Search Results (1,807)

This study investigates the incorporation of a standardised flexibility protocol within a physics-based models to enable controllable demand-side flexibility in residential energy systems. A heating subsystem is developed using MATLAB/Simulink and Simscape, serving as a testbed for protocol-driven control within a Multi-Energy System (MES). A conventional thermostat controller is first established, followed by the implementation of an OpenADR event engine in Stateflow. Simulations conducted under consistent boundary conditions reveal that protocol-enabled control enhances system performance in several respects. It maintains a more stable and pronounced indoor–outdoor temperature differential, thereby improving thermal comfort. It also reduces fuel consumption by curtailing or shifting heat output during demand-response events, while remaining within acceptable comfort limits. Additionally, it improves operational stability by dampening high-frequency fluctuations in mdot_fuel. The resulting co-simulation pipeline offers a modular and reproducible framework for analysing the propagation of grid-level signals to device-level actions. The research contributes a simulation-ready architecture that couples standardised demand-response signalling with a physics-based MES model, alongside quantitative evidence that protocol-compliant actuation can deliver comfort-preserving flexibility in residential heating. The framework is readily extensible to other energy assets, such as cooling systems, electric vehicle charging, and combined heat and power (CHP), and is adaptable to additional protocols, thereby supporting future cross-vector investigations into digitally enabled energy flexibility. Full article

Multimodal electric transport systems demand substantial active and reactive energy, making power-quality management essential for ensuring efficient and reliable operation. This paper analyses reactive-energy transport in mass-transit networks and introduces a unified current-based framework that enables a consistent interpretation of the conventional power factor under harmonic distortion, fundamental unbalance, and short-term load fluctuation, without modifying its original definition. The framework enables a consistent assessment of compensation needs, independent of billing schemes, and is aligned with the way modern compensation equipment is specified and controlled. Applied to the Metro de Medellín system, field measurements and digital simulations show that traditional reactive-energy limits fail to distinguish between harmful and beneficial operating conditions, leading to disproportionate charges under the former Colombian regulation. Beyond this case, the proposed framework is directly applicable to other electric-mobility systems—including railways, trams, trolleybuses, and electric-bus networks—providing clearer technical signals for compensation planning and offering a comprehensive basis for future regulatory approaches that integrate multiple power-quality phenomena. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

Amidst the increasing aerial traffic and road traffic congestion, Urban Air Mobility (UAM) has emerged as a new mode of aerial transport offering less travel time and ease of portability. A critical factor in reducing travel time is the emerging electric Vertical Take-Off and Landing (eVTOL) vehicles, which require infrastructure such as vertiports to operate smoothly. However, the dynamics of vertiport operations, particularly the integration of battery charging facilities, remain relatively unexplored. This work aims to bridge this gap by delving into vertiport management by utilizing separate taxing and parking levels. The study also focuses on the time eVTOLs spend at the vertiport to anticipate potential delays. This factor helps optimise arrival and departure times via a scheduling strategy that accounts for hourly demand fluctuations. The simulation results, conducted with hourly demand, underscore the significant impact of battery charging on operational time while also highlighting the role of parking spots in augmenting capacity and facilitating more efficient scheduling. Full article

