Sustainable Energy Systems

Topic Information

Dear Colleagues,

Sustainable energy systems are at the forefront of scientific research due to their potential to combat climate change, promote energy security, and reduce environmental degradation. These systems, based on renewable energy sources, seek to meet global energy demands while minimizing the negative impacts of traditional fossil fuel use. Scientific interest in sustainable energy systems spans multiple disciplines, addressing challenges from energy generation to technological integration in key sectors like agriculture, buildings, and cities.

From a general perspective, sustainable energy systems include renewable energy sources such as solar, wind, geothermal, and bioenergy. These systems generate clean, renewable energy, which helps reduce carbon emissions and fossil fuel dependence. However, optimizing these systems to meet the fluctuating energy demands while ensuring reliability and efficiency remains a major focus of research. Innovations in energy storage and grid integration are key to expanding their scalability and applicability across different regions.

Sensor technologies are essential for the efficient management of sustainable energy systems. Sensors monitor energy production, consumption, and system performance in real time, providing valuable data for optimizing operations. In agriculture, sensors enable precision farming by managing energy-intensive processes like irrigation. In buildings and cities, smart sensors are used to regulate lighting, heating, cooling, and overall energy consumption, thereby improving efficiency and reducing waste. The integration of these sensors with the Internet of Things (IoT) further enhances the effectiveness of energy systems by enabling automated adjustments and predictive maintenance.

From a technological standpoint, energy storage solutions are a critical area of research. Technologies such as advanced batteries, hydrogen storage, and thermal storage systems allow renewable energy to be stored and used when needed, addressing the intermittency of sources like solar and wind. Additionally, smart grids and microgrids, which incorporate decentralized renewable energy sources, are emerging as important components of sustainable energy infrastructures. These technologies allow for better control, distribution, and management of energy flows, leading to improved reliability and resilience in energy systems.

The applications of sustainable energy systems are expanding in diverse sectors. In agriculture, renewable energy powers efficient irrigation systems, farm machinery, and cold storage, reducing energy costs and improving sustainability. Solar-powered water pumps and wind turbines provide off-grid energy solutions for rural farms, while biogas from agricultural waste offers a circular approach to energy production. In buildings, sustainable energy technologies like solar panels, energy-efficient insulation, and smart HVAC systems lower energy demand, reduce emissions, and improve building sustainability. Cities are also adopting sustainable energy systems, with smart grids, renewable-powered public infrastructure, and electric vehicle (EV) charging stations becoming increasingly prevalent. These innovations are crucial for reducing urban carbon footprints and improving energy management in densely populated areas.

New trends in sustainable energy systems include the development of hybrid renewable energy systems, which combine multiple energy sources (e.g., solar and wind) to improve reliability and efficiency. In agriculture, the rise of agrivoltaics—where solar panels are installed over crops—optimizes land use by generating electricity while protecting crops. In buildings, net-zero energy buildings (NZEBs) are becoming a significant focus, where buildings are designed to generate as much energy as they consume. In cities, decentralized energy systems such as microgrids are becoming more prominent, providing communities with localized energy generation and distribution. The application of artificial intelligence (AI) and machine learning is also advancing, allowing for the real-time optimization of energy production, distribution, and consumption, as well as predictive maintenance for energy infrastructure.

The transition to sustainable energy systems is essential to address the growing challenges of climate change and energy security. Research plays a pivotal role in developing innovative solutions that optimize energy efficiency, integrate renewable resources, and minimize environmental impact. Within this context, vehicles serve as fundamental agents of transformation. By advancing technologies such as electric mobility, hydrogen-powered engines, and smart transportation systems, vehicles not only reduce emissions but also act as catalysts for cleaner, more sustainable energy ecosystems. For example, agricultural vehicles hold a particularly vital role in this transition, as they are key to enhancing the sustainability of the farming sector. By integrating renewable energy sources, precision farming technologies, and low-emission powertrains, these vehicles can reduce environmental impact while boosting efficiency and productivity. Their evolution is instrumental in building a sustainable future for both agriculture and energy systems.

In conclusion, sustainable energy systems are a crucial area of scientific research, with applications across agriculture, buildings, and cities. These systems offer solutions to pressing global challenges such as climate change, energy security, and resource depletion. Innovations in sensor technologies, energy storage, and smart grids are helping to optimize these systems, making them more efficient and scalable. As new trends continue to emerge, sustainable energy systems will play an increasingly important role in shaping a low-carbon, resilient energy future.

