Search Results (230)

Urban areas significantly influence atmospheric flow fields and momentum exchange processes, which are relevant for wind energy applications and meso-scale atmospheric modeling. However, meso-scale simulations typically represent urban effects using surface roughness parameterizations that neglect volumetric momentum losses within the urban canopy layer. In this study, a methodology is presented to derive a volumetric urban canopy parameterization directly from building-resolved computational fluid dynamics (CFD) simulations. A detailed micro-scale CFD simulation of a real urban region is used to evaluate the momentum balance within a control volume surrounding the urban region. Based on this analysis, two key parameters are derived: the vertical distribution of the House Area Density (HAD), representing the geometric characteristics of the urban morphology, and an effective drag coefficient describing the momentum loss induced by the built environment. These parameters are subsequently implemented as volumetric source terms in a urban canopy model formulated analogously to plant canopy parameterizations. The resulting urban canopy model is validated by comparison with the fully resolved CFD simulation. The results show good agreement in the streamwise momentum balance and pressure loss distribution, while computational cost is significantly reduced. The proposed urban canopy model provides a physically consistent framework for representing urban momentum sinks in meso-scale flow simulations. Full article

With the large-scale grid integration of renewable energy sources such as wind power and photovoltaics, power system net load fluctuations have become significantly more severe, imposing higher demands on system flexibility. Traditional optimal transmission switching (OTS) models require the simultaneous optimization of continuous and discrete variables, resulting in high computational complexity that renders them unsuitable for daily real-time scheduling in large-scale power systems. This paper develops a flexible real-time rolling optimization scheduling model that incorporates OTS and proposes a two-stage fast solution framework based on deep neural networks (DNN). In the offline training phase, a multilayer perceptron-based DNN is trained using load and renewable generation data to rapidly and accurately predict the optimal line switching scheme. In the online application phase, the network topology predicted by the DNN transforms the original mixed-integer linear programming problem into a standard linear programming problem, substantially reducing computational complexity and solution time. Case studies on the modified IEEE 118-bus and IEEE 300-bus systems show that the proposed method achieves high prediction accuracy, reduces solution time by up to 117 times, and maintains nearly identical system operating costs to the physics-driven approach in the majority of cases. The results demonstrate that the proposed approach effectively balances computational efficiency and economic performance, verifying the practical value of optimal transmission switching in enhancing large-scale renewable energy accommodation and overall power system flexibility. Full article

The sustainable collaborative operation of multi-park integrated energy systems (MPIESs) with shared energy storage (SES) provides a significant pathway for low-carbon transition, renewable energy utilization, and energy efficiency improvement, thereby supporting regional energy sustainability. However, realizing this potential faces challenges, including source-load uncertainty, conflicts of interest among multiple entities, and the need for privacy-preserving distributed coordination. To address these issues, this paper proposes a distributed robust energy management strategy for MPIESs with SES, which is decomposed into two sub-problems. In the first sub-problem, a robust optimization model incorporating the SES leasing mechanism is established to handle the uncertainties of photovoltaic (PV) generation and loads. In the second sub-problem, a cooperative game model based on Nash bargaining theory is constructed to fairly allocate the cooperative surplus among participating parks. The alternating direction method of multipliers (ADMM) is employed to solve the overall model in a distributed manner, and enabling collaborative scheduling with limited information exchange. Case studies indicate that the proposed strategy reduces the total system operating cost by 17.57% compared to the independent operation mode. The benefit allocation mechanism achieves Pareto improvement and effectively mitigates the uneven distribution of cooperative surplus among parks. Furthermore, the distributed algorithm converges within 13 iterations in the test case, demonstrating good computational tractability. Consequently, the results verify the effectiveness of the proposed framework in balancing economy, fairness, and robustness, thereby promoting the low-carbon and sustainable operation of regional integrated energy systems. Full article

Traditional power and energy balance methods suffer from several limitations, such as inadequate coordination across long-term and short-term temporal scales, confinement to single-region spatial boundaries, and insufficient exploitation of multi-energy complementarity. This paper proposes a multi-temporal, multi-spatial power, and energy balance framework that integrates cross-regional energy sharing and leverages the complementarity among diverse power sources. A two-level feedback optimization model is formulated, coupling the medium- to- long-term energy balance with short-term power balance. The model comprehensively incorporates constraints, including the characteristics of various power sources, unit operating status, dynamic power flow on cross-regional tie-lines, as well as renewable energy curtailment minimization and power supply reliability requirements. This hierarchical structure enables coordination optimization across both the long-term and short-term temporal dimension and cross-regional mutual aid in the spatial dimension. A hierarchical solution strategy is employed, which integrates an improved particle swarm optimization algorithm with the Gurobi solver. Case studies on realistic power systems demonstrate that the proposed method effectively exploits the potential of multi-energy coordination and cross-regional mutual aid, achieving improved renewable energy accommodation, enhanced cross-regional resource utilization efficiency, and robust power and energy balance across multi-temporal and spatial scales. Full article

