Search Results (396)

This study presents a technical assessment of solar energy systems for integrated agricultural use and rural electrification. A model village comprising 30 households was considered, and high-resolution hourly load profiles were developed to characterize consumption dynamics, including peak demand and sectoral distribution across residential, agricultural, public, healthcare, and commercial users. A 60 kW photovoltaic (PV) system was designed in conjunction with an independent solar thermal installation for hot water supply. The system configuration was established through component sizing and numerical modeling, incorporating heat transfer mechanisms and operational constraints. Time-dependent simulations performed in MATLAB (R2022b) evaluated PV power output, battery storage cycling, and thermal system performance over a 24-h horizon. A comparative analysis of standalone PV, hybrid PV/T, and decoupled PV–thermal configurations was conducted based on performance and operational criteria. The results indicate that separated electrical and thermal subsystems achieve improved cost-effectiveness, enhanced reliability, and reduced maintenance requirements. The proposed approach demonstrates the technical viability of solar-based energy systems for rural applications, supporting energy autonomy, reduced fossil fuel dependence, and sustainable agricultural development. Full article

Nonlinear kickers (NLKs) enable off-axis injection in ultralow-emittance storage rings by providing a strong kick to the injected beam while remaining nearly transparent to the stored beam. In hybrid schemes, a conventional four-kicker bump defines the injected trajectory, and the NLK reduces the first-turn action under constrained beam offset and optics conditions. Effective operation additionally requires stable and reproducible first-turn injection trajectories. We develop a compact action–angle framework that expresses NLK dynamics in terms of Courant–Snyder invariants and yields an analytical bound on achievable action reduction. This formulation provides direct design rules for NLK placement, phase advance, injected-beam offset, and kicker field profile. Within this framework, we identify the 8-wire NLK as a practical baseline and extend its design by relaxing the square-geometry constraint, enabling inward shifting of the off-axis field peak while preserving on-axis field and gradient cancellation. Application to the NSLS-II lattice shows how aperture, pulsed-power, and mechanical constraints combine to determine a coupled design solution. Multi-turn tracking confirms that candidate NLK locations maintain sufficient stay-clear (aperture-clearance) margin, while the optimized wire geometry reduces the required current and Lorentz force load. The results establish a unified approach for NLK-assisted injection design and provide a practical pathway for upgrades in diffraction-limited storage rings. Full article

Grid-connected photovoltaic–battery energy storage–electric vehicle (PV-BESS-EV) charging stations require supervisory energy management that can coordinate tariff response, carbon-intensity signals, peak constraints, storage utilization, and converter-level operability within a transparent evidential framework. This study develops a bounded-reference rule-based supervisory energy management system (RB-SEMS) that preserves lower-level local converter controllers while generating operating modes and saturated reference commands for BESS power, grid exchange, and EV charging limits. A dual-time-scale evaluation framework is established by combining short-time switching/control simulations for dynamic traceability and SOC-sensitive protection with 24 h, 15 min EMS-level energy-balance simulations for cost, carbon, peak, PV utilization, EV service, and storage throughput assessment. Selected daily reference-injection cases are retained as copied-model diagnostic checks rather than as full-day switching-level validation. Under the D4-LSOC condition, RB-SEMS reduces the reported post-startup DC-bus deviation from 46.13 V to 40.60 V and the filtered BESS peak from 269.18 kW to 84.42 kW. In the E1-TOU scenario, E1-TOU-cost reduces daily total cost from 623.57 CNY to 564.05 CNY, lowers peak-period grid import from 183.75 kWh to 126.75 kWh, and increases local PV utilization from 71.13% to 78.71%; E1-PC66 further reduces the maximum 15 min grid import from 77.88 kW to 66.00 kW. Under the prescribed E2-PCC scenario, E2-CP reduces the calculated grid-related CO₂ emissions from 550.29 kg to 500.42 kg, whereas the price-only diagnostic increases them to 572.29 kg. Same-metric PV-SC and MILP comparisons, tested-range sensitivity analysis, and a throughput-based degradation proxy clarify that RB-SEMS is an interpretable supervisory baseline for cost–carbon–peak–cycling trade-off analysis rather than a cost-optimal controller or regionally validated proof of carbon reduction. Full article