Prof. Dr. Luis Hernández-Callejo
Prof. Dr. Carlos Meza Benavides
Dr. Jesús Armando Aguilar Jiménez
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	4.5	7.8	2011	17.4 Days	CHF 2600	Submit
Algorithms algorithms	2.6	5.4	2008	17.6 Days	CHF 1800	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit
Urban Science urbansci	3.2	3.7	2017	20.8 Days	CHF 1800	Submit
Vehicles vehicles	3.2	5.5	2019	19.7 Days	CHF 1800	Submit

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Published Papers (17 papers)

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34 pages, 9262 KB  

Open AccessArticle

Parameter Importance Ranking of a Heat Pump–Organic Rankine Cycle Pumped Thermal Energy Storage System: A Weighted Neighborhood Rough Set Feature Selection

by Xiaoqiang Ma and Yuming Xing

Energies 2026, 19(19), 4578; https://doi.org/10.3390/en19194578 - 26 Sep 2026

Viewed by 184

Abstract

Industrial waste heat and renewable thermal sources below 350 °C can be stored and dispatched through heat pump–organic Rankine cycle (HP-ORC) pumped thermal energy storage (PTES), yet its performance is governed by a series of coupled thermodynamic and operational variables whose relative importance [...] Read more.

Industrial waste heat and renewable thermal sources below 350 °C can be stored and dispatched through heat pump–organic Rankine cycle (HP-ORC) pumped thermal energy storage (PTES), yet its performance is governed by a series of coupled thermodynamic and operational variables whose relative importance to competing objectives remains poorly quantified. This study, for the first time, applies a weighted neighborhood rough set (WNRS) algorithm to rank the significance of fifteen continuous decision variables of a latent-storage HP-ORC PTES system with respect to electrical-power-to-power ratio, exergy efficiency, levelized cost of storage (LCOS), and life-cycle carbon intensity (CI). The WNRS-reduced feature subsets, with neighborhood radius calibrated by KNN and SVM cross-validation, are then optimized through a two-stage particle swarm optimization (PSO)–technique for order of preference by similarity to ideal solution (TOPSIS) framework. Results show that the four objectives are governed by distinct parameter subsets: electrical-power-to-power ratio depends on a compact set of cycle temperatures, LCOS on heat-source-related operating variables, and exergy efficiency on a broader set including heat-transfer matching parameters, while CI on the mass flow rates. The HP evaporating temperature emerges as the dominant coupling variable. For the composite TOPSIS objective, the eight-variable reduct selected for the composite objective recovers about 69% of the 11-feature improvement while markedly reducing dimensionality. An independent benchmark confirms that the WNRS reduction is physically interpretable rather than purely statistical. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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17 pages, 4022 KB  

Open AccessArticle

From Geospatial Assessment to Road Thermal Management: A Digital Framework for Climate-Resilient Infrastructure Using Low-Enthalpy Geothermal Energy

by Cristina Sáez Blázquez, Sergio Alejandro Camargo Vargas, Daniel Herranz Herranz and Miguel Ángel Maté-González

Energies 2026, 19(18), 4237; https://doi.org/10.3390/en19184237 - 8 Sep 2026

Viewed by 253

Abstract

Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact [...] Read more.

Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact compared to conventional maintenance practices. This study presents the methodology developed within the GEO-ROAD project to assess shallow geothermal resources across Spain and support the future deployment of geothermal road systems. The proposed framework integrates geological, thermal, and satellite-derived geophysical information through a unified GIS-based workflow, combining multivariate statistical analysis, map algebra, and automated geospatial processing to generate a regional geothermal potential model. In addition to conventional geological characterization, the methodology incorporates magnetic and gravity data from satellite missions, airborne surveys, and ground-based observations to improve the spatial representation of subsurface conditions. The resulting geothermal potential assessment constitutes a key component of the GEO-ROAD digital platform, where it will be combined with climatic risk maps and road infrastructure information to identify the most suitable locations for geothermal applications. By linking geothermal resource assessment with infrastructure-oriented decision-making, the proposed methodology provides a scalable and transferable framework for supporting the planning of sustainable and climate-resilient road thermal management systems. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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23 pages, 1980 KB  

Open AccessArticle

Enabling Cooperative Dispatch of Heterogeneous Agricultural Microgrids Through Trusted-Node Coordination

by Jue Han, Fangyuan Li, Zhengqi Li, Xuke Zuo and Yanhong Liu

Agriculture 2026, 16(17), 1869; https://doi.org/10.3390/agriculture16171869 - 29 Aug 2026

Viewed by 334

Abstract

Driven by rural energy decarbonization and the electrification of agricultural production, agricultural microgrids (agri-microgrids) have become a critical paradigm for accommodating distributed renewable generation and volatile farming loads. However, agri-microgrids usually face acute cost sensitivity. Coordinated operation of autonomous agri-microgrids offers a promising [...] Read more.