Investigating the spatio-temporal differentiation patterns and driving factors of ecosystem services (ESs) supply and demand is of great significance for early warning of ecosystem imbalance risks and identifying regional natural resource supply–demand conflicts. This study takes the typical coal-grain overlapping area (CGOA) in Eastern China as the research object, dividing it into mining townships (MT) and non-mining townships (NMT) for comparative analysis. By integrating the InVEST model, ESs supply–demand ratio (ESDR) index, four-quadrant model, and the XGBoost-SHAP algorithm, the study systematically reveals the spatiotemporal differentiation characteristics and driving mechanisms of ESs supply and demand from 2000 to 2020. The results indicated that: (1) grain production (GP) service maintained a continuous supply–demand surplus, with the ESDR of NMT areas surpassing that of MT areas in 2020. The ESDR of water yield (WY) service was significantly influenced by interannual fluctuations in supply, showing deficits in multiple years. The decline in carbon sequestration (CS) service and sharp increase in carbon emissions led to a continuous decrease in the ESDR of CS service, with MT areas facing a higher risk of carbon deficit. (2) The spatial heterogeneity of ESs supply and demand was significant, with GP and CS services exhibiting a typical urban-rural dual spatial structure, and the overall region was dominated by the Type Ⅱ ESs supply–demand matching (ESDM) pattern. The ESDR of WY service generally decreases from Southeast to Northwest across the region. with the Type Ⅳ ESDM pattern dominating in most years. (3) Human activities are the core driving force shaping the supply–demand patterns of ESs. Among these, land use intensity exhibits a nonlinear effect, high population density demonstrates an inhibitory effect, and MT areas are more significantly affected by coal mining subsidence. Natural environmental factors primarily drive WY service. The research findings can provide a scientific reference for the coordinated allocation of regional natural resources and the sustainable development of the human–land system. Full article

Interim periods between peak and off-peak operations in urban rail transit networks often suffer from mismatched headways across lines, which increases passenger transfer waiting and operating costs. This paper proposes a passenger-oriented timetable synchronization method for network-wide interim period train service. In this study, based on the AFC data, passengers are assigned to the shortest travel time paths, and passenger transfer flows are linked to connecting train pairs by consideration of the maximum acceptable waiting time. As a result, the transfer waiting time is accurately calculated by matching passengers’ platform arrival times with the departures of feasible connecting trains. A mixed integer nonlinear programming model then jointly optimizes departure headways at each line’s first station, arrival and departure times at transfer stations, subject to safety headways and time bounds. The objective minimizes total cost, combining transfer waiting time cost and train operating cost (depreciation and distance-related cost). A simulated-annealing-based genetic algorithm (SA-GA) is designed to solve the NP-hard problem. A case study on the Nanjing rail transit network from 6:30 to 7:30 reduces total cost by 6.88%, including 3.77% lower transfer waiting time cost and 14.49% lower operating cost, and shows stable results under typical transfer demand fluctuations. Full article

In the modern era, demand forecasting enhances the decision-making tasks of industries for controlling production planning and reducing inventory costs. However, the dynamic nature of the fashion and apparel retail industry necessitates precise demand forecasting to optimize supply chain operations and meet customer expectations. Consequently, this research proposes the Formicary Zebra Optimization-Based Distributed Attention-Guided Convolutional Recurrent Neural Network (FZ-DACR) model for improving the demand forecasting. In the proposed approach, the combination of the Formicary Zebra Optimization and Distributed Attention mechanism enabled deep learning architectures to assist in capturing the complex patterns of the retail sales data. Specifically, the neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate extracting the local features and temporal dependencies to analyze the volatile demand patterns. Furthermore, the proposed model integrates visual and textual data to enhance forecasting accuracy. By leveraging the adaptive optimization capabilities of the Formicary Zebra Algorithm, the proposed model effectively extracts features from product images and historical sales data while addressing the complexities of volatile demand patterns. Based on extensive experimental analysis of the proposed model using diverse datasets, the FZ-DACR model achieves superior performance, with minimum error values including MAE of 1.34, MSE of 4.7, RMS of 2.17, and R² of 93.3% using the DRESS dataset. Moreover, the findings highlight the ability of the proposed model in managing the fluctuating trends and supporting inventory and pricing strategies effectively. This innovative approach has significant implications for retailers, enabling more agile supply chains and improved decision making in a highly competitive market. Full article