This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not only due to the spectral overlap between hovering targets and clutter but also because of the visual disappearance of micro-Doppler features under heavy noise. The framework consists of three sequential modules. A prior-template orthogonal projection (PTOP) module suppresses clutter via a single-step orthogonal projection, preserving the micro-Doppler flash signature without distortion while approximately maintaining the Gaussian noise statistics required for subsequent detection. A flash power spectrum construction module then collapses the periodic blade flash energy onto a sharp spectral peak in a one-dimensional (1D) power spectrum via Gabor transform, power projection, and fast Fourier transform (FFT). A cell-averaging constant false alarm rate (CA-CFAR) detection module with an analytically derived threshold factor finally renders a reliable detection decision. Simulations under a signal-to-clutter ratio (SCR) of $-21$ dB and signal-to-noise ratio (SNR) of $-23$ dB confirm that the proposed framework achieves reliable detection even when the micro-Doppler flash signatures are visually obscured by residual noise in the time–frequency domain. Parametric SNR sweep curves and a two-dimensional (2D) SCR–SNR detection-probability heatmap under a non-stationary clutter model further quantify the practical performance boundaries of the framework. By transforming these concealed periodic features into a sharp spectral peak, the framework provides robust detection performance where conventional range-Doppler and moving target indication (MTI)-based methods both exhibit severe performance degradation. Full article

The integration of high-penetration renewable energy creates new requirements for cross-timescale peak shaving and for system robustness under extreme meteorological conditions. This study develops a dual-timescale capacity allocation method for pumped hydro storage (PHS), combining 8760 h chronological production simulation with monthly typical-day retrospective analysis. The model represents the operating limits of conventional units, nuclear power, hydropower, wind power, photovoltaic generation, tie-line exchange, and PHS energy shifting. On this basis, a stepwise capacity-sensitivity framework is established to minimize annualized comprehensive system cost while controlling renewable energy curtailment within a predefined planning threshold, rather than treating zero curtailment as an unconditional monthly hard constraint. Using long-term planning data from a coastal provincial power grid in southeastern China, the study compares the 2035 and 2040 planning scenarios. The results show that isolated typical-day models tend to overestimate PHS requirements because they disconnect chronological continuity and cross-day reservoir buffering. In 2035, the system presents a two-level seasonal capacity structure: 15,000 MW can support normalized operation in stable months, whereas the rigid boundary rises to 19,000 MW under extreme autumn high-wind conditions. In 2040, wind and photovoltaic capacity increase by approximately 20.01 GW compared with 2035, deepening low-net-load valleys and compressing seasonal regulation margins. Under the assumed planning boundary, the recommended PHS capacity converges to 23,000 MW. The proposed framework provides a practical reference for flexible resource planning in coastal power grids with deep renewable energy integration. Full article

Accurate and scalable urban land cover mapping is critical for sustainable urban planning and environmental management. While deep learning models offer powerful tools for this task, their performance is often constrained by the need for vast, manually labeled datasets, which are costly and challenging to acquire for diverse urban environments. To address this limitation, we propose the Cross-Scale U-Net, an original, highly adaptable operational framework that systematically exploits the inherent scale effects of remote-sensing imagery to optimize transfer learning. By operationalizing prior theoretical findings on receptive fields, this workflow provides an actionable method for users to manipulate spatial resolution, identify an optimal scale to bridge the domain gap, and subsequently automate feature extraction with significantly reduced manual effort. Using the well-annotated ISPRS Potsdam dataset as the source domain, our framework transfers learned knowledge to classify National Agriculture Imagery Program (NAIP) data from Phoenix, AZ (2015), into four primary land cover classes. We systematically evaluated the framework’s performance across spatial resolutions ranging from 15 cm to 100 cm, achieving a peak overall accuracy (OA) of 82.45%. To assess generalizability, the model was applied in a label-free transfer scenario to NAIP imagery from Las Vegas, NV (2015), and Phoenix, AZ (2013 and 2019), consistently delivering OA values above 70%. In a comparative analysis, the Cross-Scale U-Net significantly outperformed traditional classification techniques. While our current empirical validation is focused on arid urban environments due to experimental constraints, the framework introduces a highly flexible, actionable scale-adjustment process. This approach offers a scalable workflow that can be tailored to various landscape scales—such as expanding to coarser resolutions for large-scale forests or protected areas—delivering high-fidelity maps while mitigating data scarcity. Full article

Thermal regulation of lithium-ion (Li-ion) battery modules is a critical constraint for electric vehicle (EV) safety and durability, particularly during high-C-rate operation. Phase change materials (PCMs) have emerged as promising passive solutions due to their latent heat storage capability; however, current literature is heavily biased toward organic paraffin-based systems and lacks structured benchmarking across PCM categories and integration architectures. This review provides a systematic comparative assessment of PCM-based battery thermal management systems (BTMSs) comprising organic, inorganic, and eutectic materials under EV-relevant discharge conditions. The review is structured according to the conventional classification of PCMs; however, the available literature is predominantly focused on organic materials, particularly paraffin-based PCMs, leading to greater depth of analysis for this category. Thermophysical properties are analyzed in conjunction with discharge rate, module configuration, and hybrid cooling strategies. The results indicate that peak temperature mitigation is weakly correlated with latent heat magnitude when thermal conductivity remains below critical values. Conductivity-enhanced composites incorporating expanded graphite or metal foams significantly improve heat diffusion, reducing hotspot intensity and inter-cell temperature gradients under medium-to-high C-rates. Pure passive PCM systems exhibit thermodynamic limitations during sustained high-power operation due to saturation effects, underscoring the need for hybrid architectures for continuous heat rejection. This work establishes a structured benchmarking framework and demonstrates that effective thermal conductivity, integration strategy, and discharge-dependent design dominate BTMS performance over latent heat alone. The findings also reveal that inorganic and eutectic PCM-based BTMSs remain comparatively less explored in the literature, particularly at the battery module level and under realistic electric vehicle operating conditions, highlighting opportunities for future research. Full article