Driven by rural energy decarbonization and the electrification of agricultural production, agricultural microgrids (agri-microgrids) have become a critical paradigm for accommodating distributed renewable generation and volatile farming loads. However, agri-microgrids usually face acute cost sensitivity. Coordinated operation of autonomous agri-microgrids offers a promising solution. Yet coordinating heterogeneous energy resources to exploit temporal complementarity while fairly allocating costs remains a key challenge. Information asymmetry, the lack of trusted node, and the computational complexity of revenue allocation impede cooperative dispatch of multi-microgrids.This paper proposes a decentralized game-theoretic framework for cooperative multi-microgrid dispatch. Firstly, a distributed node information collection algorithm is designed to acquire necessary data while preserving local privacy. Secondly, a trusted-node election method is proposed to enable reliable coalition profit calculation without external authorities or predefined centralized coordinators. Then, an enhanced Shapley value-based revenue allocation strategy, which reduces the computational complexity, is presented. Finally, case studies verify the effectiveness of the proposed algorithms in reducing operational costs and promoting green energy coordination among agri-microgrids. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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24 pages, 4001 KB  

Open AccessArticle

Black-Box and Interpretable Artificial Intelligence Models for Hydrogen Uptake Across Various Metal–Organic Frameworks

by Regan Solomon Ward Taylor, Shahin Alipour Bonab and Mohammad Yazdani-Asrami

Algorithms 2026, 19(8), 640; https://doi.org/10.3390/a19080640 - 2 Aug 2026

Viewed by 458

Abstract

Hydrogen (H₂) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H₂ remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic [...] Read more.

Hydrogen (H₂) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H₂ remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic Frameworks (MOFs), highly porous crystalline materials, have emerged as promising H₂ storage candidates owing to their high surface areas and tuneable pore structures. Molecular simulations such as grand canonical Monte Carlo or density functional theory are costly and limited in exploring large material spaces, motivating efficient predictive tools to accelerate discovery. Here, Machine Learning (ML) techniques are compared to an explainable artificial intelligence (XAI) approach using symbolic regression (SR), trained on 10,123 experimentally measured H₂ adsorption datapoints from real-world MOFs. The best performing model achieved a goodness of fit of 0.9986 with lower computational demand, but reduced interpretability, addressed using XAI analysis and clustering. SR achieves a lower goodness of fit of 0.914 but produces a physically meaningful equation highlighting structural features driving high gravimetric efficiencies. These results demonstrate strong ML capability for predicting how MOF properties and environmental conditions affect H₂ uptake. This offers engineers and researchers a practical means of screening potential MOFs for H₂ storage applications, with the XAI analyses providing additional confidence in the predictions. They allow researchers to understand the physical reasoning behind each output, assess the reliability of individual predictions, and make fully informed decisions, enabling predictive models to be acted upon with confidence in real-world contexts. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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24 pages, 7693 KB  

Open AccessArticle

The DC Series Arc Fault Detection System Based on Multi-Scale Generalized Amplitude-Aware Permutation Entropy

by Zhendong Yin, Hongxia Ouyang and Junchi Lu

Agriculture 2026, 16(13), 1466; https://doi.org/10.3390/agriculture16131466 - 4 Jul 2026

Viewed by 554

Abstract

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application [...] Read more.

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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27 pages, 5645 KB  

Open AccessArticle

Impact of DC-Link Dynamics on Shaft Damping and Grid Frequency Coupling in Doubly Fed Induction Generator Wind Turbines: Mechanism Analysis and a Suppression Strategy

by Zheng Wang and Yimin Lu

Energies 2026, 19(12), 2857; https://doi.org/10.3390/en19122857 - 16 Jun 2026

Viewed by 404

Abstract

In this paper, we address shaft oscillations and grid-connected oscillation frequency coupling in doubly fed induction generators (DFIGs) under DC-link dynamics. A comprehensive DFIG shaft system model incorporating DC-link dynamics is established, and frequency coupling is analyzed. From our findings, we reached the [...] Read more.

In this paper, we address shaft oscillations and grid-connected oscillation frequency coupling in doubly fed induction generators (DFIGs) under DC-link dynamics. A comprehensive DFIG shaft system model incorporating DC-link dynamics is established, and frequency coupling is analyzed. From our findings, we reached the following conclusions: (a) DC-link voltage fluctuations alter electromagnetic torque through rotor-side converter (RSC) and grid-side converter (GSC) coupling, affecting shaft dynamics; (b) DC-link dynamics compromise grid connection stability by influencing both GSC and RSC output voltages. To mitigate these effects, a DC-link dynamics suppression module is proposed. Simulations confirm that in maximum power point tracking (MPPT) mode, the module enhances electrical positive damping and improves shaft stability. In constant power mode, its stabilizing effect is comparatively limited. The suppression module effectively mitigates grid-connected frequency coupling during DC-link voltage fluctuations. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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15 pages, 1259 KB  