This article analyzes the case of a typical modern residential area, which was built following current legal regulations in Poland. For the purposes of the calculations, a housing estate consisting of 32 houses was assumed, with a connection power of 36 kW each. The three variants evaluate power consumption and photovoltaic system operation: Variant I assumes no PV installations and fluctuating consumer power demands; Variant II involves PV installations in all estate buildings with a total capacity matching the building’s 36 kW connection power and minimal consumption; and Variant III increases installed PV capacity per building to 50 kW, aligning with apartment connection powers, also with minimal consumption. The simulations performed indicated that there may be problems with voltage levels and current overloads of network elements. Although in case I the transformer worked properly, after connecting the PV installation in an extreme case, it was overloaded by about 117% (Variant II) or even about 180% (Variant III). The described case illustrates the impact of changes in regulations on the stability of the electricity distribution network. A potential solution to this problem is to oversize the distribution network elements, introduce power restrictions for PV installations or to oblige prosumers to install energy storage facilities. Full article

Ice-and-snow tourism cities face pronounced seasonal fluctuations that place strong pressure on urban accommodation systems. Understanding the spatial distribution, accessibility, and service capacity of hotels is therefore critical for sustainable tourism management in cold-region cities. Taking Harbin, China, as a representative winter tourism destination, this study develops a GIS-based spatial analytical framework to examine the spatial organization and service performance of star-rated hotels. Using data from 553 three-star and above hotels, combined with questionnaire survey data (N = 224), we apply the Nearest Neighbor Index (NNI), Kernel Density Estimation (KDE), and raster-based cost-distance accessibility analysis to identify spatial clustering patterns, accessibility differentiation, and mismatches between hotel supply and peak seasonal demand. We find that available hotel rooms can only meet about 60% of peak-season demand, indicating a severe capacity deficit. The results reveal a clear core–periphery spatial structure of star-rated hotels, significant accessibility disparities among hotel categories, and a pronounced mismatch between accommodation capacity and tourism demand during peak winter seasons. Peripheral areas exhibit limited accessibility and insufficient service capacity, while central districts experience high concentration and pressure. These findings highlight the importance of integrating spatial equity and seasonal demand considerations into accommodation planning and infrastructure optimization, providing policy-relevant insights for sustainable tourism development in cold-region cities. Full article

Root anatomy serves as a critical indicator for understanding wetland plant adaptation strategies to environmental changes. Since water depth determines root oxygen demand while nitrogen addition regulates nutrient acquisition, the two factors exert significant and interactive effects on root anatomical structure. In this study, we established a controlled experiment employing three water depth treatments (W1: −10 cm; W2: 10 cm; W3: 30 cm), two nitrogen (N) forms (ammonium-N, nitrate-N), and four N addition levels (N0: 0 mg/L; N1: 40 mg/L; N2: 80 mg/L; N3: 160 mg/L). This design enabled us to analyze the effects of water–nitrogen interactions on the anatomical structure of reed roots to reveal wetland plants’ adaptive strategies to water-nitrogen fluctuations. The results indicate that (1) under nitrogen-free treatment, compared to the control group, the W1 treatment reduced the root aerenchyma proportion and the stele-to-root diameter ratio by 15.8% and 37.0%, respectively. In contrast, exodermis thickness increased by 32.4%, while epidermis thickness decreased by 33.7%. Under the W3 treatment, the aerenchyma proportion increased by 21.0%, the stele-to-root diameter ratio decreased by 22.2%, and exodermis thickness increased by 35.3%. (2) Compared to the nitrogen-free treatment, nitrate addition increased the root aerenchyma proportion under W1, W2, and W3 by 18.8%, 6.9%, and 18.3%. The stele-to-root diameter ratio increased by 27.9% and 12.7% under W1 and W2, but decreased by 10.8% under W3. Exodermis thickness increased by 26.3% under W2, whereas it decreased by 10.8% under W3. Epidermis thickness increased by 36.1% and 22.2% under W1 and W3, while a decrease of 12.7% occurred under W2. (3) Compared to the nitrogen-free treatment, ammonium addition increased the root aerenchyma proportion under W1, W2, and W3 by 13.6%, 13.2%, and 10.0%. The stele-to-root diameter ratio increased by 28.1% under W1 but decreased by 10.4% under W3. Conversely, exodermis thickness decreased by 20.2% under W1 while increasing by 12.6% under W3. Epidermis thickness increased by 26.3% and 20.8% under the W1 and W3 treatments. In summary, the root anatomical structure of P. australis adaptively responds to variations in water depth, nitrogen forms, and nitrogen concentrations by modulating aerenchyma proportion, the stele-to-root diameter ratio, exodermis thickness, and epidermis thickness. Future research should strengthen the study of the relationship between root anatomical traits and plant functions, to more comprehensively explore the adaptation mechanisms of wetland plants to global environmental change. Full article