The increasing penetration of wind generation into autonomous and weakly coupled industrial microgrids requires control strategies that can maintain power-supply reliability under stochastic generation and sharply variable loads. This paper proposes an adaptive corridor-based supervisory control algorithm for a lithium-ion battery energy storage system (BESS) integrated with a wind-turbine generator. The novelty of the method is not the general use of battery storage for power smoothing but a control law that maintains the generator within a predefined active-power corridor while transferring fast and medium-duration imbalances to the battery under state-of-charge, power-limit, and response-delay constraints. Unlike PI-based smoothing, model predictive control, or fixed rule-based switching, the proposed approach uses corridor retention as the primary operating criterion and relies only on directly measurable variables. The model was implemented in MATLAB/Simulink for a 2 MW wind-turbine generator coupled with a 444 kWh/1776 kW lithium-ion battery energy storage system. Field-measurement-based simulation validation was performed in MATLAB/Simulink using industrial load data measured at an autonomous oilfield power plant; the validation scenarios included extracted step disturbances, a real multi-peak load profile, prolonged deficit operation, and a scaled configuration scenario. The algorithm compensated for 86.3–87.4% of short-term load peaks, reduced the standard deviation of generator power from 467 to 98 kW, and decreased recovery time from 6.8 to 1.6 s. Full article

With the ongoing trend of miniaturization and intelligent power transmission equipment, the compact design of environmentally friendly gas-insulated switchgear (GIS) has emerged as a critical technical challenge. This study presents a detailed case study of a 40.5 kV dry air-insulated switchgear under specific dimensional constraints. Specifically, the cabinet width was reduced from 1000 mm to 800 mm, significantly narrowing the phase-to-phase and phase-to-ground clearances. A high-fidelity three-dimensional electric field model was established using the finite element method to evaluate the dielectric stress distribution within the enclosure. Numerical results indicate pronounced electric field concentrations at critical regions—including copper busbar joints, disconnector contacts, and the inlet bushing shielding rings—where local intensities exceeded the insulation safety threshold. To mitigate these issues, integrated design refinement strategies were evaluated, encompassing the structural modification of shielding rings, the application of silicone rubber coatings, and insulation reinforcement via heat-shrinkable tubing. Comparative analysis and experimental results demonstrate that the refined configuration effectively suppressed the peak electric field intensity. Finally, the design was validated through comprehensive dielectric tests, including a 215 kV lightning impulse withstand voltage test. This work may offer useful engineering references and quantitative data for the ultra-compact design of eco-friendly switchgear under similar constraints. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article

Demand-side management plays a critical role in the secure and efficient operation of smart grids. Traditional real-time pricing generally takes social welfare maximization as the only objective, while ignoring the benefit balance among electricity suppliers, grid company and users. This will lead to uneven benefit distribution among stakeholders and impair the long-term stable operation of power systems. To solve this problem, a bilevel real-time pricing strategy based on tripartite welfare equilibrium is proposed in this paper. The upper-level model minimizes the welfare differences among electricity suppliers, grid company and users to ensure fair benefit allocation, and the lower-level model maximizes the total social welfare so as to guarantee the economic efficiency of the system. The model adopts different utility functions for residential and industrial users to describe user heterogeneity. By using the Karush–Kuhn–Tucker conditions, the original bilevel model is transformed into a single-level optimization problem with complementarity constraints. The CHKS smoothing function and pseudo-Huber function are introduced to deal with complementarity constraints and absolute-value objective functions respectively. Combined with the central difference method, a modified rolling penalty function algorithm is developed for numerical solution. The 24 h simulation results show that the prices of four time periods converge steadily to equilibrium values as iterations proceed. Compared with the total social welfare maximization model, the proposed bilevel model effectively reduces the peak-to-average load ratio. It reduces the welfare disparities among the three stakeholders while maintaining the total social welfare at a stable level. Furthermore, it still maintains excellent applicability and robustness when the user scale is expanded. Full article