Open AccessArticle

Beyond Structural Adjustment: Quantifying the Dominance of New Energy Vehicles in Expressway Carbon Mitigation Targets

by Songlin Xu, Huiying Wen and Sheng Zhao

Vehicles 2026, 8(6), 118; https://doi.org/10.3390/vehicles8060118 - 29 May 2026

Viewed by 318

Abstract

Reducing carbon emissions from expressway systems has become increasingly important under continued growth in passenger and freight activity. Using Guangdong Province as a case study, this paper develops an evolutionary system dynamics model to compare the mitigation effects of transport structure adjustment and [...] Read more.

Reducing carbon emissions from expressway systems has become increasingly important under continued growth in passenger and freight activity. Using Guangdong Province as a case study, this paper develops an evolutionary system dynamics model to compare the mitigation effects of transport structure adjustment and increasing new energy vehicle (NEV) penetration. The model integrates socioeconomic development, traffic activity, vehicle technology composition, energy use, and carbon emissions, and simulates the carbon-emission trajectory of the provincial expressway network from 2016 to 2035. The results show that expressway carbon emissions in Guangdong remain under clear upward pressure in the baseline scenario. By 2035, the NEV Growth scenario reduces emissions by 14.73% relative to the baseline, whereas the Transport Structure Adjustment scenario reduces emissions by only 2.41%. The Combined Scenario achieves the largest reduction, reaching 18.06%. These results indicate that technological substitution contributes much more to carbon mitigation than moderate structural adjustment, while the combined pathway produces the strongest overall effect. The findings suggest that expressway decarbonization policy should prioritize NEV deployment and supporting infrastructure, while treating transport structure adjustment as a supplementary pathway. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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25 pages, 3496 KB  

Open AccessArticle

Evidence-Based Assessment of Commercial Fuel Additives Using OBD-Derived Fuel Economy Under Real-World High-Altitude Driving Conditions

by Daniel Barzallo-Arce, Edgar Vicente Rojas-Reinoso, Daysi Baño-Morales, David Calderón Herrera and José Antonio Soriano

Vehicles 2026, 8(6), 115; https://doi.org/10.3390/vehicles8060115 - 22 May 2026

Viewed by 1244

Abstract

This exploratory study assessed the vehicle- and route-dependent response of five multipoint injection passenger vehicles to two commercial fuel additives marketed as octane-related gasoline additives under real-world high-altitude driving conditions in Quito, Ecuador. The tests were conducted on one urban route and one [...] Read more.

This exploratory study assessed the vehicle- and route-dependent response of five multipoint injection passenger vehicles to two commercial fuel additives marketed as octane-related gasoline additives under real-world high-altitude driving conditions in Quito, Ecuador. The tests were conducted on one urban route and one rural/peripheral route using base gasoline with a nominal octane index of RON 85, RON 85 gasoline with Additive A, and RON 85 gasoline with Additive B. Fuel economy and CO₂-related indicators were obtained through the OBD-II port using the Torque Pro application; therefore, the reported values were interpreted as electronic control unit-based estimates rather than direct gravimetric fuel consumption or laboratory emissions measurements. The revised analysis used OBD-derived trip-average fuel economy as the primary response variable. The mixed-effects model showed a significant effect of route on fuel economy (p < 0.001) and a significant fuel condition × route interaction (p = 0.0089), while the main effect of fuel condition was not statistically significant (p = 0.0699). Additive B increased the mean OBD-derived trip-average fuel economy on the urban route from 11.56 to 12.60 km·L⁻¹, but reduced it on the rural route from 13.46 to 12.65 km·L⁻¹. At the vehicle level, the previously extreme Vehicle 3 response was revised to a more plausible increase from 11.03 to 13.64 km·L⁻¹ (+23.68%) when trip-average fuel economy was used. Since the actual RON/MON values and physicochemical properties of the final fuel blends were not experimentally measured, the observed responses cannot be attributed exclusively to octane number enhancement. Overall, the findings indicate that commercial additive performance was vehicle- and route-dependent rather than universally beneficial. This field-based assessment supports evidence-informed decision-making for sustainable mobility and aligns with SDG 16 and SDG 17 through transparent technical evaluation and academic collaboration. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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23 pages, 5479 KB  

Open AccessArticle

Development and Validation of a Physical Model Optimized by Evolutionary Algorithms for the Accurate Estimation of Cell Temperature in Photovoltaic Systems

by Doroteya Dimitrova-Angelova, Diego Carmona Fernández, Manuel Calderón Godoy, Juan Antonio Álvarez Moreno and Juan Félix González González

Energies 2026, 19(10), 2286; https://doi.org/10.3390/en19102286 - 9 May 2026

Viewed by 537

Abstract

Accurate photovoltaic cell temperature estimation is critical for maximizing energy management and improving digital twin fidelity in building-integrated solar systems. Classical models, NOCT (Nominal Operating Cell Temperature), King, Skoplaki, and PVsyst/Faiman, provide a practical baseline but exhibit significant limitations when applied to complex, [...] Read more.