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R² of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R² ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R² ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation. Full article

To address the critical demand for high-gain, low-sidelobe, and high-efficiency antennas in W-band arrays, this work presents a low-sidelobe all-metal array antenna based on ridge gap waveguide technology. The design employs a three-layer contactless metal structure, integrating a stepped-ridge feeding network with Taylor amplitude distribution and a higher-order mode resonant cavity. This integration enables efficient power distribution and low-loss transmission while eliminating the need for conventional welding or bonding processes. Measurement results indicate that the antenna exhibits a reflection coefficient below −10 dB across the 92.5–103.5 GHz. The in-band gain exceeds 25.8 dBi with less than 1 dB fluctuation, and the radiation efficiency surpasses 78%. Specifically, the sidelobe levels in both E- and H-planes remain below −17.5 dB, reaching under −19.5 dB at 94 GHz, while cross-polarization is better than −30 dB. The proposed antenna demonstrates high gain, low sidelobe, and high efficiency, showing promising potential for applications in millimeter-wave radar, imaging, and 6G communication systems. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

The natural gas sector, as a pivotal transition fuel, is fundamentally constrained by the “Energy Trilemma”—the intertwined and often competing goals of energy security, affordability, and sustainability. Current research predominantly focuses on the demand side, leaving a significant gap in understanding the synergistic dynamics within production regions, which are critical to resolving this trilemma at its source. To address this gap, this study constructs a “Safety–Economy–Green” (S-E-G) evaluation framework aligned with the trilemma’s dimensions. Utilizing panel data (2011–2021) from four major Chinese natural gas production regions (Sichuan, Chongqing, Shaanxi, and Shanxi). By integrating the Entropy Weight Method, a Coupling Coordination Model, and Kernel Density Estimation, it delineates the system’s synergistic dynamics from both temporal and regional perspectives. The key findings are as follows: (1) Significant disparities and polarization are observed in the S and G dimensions, while the E dimension shows a narrowing gap, with its peak height increasing by 177.8% and bandwidth shrinking by 64.2%. G has emerged as a constraint on overall system coupling coordination. The persistently high coupling degree—rising from 0.87 in 2011 to 0.97 in 2021 while consistently exceeding the coordination degree, which increased from 0.45 to 0.62—underscores the continued need for improvement in synergistic development. (2) The coupling coordination degree of the S-E-G system underwent a three-stage evolution: rapid improvement (2011–2013, from 0.36 to 0.58 at 7.3% annually), fluctuating adjustment (2014–2017, between 0.58 and 0.66), and finally high-level stability (2018–2021, stabilizing at 0.76–0.80). (3) Obvious regional differentiation exists: Sichuan achieved a moderate level of 0.76 by 2021, Shaanxi maintained primary coupling coordination (0.6–0.7), while Chongqing and Shanxi remained marginal, fluctuating between 0.4 and 0.6. Enhancing subsystem coordination and implementing differentiated pathways are therefore essential for these regions’ sustainable development. The study suggests promoting the sustainable development of natural gas production regions by enhancing subsystem coordination and exploring differentiated pathways, thereby providing practical guidance for the energy transition of resource-based regions. Full article