With the high penetration of distributed photovoltaics (PV) in distribution areas, transformer capacity limits and source–load fluctuations have become key factors constraining PV accommodation. To accurately assess the PV hosting capacity under energy storage regulation, this paper proposes an assessment method based on operating region analysis. First, a coordinated operation model for the distribution area is established, incorporating the transformer capacity, energy storage constraints, and power balance. On this basis, the calculation boundaries for the PV hosting capacity are discussed in two scenarios: Model 1 ignores power curve uncertainty, characterizing the geometry of the conventional operating region to find the maximum deterministic hosting capacity (S₁) that keeps the region non-empty. Model 2 introduces box-type uncertainty sets for the source and load, proposes the concept of a “Self-Balanced Operating Region”, and constructs a robust feasibility determination model (f₃) based on a Min–Max–Min structure. To solve this multi-layer nested non-convex model, an iterative algorithm based on duality theory and Benders decomposition is employed to determine the robust hosting capacity under uncertainty (S₂) at the critical point where f₃ shifts from zero to non-zero. Case studies show that source–load uncertainty leads to a significant contraction of the operating region, and the robust hosting capacity under uncertainty requirements is strictly less than the deterministic hosting capacity (S₁ > S₂). This method quantifies the reduction effect of uncertainty on the accommodation capability, providing a theoretical basis for planning high-renewable penetration distribution areas and energy storage configuration. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article

Achieving a balance between accuracy and computational efficiency in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Given the high computational cost of spectral models, a widely used simplification strategy consists of parameterizing atmospheric spectral transmittances through wavelength-averaging formulations. This study introduces a Clear-Sky Multivariable (CSMV) broadband parametric model derived from the Leckner spectral model for estimating the three components of solar irradiance under clear-sky conditions: direct normal irradiance (DNI), diffuse irradiance (Gd), and global irradiance (G). The model development follows a two-stage procedure. First, discrete broadband transmittances are obtained by applying an independent spectral integration scheme to the transmittances of the source spectral model. In the second stage, these discrete values are fitted with analytical functions expressed solely in terms of atmospheric state parameters, yielding wavelength-independent broadband formulations. While the overall development framework follows a classical parameterization approach, the calculation of the diffuse component introduces a novel way of estimating the fraction of aerosol scattering directed toward the ground. The model was tested against data collected from eight radiometric stations distributed across six continents and benchmarked against two well-established reference models. Overall, the results indicate a high level of accuracy and demonstrate the practical applicability of the model. Full article

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article

In ground-source heat-pump (GSHP) systems equipped with a single U-tube borehole heat exchanger (BHE), the heat-carrier fluid in the return leg may release heat to the surrounding ground in the shallow part of the borehole. From a fluid energy balance perspective, this is an exothermic process; however, it is detrimental during heating operation: It lowers the effective source temperature available to the heat pump and therefore degrades the overall coefficient of performance (COP). This study proposes a measurement-driven procedure to determine the exothermic transition depth $z_{\uparrow }^{*}$ from temperature profiles recorded at multiple depths along the ascending (return) pipe. The borehole is discretized into axial segments and, assuming a constant mass flow rate, the linear heat-exchange rate is estimated from the segment-wise enthalpy change. Time integration yields the segment-wise net energy exchange $Q_{\uparrow ,i}$, which is then classified as exothermic or endothermic using an uncertainty-based threshold derived from the standard uncertainty of the temperature sensors. The exothermic transition depth $z_{\uparrow }^{*}$ is defined as the first statistically stable sign change in the integrated segment energy (from exothermic to endothermic) and is obtained by linear interpolation between adjacent segment centres. By summing the exothermic energy exchange and the corresponding average loss power, an equivalent change in source-side outlet temperature $∆T_{out}$ is estimated and interpreted in terms of COP impact using a Carnot-scaled surrogate model. For two representative operating conditions, $z_{\uparrow }^{*}$ was found at $31.17 \mathrm{m}$ and $24.01 \mathrm{m}$, respectively, while the average exothermic loss power remained approximately 0.48 kW. The estimated $∆T_{out}$ ranged from $0.52$ to $0.75 \mathrm{K}$, corresponding to a diagnostic COP improvement if this parasitic exothermic exchange could be mitigated. The present results should therefore be interpreted as a case study-based demonstration of the method on one instrumented borehole rather than as a universal quantitative prediction for other sites or borehole fields. Full article