Accurate photovoltaic cell temperature estimation is critical for maximizing energy management and improving digital twin fidelity in building-integrated solar systems. Classical models, NOCT (Nominal Operating Cell Temperature), King, Skoplaki, and PVsyst/Faiman, provide a practical baseline but exhibit significant limitations when applied to complex, real-world scenarios. These static and linear approaches fail to capture dynamic thermal phenomena such as thermal inertia, nonlinear irradiance effects, and wind-temperature interactions. This paper presents an advanced physical model that incorporates thermal memory effects, sophisticated wind modeling, transient cloud-response mechanisms, and non-linear thermal dependencies. Parameter calibration was performed using a differential evolution algorithm, automatically optimizing the model fit to one year of experimental data from a 2.79 kW pilot installation at the University of Extremadura. The validation results demonstrate consistent improvements across all seasons: RMSE reductions of up to 4.9% and MAE reductions of up to 14.4% compared to classical approaches, with particularly pronounced gains during the summer and autumn. The methodology is readily transferable to diverse installations and climatic contexts, providing a robust framework for developing high-accuracy PV digital twins and enabling early fault detection and operational optimization. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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18 pages, 4256 KB  

Open AccessArticle

Analytical Modeling of Solar Radiation Distribution on Vertical PV Facades in Urban Settings

by Joseph Appelbaum and Assaf Peled

Urban Sci. 2026, 10(5), 258; https://doi.org/10.3390/urbansci10050258 - 8 May 2026

Viewed by 626

Abstract

With the need for increasing energy demand due to population growth in cities, and advancements in the efficiency of semi-transparent photovoltaic (ST-PV) technology, the integration of ST-PV modules into building windows has become feasible. This manuscript presents a novel analytical methodology for estimating [...] Read more.

With the need for increasing energy demand due to population growth in cities, and advancements in the efficiency of semi-transparent photovoltaic (ST-PV) technology, the integration of ST-PV modules into building windows has become feasible. This manuscript presents a novel analytical methodology for estimating incident solar energy on vertical PV modules integrated into building facades in an urban environment, emphasizing shade caused by nearby buildings. Monthly and annual direct-beam, diffusion, and global energies are calculated for different wall heights, building separation, and orientation. In addition, the distribution of the incident energy along the height of the wall is evaluated, indicating a non-uniform distribution. The incident diffusion energy is compared between isotropic and anisotropic models. The anisotropic model predicts higher diffusion energy by 3.5% to 14.5%, depending on the building separation. The incident energy on building facades is calculated for locations at low-mid ($32°6’ N$) and at high-mid ($52.2° N$) latitudes. The results show, for example, that both the front side of a front-building wall and the front side of a rear-building wall receive the same amount of annual global energy—$913 kWh/m^2$—for a separation of 25 m between the buildings. Decreasing the distance from 25 m to 10 m decreases the annual incident global energy on a rear-building wall by 15%. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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18 pages, 3256 KB  

Open AccessArticle

Macroaggregate–Microaggregate Interactions Drive Soil Carbon and Nitrogen Stabilization Under Rotational Tillage in Dryland Farming

by Sha Yang, Zhigang Wang, Jin Tong, Jing Xu, Juan Bai, Xingxing Qiao, Meichen Feng, Lujie Xiao, Xiaoyan Song, Meijun Zhang, Guangxin Li, Fahad Shafiq, Jiancheng Zhang, Chao Wang and Wude Yang

Agriculture 2026, 16(2), 264; https://doi.org/10.3390/agriculture16020264 - 21 Jan 2026

Cited by 1 | Viewed by 1208

Abstract

Soil total carbon (TC) and total nitrogen (TN) are key indicators of soil fertility and ecosystem stability, particularly in dryland agroecosystems. However, how rotational tillage combined with straw return affects aggregate formation and aggregate-associated TC and TN stabilization remains insufficiently understood. In this [...] Read more.

