-turbine benchmarks, showing close agreement in wake deficits and recovery trends. The framework is then demonstrated for the South Fork Wind project (12 turbines, ∼132 MW) using a set of time-varying cases over a 24 h period. Simulations reproduce hub-height wind variability, row-to-row power differences associated with wake interactions, and turbine-level power fluctuations (order 1 MW) that converge with appropriate averaging windows. The results illustrate how an LES-augmented hierarchical modeling system can complement conventional forecasting by providing physically interpretable flow fields and power estimates at operational scales. Full article

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25 pages, 2485 KB

Open AccessArticle

The Impact of Energy Consumption Structure Transformation on Urban Green Development Efficiency: Considering the Threshold Effect of Industrial Digitalization

by Runde Gu, Yuwei Hu, Han Jia, Shijun Du and Runlin Tian

Energies 2025, 18(24), 6385; https://doi.org/10.3390/en18246385 - 5 Dec 2025

Abstract

Energy consumption structure transformation (ECST) is essential to decrease pollutant emissions and realize green development. By constructing fixed effects, spatial Durbin, and threshold models, this paper adopts the panel data of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration to investigate the impact [...] Read more.

Energy consumption structure transformation (ECST) is essential to decrease pollutant emissions and realize green development. By constructing fixed effects, spatial Durbin, and threshold models, this paper adopts the panel data of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration to investigate the impact and spatial spillover effect of ECST on urban green development efficiency (GDE). The study also reveals the moderating effect of industrial digitization level (IDL) on the main effect and the varying trend of effect intensity across different threshold intervals. This study represents the first comprehensive investigation into the impact of energy consumption structure transformation on urban green development efficiency, broadening the research perspective on energy consumption structure. The innovative incorporation of spatial dimensions and industrial digitization threshold effects provides theoretical support and practical guidance for regional collaborative development and precise policy regulation. We found the following: (1) ECST can enhance BTH’s urban GDE; (2) The effect of ECST on BTH’s urban GDE shows a U-shaped relationship of first inhibition and then enhancement; (3) ECST has a spatial spillover effect and significantly contributes to enhancing BTH’s urban GDE; and (4) IDL can significantly increase BTH’s urban GDE and has the moderating effect of strengthening ECST for increasing urban GDE. As IDL increases, its moderating effect on the main effect displays a steady and gradual decreasing trend. Full article

20 pages, 2001 KB

Open AccessArticle

Research into the Energy Potential of Vine Pruning Residues in Western Serbia

by Aleksandar Ašonja, Sunčica Vještica, Aleksandar Bošković, Svetlana Živković Radeta, Mirjana Ćeranić, Zoran Jovanović and Siniša Škrbić

Energies 2025, 18(24), 6384; https://doi.org/10.3390/en18246384 - 5 Dec 2025

Abstract

Research and practice experience have shown that in the Republic of Serbia, vine pruning residues (VPRs) from vineyard production are mostly partially ploughed or uncontrollably burned in fields. Uncontrolled burning of VPRs in fields can destroy flora and fauna and cause uncontrolled fires. [...] Read more.

Research and practice experience have shown that in the Republic of Serbia, vine pruning residues (VPRs) from vineyard production are mostly partially ploughed or uncontrollably burned in fields. Uncontrolled burning of VPRs in fields can destroy flora and fauna and cause uncontrolled fires. On the other hand, on an annual basis, the resulting VPRs can completely replace the fossil fuels used for thermal energy production on these estates and significantly reduce the emission of pollutants from fossil fuels. The novelty of this study lies in the fact that the research was conducted on a very young vineyard, four years old, and the results show that the agricultural property is fully sustainable in terms of thermal energy needs. The research aimed to investigate the energy potential of VPRs at the vineyard located in the Mrčić settlement in Western Serbia. The research results include the following grape varieties: Tamjanika, Morava, Cabernet Sauvignon, and Cabernet Franc. The average yield of VPR biomass for all tested varieties was 0.387 kg/vine or 1741.50 kg/ha. The lower calorific values for the tested biomass samples at 15% moisture content ranged from 14,668 kJ/kg to 14,258 kJ/kg, while the upper values ranged from 16,099 kJ/kg to 15,721 kJ/kg. The total energy potential of biomass obtained from a vineyard, expressed in final energy, was 41.90 MWh/year. In the observed vineyard, for the same equivalent value, biomass from VPRs was 3.57 times cheaper compared to brown coal and 8.26 times cheaper compared to diesel fuel. Full article

(This article belongs to the Special Issue Renewable Energy Integration into Agricultural and Food Engineering)

18 pages, 3656 KB

Open AccessReview

Multi-Terminal HVDC Networks for Offshore Energy Integration: Technical Challenges and Grid Interfacing Strategies

by Moazzam Nazir, Johan H. Enslin, James McCalley and Eric Hines

Energies 2025, 18(24), 6383; https://doi.org/10.3390/en18246383 - 5 Dec 2025

Abstract

Offshore wind (OSW) energy represents a vast and largely untapped resource capable of significantly contributing to the rising global electricity demand while advancing ambitious decarbonization and clean energy transition goals. Despite its potential, the effective harnessing of OSW is contingent upon the strategic [...] Read more.