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ET_lys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature and biophysical inputs (e.g., NDVI and fractional cover) to derive spatially explicit ET, while AquaCrop provided continuous daily simulations between flight dates. Direct lysimeter-based validation indicated high agreement for AquaCrop (R² = 0.85; RMSE = 0.26 mm d⁻¹; MBE = 0.01 mm d⁻¹) and moderate agreement for UAV–TSEB (R² = 0.66; RMSE = 0.81 mm d⁻¹; MBE = 1.01 mm d⁻¹). Model intercomparison further showed consistent temporal dynamics of ET (R² = 0.70; RMSE = 1.35 mm d⁻¹) and robust partitioning of crop transpiration (R² = 0.79; RMSE = 0.99 mm d⁻¹) and soil evaporation (R² = 0.76; RMSE = 1.03 mm d⁻¹) while revealing a systematic divergence under near-complete canopy cover: AquaCrop tended to suppress evaporation, whereas UAV–TSEB detected residual evaporation from the flooded surface. Overall, the results highlight the complementarity of both approaches—UAV–TSEB as a spatial diagnostic tool and AquaCrop as a temporally continuous simulator—providing a robust framework for ET monitoring, flux partitioning, and water-use-efficiency assessment in water-scarce rice systems. Full article

In order to cope with the challenge of large-scale integration of renewable energy to the balance of power supply and demand, and give full play to the potential of flexible regulation of iron and steel enterprises, a source load coordination optimization scheduling model considering the flexible coordination of iron and steel production and energy storage is proposed. Firstly, the multi-unit coupling adjustable capacity model of electric arc furnace (EAF), air separation unit (ASU), rolling mill and captive power plant is established, and the regulation characteristics and coupling relationship between different production units are clarified. Secondly, a day-ahead and intra-day two-stage scheduling framework is proposed. In the intra-day stage, the energy storage system is introduced to mitigate the fluctuation in wind power, and the mixed integer linear programming method is adopted to minimize the total operating cost of the system. Finally, an example is given to verify the effectiveness of the model. Case studies demonstrate that the proposed approach effectively reduces load variability and enhances operational stability. After the introduction of energy storage, the power standard deviation of EAFs and ASUs decreases by 29.6% and 28%, respectively, and the operational continuity of the rolling process is improved. Although the initial wind curtailment level in the test system is relatively low, the proposed strategy further mitigates peak curtailment and improves renewable accommodation capability. In addition, moderate operational cost savings are achieved. Full article

Conventional material balance methods, typically based on single- or dual-porosity models solvable via single-step linearization, are inadequate for hydraulically fractured shale oil reservoirs due to their pronounced heterogeneity and contrasting interzonal connectivity. Specifically, dual-zone models fail to represent the realistic characteristics of shale oil reservoirs because they treat artificially created hydraulic fractures and natural fractures as equivalent, despite their substantially different properties. To address this gap, this paper proposes a novel three-zone conceptual model, segmenting the reservoir into the matrix zone (MZ), the Weakly Stimulated Zone (WSZ, low-conductivity zone), and the Strongly Stimulated Zone (SSZ, high-conductivity zone). A corresponding three-zone gas injection replenishment material balance model is developed. This model explicitly captures interactions between injected gas and formation fluids and incorporates dynamic variations in pore volume and fluid saturation induced by imbibition. To solve the complexities introduced by the triple-porosity system, a dedicated two-step linearization solution procedure is proposed. Utilizing conventional production performance and basic PVT data, the method enables simultaneous estimation of zone-specific developed reserves and prediction of the Estimated Ultimate Recovery (EUR) through a least squares algorithm. Validation against actual well cases and multi-well statistics confirms that the method provides stable and reliable zonal reserve characterization and EUR forecasting. The results indicate that the MZ contributes the majority of the geological reserves, accounting for >70%. The WSZ contributes approximately 29.5% of the reserves and serves as the primary source for energy replenishment in the shale oil reservoir. In contrast, the SSZ contributes less than 0.5% of the reserves but acts as the dominant channel for flow convergence, controlling the main fluid production pathways. The proposed framework not only offers a practical tool for refined reserve assessment in shale oil reservoirs but also provides a computational basis and decision support for the design and injection parameter optimization of pre-pad CO₂ energy storage fracturing schemes. Full article