Soil total carbon (TC) and total nitrogen (TN) are key indicators of soil fertility and ecosystem stability, particularly in dryland agroecosystems. However, how rotational tillage combined with straw return affects aggregate formation and aggregate-associated TC and TN stabilization remains insufficiently understood. In this study, we aimed to clarify how rotational tillage affects aggregate structure, stability, and the spatial distribution of TC and TN, thereby revealing internal processes driving nutrient stabilization in dryland farming systems. A long-term field experiment was conducted at the Shenfeng site of Shanxi Agricultural University, China, including three rotational tillage systems with straw return: T1 (two years of no tillage (NT) + one year of deep tillage (DT)), T2 (two years of conventional tillage (CT) + one year of DT), and T3 (two years of DT + one year of CT). Soil aggregates were separated into total mechanical aggregate (TMA), 0.25–2 mm MA, and 2–10 mm MA, and they were further fractionated into water-stable aggregates (WM, Wm, and Wf) for TC and TN analysis. The results showed that aggregate stability, TC, and TN were positively correlated and decreased with soil depth, indicating strong surface enrichment. TC was mainly enriched in 0.25–2 mm MA, whereas TN was concentrated in 2–10 mm MA, and water-stable macroaggregates (WM) acted as the dominant reservoirs for R_C and R_N. Relative to the 2016 baseline (CK), TC in 2022 tended to be higher under rotational tillage with straw return, while NT-containing systems better maintained TN across the 0–60 cm profile. Among the treatments, T1 provided the most balanced performance, with a higher MWD and GMD, lower D, and improved aggregate-associated TC and TN retention. These findings suggest that rotational tillage with straw return, particularly the NT–NT–DT sequence, can support aggregate stability and is associated with improved aggregate-mediated TC and TN retention in the Loess Plateau dryland winter wheat system. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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21 pages, 6454 KB  

Open AccessArticle

Probabilistic Photovoltaic Power Forecasting with Reliable Uncertainty Quantification via Multi-Scale Temporal–Spatial Attention and Conformalized Quantile Regression

by Guanghu Wang, Yan Zhou, Yan Yan, Zhihan Zhou, Zikang Yang, Litao Dai and Junpeng Huang

Sustainability 2026, 18(2), 739; https://doi.org/10.3390/su18020739 - 11 Jan 2026

Cited by 13 | Viewed by 2255

Abstract

Accurate probabilistic forecasting of photovoltaic (PV) power generation is crucial for grid scheduling and renewable energy integration. However, existing approaches often produce prediction intervals with limited calibration accuracy, and the interdependence among meteorological variables is frequently overlooked. This study proposes a probabilistic forecasting [...] Read more.

Accurate probabilistic forecasting of photovoltaic (PV) power generation is crucial for grid scheduling and renewable energy integration. However, existing approaches often produce prediction intervals with limited calibration accuracy, and the interdependence among meteorological variables is frequently overlooked. This study proposes a probabilistic forecasting framework based on a Multi-scale Temporal–Spatial Attention Quantile Regression Network (MTSA-QRN) and an adaptive calibration mechanism to enhance uncertainty quantification and ensure statistically reliable prediction intervals. The framework employs a dual-pathway architecture: a temporal pathway combining Temporal Convolutional Networks (TCN) and multi-head self-attention to capture hierarchical temporal dependencies, and a spatial pathway based on Graph Attention Networks (GAT) to model nonlinear meteorological correlations. A learnable gated fusion mechanism adaptively integrates temporal–spatial representations, and weather-adaptive modules enhance robustness under diverse atmospheric conditions. Multi-quantile prediction intervals are calibrated using conformalized quantile regression to ensure reliable uncertainty coverage. Experiments on a real-world PV dataset (15 min resolution) demonstrate that the proposed method offers more accurate and sharper uncertainty estimates than competitive benchmarks, supporting risk-aware operational decision-making in power systems. Quantitative evaluation on a real-world 40 MW photovoltaic plant demonstrates that the proposed MTSA-QRN achieves a CRPS of 0.0400 before calibration, representing an improvement of over 55% compared with representative deep learning baselines such as Quantile-GRU, Quantile-LSTM, and Quantile-Transformer. After adaptive calibration, the proposed method attains a reliable empirical coverage close to the nominal level (PICP₉₀ = 0.9053), indicating effective uncertainty calibration. Although the calibrated prediction intervals become wider, the model maintains a competitive CRPS value (0.0453), striking a favorable balance between reliability and probabilistic accuracy. These results demonstrate the effectiveness of the proposed framework for reliable probabilistic photovoltaic power forecasting. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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28 pages, 1089 KB  

Open AccessReview

A Review of Geothermal–Solar Hybrid Power-Generation Systems

by Shuntao Hu, Jiali Liu, Xinli Lu and Wei Zhang

Energies 2025, 18(21), 5852; https://doi.org/10.3390/en18215852 - 6 Nov 2025

Cited by 3 | Viewed by 3466

Abstract

Hybrid geothermal–solar systems leverage complementary resources to enhance efficiency, dispatchability, and low-carbon supply. This review compares mainstream configurations (solar-preheating configurations, solar-superheating configuration, and other emerging concepts) and reports typical performance gains—thermal efficiency of 5–80% and exergy efficiency up to ~60%—observed across resource contexts. [...] Read more.