There are significant difficulties with power quality and stability as a result of active cooperation between renewable energy sources and load demand. To maintain power stability between renewable energy supplies and the microgrid/utility grid, novel solutions must be implemented. By using an artificial and computational intelligence controller to schedule power from multiple sources (photovoltaic, wind, grid, and battery) under a set of constraints, such as weather, load-shedding hours, and peak pricing hours, this paper introduces a novel approach for power management in grid-connected hybrid renewable systems with PV–wind and energy storage systems. The approach involves using an artificial neural network (ANN) to process all of the inputs and creating an ANN rule set from a modelled hybrid renewable system. A rule-based power scheduler is developed, and simulations are run for a full day. The suggested fuzzy control approach can detect ongoing variations in grid load-shedding patterns, PV–wind power generation, load demands, and battery state-of-charge to enable prompt and accurate decision-making. The proposed ANN rule-based scheduler can handle nonlinearity by integrating metaheuristics into computer-assisted decision-making and can function effectively with imprecise inputs, negating the need for an exact numerical model. The MATLAB/Simulink R2023a software was used for simulation, and the system operated as efficiently as possible. The simulation results suggested that an ANN offers a foundation for extension to handle numerous particular scenarios. Full article

The growing adoption of electric vehicles (EVs) and the rapid expansion of public charging infrastructure pose new challenges and opportunities for energy systems, particularly in urban settings. This study presents an optimization-based evaluation of different EV charging strategies including direct charging, average-based methods, smart charging, and vehicle-to-grid (V2G) at public parking lots using real-world charging session data. This data-driven model is set to optimize the public EV charging of vehicles in Gothenburg, without sacrificing on the energy requirement while minimizing charging costs for the operators. Results indicate that direct charging scenarios lead to significantly higher peak loads (up to 1286 kW) and costs (around 370 k€), highlighting their inefficiency under unmanaged operation. In contrast, smart charging reduces peak loads by approximately 47% and overall costs by around 74%, showcasing its potential for cost-effective grid-friendly operation. Two different V2G scenarios were tested based on the impact of discharged power accounted for in peak costs, though it enables energy discharge back to the grid, the benefits remain modest under current assumptions due to tight operational constraints and limited incentives. The study emphasizes the value of smart optimization and appropriate market design in enhancing the flexibility and cost efficiency of public EV charging systems. Full article

This paper develops a real-time rolling-horizon optimization framework based on semidefinite programming (SDP) for vehicle-to-grid (V2G)-enabled electric vehicle (EV) fleets in distribution networks. The model coordinates time-varying EV availability, departure energy requirements, and distribution-network operating constraints under alternating-current (AC) power flow. The objective integrates voltage-dependent network loss cost, load-dependent EV energy transaction cost, and throughput-based battery degradation cost, while asymmetric charging/discharging efficiencies, EV implementation errors, and load forecast errors are also considered. To address the nonconvexity caused by AC power-flow equations and voltage-dependent losses, Hermitian lifting is used to reformulate the problem into a rank-constrained SDP model, followed by a convex SDP relaxation. Numerical studies on IEEE 33-bus and IEEE 69-bus systems show that the proposed rolling SDP method reduces EV-induced load peaks, improves load-smoothing performance, satisfies network and EV-side constraints, and yields numerically rank-one solutions in the tested cases. Further tests on time-slot lengths, look-ahead horizons, EV penetration levels, benchmark methods, EV implementation errors, and load forecast errors further verify the effectiveness and practical robustness of the proposed framework. Full article

To address the insufficient consideration of load temporal characteristics, load growth, distributed generation (DG) integration, and business-expansion load connection in existing available-capacity assessment methods, this paper proposes a load-connection decision-making method for active distribution network. Firstly, considering the load temporal characteristics, load growth, DG, and the bearing capacity of transformer distribution districts, a time-series bearing capacity analysis model of transformer distribution districts is proposed. In addition, a heuristic topology search strategy considering dynamic capacity constraints is developed to identify feasible power-supply paths and evaluate the dynamically validated available capacity. Secondly, considering the integration of DG and energy storage systems (ESSs), as well as key indicators such as load balance, temporal characteristic matching and comprehensive economic performance, a business-expansion load-connection decision-making method for active distribution network is proposed. Finally, the effectiveness of the proposed model and method is validated through a case study. The results show that after DG and ESS integration, the load balancing degree and temporal characteristic matching index are improved by approximately 31.42% and 18.21%, respectively. Compared with the peak-capacity method, single-capacity-index method, and loss-priority method, the proposed method achieves the highest or jointly highest comprehensive decision value under different operating scenarios. The improved branch-and-bound method reduces the number of actual evaluations while obtaining the same optimal decision result. The proposed method can optimize load-connection schemes and provide theoretical foundation and practical decision support for active distribution network planning and business expansion. Full article