This study presents an analysis of selected approaches to transforming the Polish power system towards a net-zero greenhouse gas (GHG) emission economy by 2060. The generation-side system models primarily comprise renewable energy sources (RES), supported by nuclear power plants. Two system balancing scenarios were examined: Model G, based on biomethane-fired gas turbines and electrolysers utilising surplus energy; and Model H, which relies primarily on reversible fuel cells (RFCs) operating in a Power-to-Power configuration. Both models were considered under two demographic projections for Poland in 2060: maintaining the current population level (100%) and a decline to 71%. Simulations were performed with an hourly time step over a nine-year period, starting from 2060, using weather data from 2015 to 2023. The total electricity demand in the analysed scenarios ranges from 352 to 542 TWh/year, representing 2.1–3.2 times the current level. The proposed systems include 64 GW of onshore wind capacity, 33 GW of offshore wind, 136 GW of PV, 10 GW of nuclear generation, and extensive storage systems for electricity, heat, and gases (biomethane and hydrogen). In Model G, biomethane and hydrogen storage play a crucial role, requiring storage capacities of 5.8–7.5 billion Nm³ for biomethane and 6.2–7.0 billion Nm³ for hydrogen. In Model H, long-term storage relies on hydrogen reservoirs (approximately 12.5 billion Nm³) integrated with RFC units. The results demonstrate that the choice of architecture dictates the scale and technical requirements of the storage infrastructure. Notably, hydrogen serves as an effective energy storage medium, enabling the elimination of peak gas turbines from the system. Consequently, biomethane resources can be redirected to support the decarbonisation of other sectors of the economy. Full article

Currently, distribution system scheduling faces significant uncertainty and dynamic complexity due to the large-scale integration of diverse heterogeneous entities, while conventional approaches suffer from limited capability in modeling user behavior responses and ensuring dispatch accuracy, making them inadequate for source–grid–load–storage collaborative optimization. To address this, this paper proposes a data-driven multi-scale coordinated scheduling method for distribution systems, in which distributed generation outputs, load responses, and energy storage states are extracted and modeled using an improved exponential smoothing technique; a hierarchical and time-divided optimization framework is then developed by combining machine learning and probabilistic modeling with spatial correlation analysis to enhance renewable generation and load forecasting accuracy; and finally, a two-stage robust optimization model considering scenario uncertainties is established through typical scenario generation and uncertainty set constraints to achieve dispatch strategies that balance economic efficiency and low-carbon objectives and supply reliability under fluctuating renewable outputs and dynamic load variations. Simulation results demonstrate that the proposed method reduces total operating cost by 16.4%, decreases carbon emissions by 10.7%, and lowers electricity purchase fluctuation by 8.75%, thereby significantly enhancing system flexibility and adaptability to renewable energy uncertainties and providing a novel pathway for the development of active and intelligent distribution systems. Full article

Evapotranspiration (ET) links the water cycle with the energy balance and serves as a key driving process for ecosystem functioning and water resource management. Canopy conductance (Gc) plays a central role in regulating transpiration, but many models inadequately represent its regulatory mechanisms and show varying applicability across different land cover types. This study develops a remote-sensing ET estimation approach suitable for large scales and diverse land cover types and proposes an improved canopy conductance model for daily latent heat flux (LE) estimation. By integrating the canopy radiation transfer concept from the K95 model into the multiplicative Jarvis framework, an improved canopy conductance model is developed that includes limiting effects from photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T), and soil moisture (θ). Eighteen combinations of limiting functions are designed to evaluate structural performance differences. Using observations from 79 global flux sites during 2015–2023 and integrating multi-source datasets, including ERA5, MODIS, and SMAP, a two-stage parameter optimization was applied to determine the optimal limiting function combination for each land cover type. And nine sites from nine different land cover types were selected for independent spatial validation. Temporal validation within the optimization sites shows that, at the daily scale, the model achieves a Kling–Gupta efficiency (KGE) of 0.82, a correlation coefficient (R) of 0.82, and a Root Mean Square Error (RMSE) of 27.83 W/m², demonstrating strong temporal stability. Spatial validation over independent holdout sites achieved KGE = 0.84, R = 0.84, and RMSE = 22.53 W/m². At the 8-day scale, when evaluated over the holdout sites, the model achieves KGE = 0.87, R = 0.88, and RMSE = 18.74 W/m². Compared with the K95 and Jarvis models, KGE increases by about 34% and 15%, while RMSE decreases by about 38% and 12%, respectively. Relative to the MOD16 and PML-V2 products, KGE increases by about 32% and 16%, while RMSE decreases by about 33% and 17%, respectively. Comprehensive comparisons show that explicitly coupling canopy structure with multiple environmental constraints within the Jarvis framework, together with structure optimization across land cover types, can markedly improve large-scale remote-sensing ET retrieval accuracy while maintaining physical consistency and physiological rationality. This provides an effective pathway and parameterization scheme for producing ET products applicable across ecosystems. Full article