Hybrid geothermal–solar systems leverage complementary resources to enhance efficiency, dispatchability, and low-carbon supply. This review compares mainstream configurations (solar-preheating configurations, solar-superheating configuration, and other emerging concepts) and reports typical performance gains—thermal efficiency of 5–80% and exergy efficiency up to ~60%—observed across resource contexts. Findings indicate that preheating routes are generally preferable under medium direct normal irradiance (DNI) and operation-and-maintenance (O&M)-constrained conditions, while superheating routes become attractive at high DNI with thermal storage; integrated multigeneration systems can deliver system-level benefits for multi-energy parks and district applications. In addition, this paper identifies technical bottlenecks—source matching, storage dependence, and the absence of a unified evaluation—and summarizes control/optimization strategies, including emerging advanced artificial-intelligence algorithms. In addition, the review highlights a standardized comprehensive performance evaluation framework, which covers thermal and exergy efficiency, net power output, complexity, the levelized cost of electricity (LCOE), reliability, and storage. Finally, according to the research status and findings, future research directions are proposed, which pave the way for more effective exploitation of geothermal and solar energy. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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12 pages, 1959 KB  

Open AccessEditor’s ChoiceArticle

Assessment of Rice Productivity and Solar Power Generation in Agriphotovoltaic Systems

by Su-Min Yun, Deok-Gyeong Seong, Jeung Joo Lee and Jung-Sung Chung

Agriculture 2025, 15(16), 1741; https://doi.org/10.3390/agriculture15161741 - 14 Aug 2025

Cited by 2 | Viewed by 1687

Abstract

This study aims to evaluate the feasibility and benefits of integrating photovoltaic (APV) systems with rice cultivation, focusing on growth characteristics, chlorophyll content and fluorescence, yield components, and electricity production. An APV system was installed over a rice paddy area in Namhae-gun, Gyeongsangnam-do, [...] Read more.

This study aims to evaluate the feasibility and benefits of integrating photovoltaic (APV) systems with rice cultivation, focusing on growth characteristics, chlorophyll content and fluorescence, yield components, and electricity production. An APV system was installed over a rice paddy area in Namhae-gun, Gyeongsangnam-do, with 607 modules providing a total capacity of approximately 97.12 kW. The Baegokchal variety of rice was cultivated following standard practices, and growth characteristics, chlorophyll content, and fluorescence were measured throughout the growing period. Yield components were analyzed, and electricity production was monitored to assess the performance of the APV system. The rice growing period in 2021 experienced lower than average temperatures and higher rainfall. Despite these conditions, rice in the APV systems showed increased chlorophyll content and fluorescence, indicating an adaptive response to reduced sunlight. Rice plants in APV systems exhibited greater plant height but fewer tillers compared to the control group. Leaves were significantly longer and wider, enhancing photosynthetic efficiency under shading. The yield of rice in APV systems was reduced by approximately 9% compared to the control, less severe than that reported in other studies. The APV system demonstrated stable electricity production, with consistent output throughout the year, despite variations in solar radiation. Integrating photovoltaic systems with rice cultivation is feasible and beneficial, providing a reliable source of renewable energy and enhancing farm income despite a slight reduction in rice yield. This study highlights the potential of APV systems to contribute to sustainable agriculture and renewable energy expansion, suggesting the need for further research on various crops and conditions to optimize system performance. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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25 pages, 11601 KB  

Open AccessArticle

Experimental Analysis of Energy Savings in a Combined Rotary Desiccant Dehumidifier with a Purge Section

by Jeongsu Yang and YoungIl Kim

Sustainability 2025, 17(9), 4126; https://doi.org/10.3390/su17094126 - 2 May 2025

Cited by 2 | Viewed by 4647

Abstract

This study focuses on improving the performance of desiccant dehumidifiers using desiccant rotors, which are widely utilized in various industries, such as manufacturing, food, and construction, to enhance product quality and production efficiency. The combined desiccant dehumidifier can reduce energy consumption compared to [...] Read more.