Offshore wind (OSW) energy represents a vast and largely untapped resource capable of significantly contributing to the rising global electricity demand while advancing ambitious decarbonization and clean energy transition goals. Despite its potential, the effective harnessing of OSW is contingent upon the strategic and reliable integration of offshore generation into existing onshore AC power systems. Multi-terminal high-voltage direct current (MTDC) networks have emerged as a promising solution for this task, offering enhanced flexibility, scalability, and operational resilience. However, several technical and operational challenges—such as lack of standardization, coordinated control of multiple multi-vendor converters, reliable communication infrastructures, protection schemes, and seamless integration of offshore HVDC substations—must be addressed to fully realize the benefits of MTDC systems. This review paper critically examines these challenges and proposes a control, communication, protection, and HVDC substation design that could be adopted as an initial guideline for the efficient and secure integration of OSW into AC grids. By identifying current research gaps and synthesizing existing solutions, the paper provides a comprehensive framework for optimizing the role of MTDC networks in future offshore wind deployments. Full article

(This article belongs to the Special Issue Grid Integration of Renewable Energy: Latest Advances and Prospects)

30 pages, 2294 KB

Open AccessReview

Heat Transfer Calculation Method, Thermal Performance, and Control Strategy of Radiant Heating and Cooling System: A Review

by Zichen Liu, Jun Wang, Chengjun Jing, Xiaozhou Wu and Dong Liu

Energies 2025, 18(24), 6382; https://doi.org/10.3390/en18246382 - 5 Dec 2025

Abstract

The radiant heating and cooling (RHC) system is one of the important air-conditioning methods that simultaneously achieves indoor thermal comfort and building energy efficiency. It is characterized by utilizing low-grade energy sources to provide low-temperature heating and high-temperature cooling, playing a significant role [...] Read more.

The radiant heating and cooling (RHC) system is one of the important air-conditioning methods that simultaneously achieves indoor thermal comfort and building energy efficiency. It is characterized by utilizing low-grade energy sources to provide low-temperature heating and high-temperature cooling, playing a significant role in promoting the development of green and low-carbon buildings. This study firstly introduces the typical heat transfer calculation methods of the RHC system and analyzes the surface heat transfer coefficients of radiant heating and cooling. Subsequently, the factors affecting the thermal performance of the RHC system are discussed from two aspects: relevant physical property parameters and flow channel structures. Finally, the control strategies of RHC systems are summarized to address issues such as condensation, overheating, and long response times. And several conclusive findings are presented that are worthy of further investigation in the future. Full article

(This article belongs to the Special Issue Innovations in Low-Carbon Building Energy Systems)

20 pages, 2445 KB

Open AccessArticle

Nonlinear Pressure Fluctuation Management for Ejector-Based Hydrogen Recirculation System in Large-Power Vehicular PEMFCs

by Haojin Xu, Lei Wang, Chen Wang and Xinli Wang

Energies 2025, 18(24), 6381; https://doi.org/10.3390/en18246381 - 5 Dec 2025

Abstract

The ejector-based hydrogen recirculation systems in vehicular proton exchange membrane fuel cells (PEMFCs) have been the research focus of fuel cell technology. However, the anode pressure fluctuations and nonlinear characteristics urgently need to be addressed under the varying operating conditions of the ejector-based [...] Read more.

The ejector-based hydrogen recirculation systems in vehicular proton exchange membrane fuel cells (PEMFCs) have been the research focus of fuel cell technology. However, the anode pressure fluctuations and nonlinear characteristics urgently need to be addressed under the varying operating conditions of the ejector-based hydrogen recirculation system. In this paper, an Adaptive Model Predictive Control strategy is proposed to stabilize anode pressure and smooth pressure fluctuations during current changes and purges. An ejector is designed and a nonlinear control model is established for the ejector-based hydrogen recirculation system of the 110 kW PEMFC. The proposed strategy achieves the mean absolute error (MAE) of 0.044 kPa and the root mean square error of 1.041 kPa in anode pressure management, outperforming traditional Model Predictive Control and Proportional-Integral-Derivative strategies. The hydrogen excess ratio exceeding 1.5 ensures compliance with the operational requirements of system. The experimental results with the pressure MAE of 0.482 kPa and the current fluctuation of ±0.7 A validate the effectiveness of the proposed strategy in practical applications. Full article

(This article belongs to the Special Issue Functional Materials for Advanced Energy Applications)

18 pages, 5231 KB

Open AccessArticle

A Comprehensive Characteristic Modeling Method for Francis Turbine Based on Image Digitization and RBF Neural Network

by Youhan Deng, Youping Li, Xiaojun Hua, Rui Lyu, Yushu Li, Lei Wang, Weiwei Yao, Yifeng Gu, Fangqing Zhang and Jiang Guo

Energies 2025, 18(24), 6380; https://doi.org/10.3390/en18246380 - 5 Dec 2025

Abstract

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on [...] Read more.

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on the comprehensive characteristic curves of the Francis turbine provided by the manufacturer, using the external characteristic method. Traditional modeling methods mostly adopt manual reading of points or the use of dedicated numerical software for curve tracing to discretely sample the comprehensive characteristic curves of the turbine. This method is labor-intensive, inefficient, and relies on manual experience, with a small sample size, which, to some extent, affects the accuracy and reliability of the numerical processing results and cannot meet the needs of transient process simulation analysis. To address these shortcomings, this paper proposes a refined modeling method based on image numerical processing and an RBF neural network. Taking the HLA685 Francis turbine as an example, the method first uses image processing to achieve large-scale automated discrete sampling of the turbine’s high-efficiency zone characteristic data, then reasonably extends the small-opening and low-speed regions, and finally uses the RBF neural network method for interpolation and extrapolation to obtain the full characteristic data. This method can effectively improve the efficiency and accuracy of comprehensive characteristic modeling of the turbine and has good reference significance for the comprehensive characteristic modeling of blade-type machinery. Full article

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