This study focuses on improving the performance of desiccant dehumidifiers using desiccant rotors, which are widely utilized in various industries, such as manufacturing, food, and construction, to enhance product quality and production efficiency. The combined desiccant dehumidifier can reduce energy consumption compared to traditional standard or purge dehumidifiers. The system operates in normal mode during seasons with high outdoor humidity and in purge mode during seasons with low outdoor humidity. By utilizing dampers, the air passing through the dry desiccant rotor can either be directly discharged indoors or supplied to the regeneration section, allowing the system to operate in two modes within a single unit. The first part of the study involved comparing the performance of the equipment through experiments. The second part compared the results from the dehumidifier rotor performance simulation program to check for deviations and validate its effectiveness. In the first experiment, the energy consumption of the standard desiccant dehumidifier in normal mode was compared with that of the combined desiccant dehumidifier in normal mode. In the second experiment, the energy consumption of the standard desiccant dehumidifier in normal mode was compared with that of the combined desiccant dehumidifier in purge mode. The airflow, temperature, and humidity values used in each experiment were analyzed using a dehumidification performance simulation program, and the deviation was found to be within 10%. Therefore, the performance analysis via simulation was considered valid. The dehumidification performance of the combined desiccant dehumidifier was found to be 5% more efficient than the traditional standard desiccant dehumidifier and 9.5% more efficient than the purge dehumidifier. Furthermore, energy consumption simulations were conducted for representative regions in Korea. The results showed energy reductions of 65% in Seoul, 65% in Daejeon, and 67% in Busan. The findings of this study suggest that energy savings can be achieved by appropriately adjusting the operation mode between normal and purge modes based on outdoor conditions. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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15 pages, 16282 KB  

Open AccessArticle

Electroluminescence Imaging Based on FFT Analysis for Outdoor Photovoltaic Module Inspection: A Self-Powered Signal Modulation Approach

by Alberto Redondo-Plaza, Amy Zulema Velasco-Bonilla, José Ignacio Morales-Aragones, Ángel L. Zorita-Lamadrid, Víctor Alonso-Gómez and Luis Hernández-Callejo

Appl. Sci. 2025, 15(9), 4606; https://doi.org/10.3390/app15094606 - 22 Apr 2025

Cited by 7 | Viewed by 2891

Abstract

Electroluminescence imaging is increasingly used in photovoltaic power plant inspections due to its effectiveness in detecting various types of failures in solar cells. This article presents a novel technique that enables the modulation of an arbitrary electroluminescence signal in PV modules using an [...] Read more.

Electroluminescence imaging is increasingly used in photovoltaic power plant inspections due to its effectiveness in detecting various types of failures in solar cells. This article presents a novel technique that enables the modulation of an arbitrary electroluminescence signal in PV modules using an electronic device that controls the signal by modulating an arbitrary current waveform in a photovoltaic module, utilizing the string current as its energy source. As a result, measurements do not require a power supply and can be performed during the normal operation of a PV string. Throughout the paper, this method is compared to a more conventional approach that relies solely on a power supply to generate the current signal. Capturing a sequence of images while modulating the current with different waveforms allows the application of the Fast Fourier Transform to suppress background signals caused by sunlight, resulting in high-quality EL images. Experimental results demonstrate that the proposed method delivers imaging quality comparable to that achieved with a power supply, while effectively detecting a broad range of solar cell failures. Furthermore, the calculated signal-to-noise ratio for both approaches yields similar values, indicating comparable quality in quantitative terms. Finally, square wave modulation has shown slightly better performance than other waveforms, such as sinusoidal and half-sinusoidal modulation. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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19 pages, 17339 KB  

Open AccessArticle

Joint Probabilistic Forecasting of Wind and Solar Power Exploiting Spatiotemporal Complementarity

by Fahong Zhang, Zhiyuan Leng, Lu Chen and Yongchuan Zhang

Sustainability 2025, 17(8), 3584; https://doi.org/10.3390/su17083584 - 16 Apr 2025

Cited by 11 | Viewed by 3410

Abstract

Reliable and precise joint probabilistic forecasting of wind and solar power is crucial for optimizing renewable energy utilization and maintaining the safety and stability of modern power systems. This paper presents an innovative joint probabilistic forecasting model designed to address probabilistic spatiotemporal power [...] Read more.

Reliable and precise joint probabilistic forecasting of wind and solar power is crucial for optimizing renewable energy utilization and maintaining the safety and stability of modern power systems. This paper presents an innovative joint probabilistic forecasting model designed to address probabilistic spatiotemporal power output forecasting challenges. Leveraging a multi-network deep learning framework, the model integrated the temporal convolutional network for temporal feature extraction, the convolutional neural network for spatial feature analysis, and the attention mechanism for spatiotemporal focus enhancement, thereby capturing the spatiotemporal complementarity of wind and solar power. It also incorporated a quantile regression-based uncertainty quantification technique, contributing to reliable probabilistic predictions. A wind farm and two solar farms in China were used as a case study. Comparison results between the proposed model and ten established models demonstrated its superior performance in both reliable deterministic and probabilistic predictions, offering valuable insights for sustainable and resilient energy system operation. Full article

(This article belongs to the Topic Sustainable Energy Systems)

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