Chenghuan Tian

During hover, DEP unmanned aerial vehicles (UAVs) suffer severe aerodynamic interference between rotor downwash and the wing, causing thrust loss. High-fidelity CFD can capture the interference physics but is too costly for iterative design. Existing reduced-order models (ROMs) are fast, but their predictions of wing download have not been adequately validated across variations in wing chord, nacelle offset, and rotor-to-wing height. To address this, we propose an integrated framework combining a validated overset-grid CFD model, a parametric reduced-order model that decomposes wing download into an equivalent coefficient multiplied by the slipstream-impinged area, and Latin hypercube sampling for design-space exploration. The equivalent coefficient is represented via Gaussian process regression over three geometric parameters using CFD-calibrated data. The reduced-order model predicts wing download in seconds (less than 10 s per evaluation on a standard desktop workstation), whereas the corresponding high-fidelity CFD simulation typically requires approximately 2 to 4 h per case on a 256 core HPC cluster. The reduced-order model predicts wing download in seconds with a maximum validation error of 7.6% and successfully classifies the design space into acceptable and high interference regimes. Nine geometrically distinct validation points across both interference regimes show correct categorization with no misclassification. The framework provides a lightweight screening tool for isolated rotor–wing interaction analysis, which is a fundamental building block for understanding more complex DEP configurations. The overall modeling framework is methodologically extendable to other configurations through recalibration, though such extensions are not pursued in the present work.

With the transition of power systems toward a high penetration of renewable energy and multi-user collaborative operation, issues related to load fluctuations and grid stability have become increasingly prominent. As a centralized energy infrastructure, a shared energy storage system (SESS) can enhance the utilization efficiency of storage resources through centralized configuration and coordinated scheduling among multiple users. This study focuses on four typical industrial user load profiles and four representative renewable power stations within an industrial park in Jiangsu Province, China. A day-ahead economic dispatch method based on a shared battery energy storage station is proposed for industrial users, integrating actual operational data from 2024 with a tiered electricity tariff scheme to minimize the total daily operating cost of the user group. Four operational scenarios are constructed: (1) no energy storage, (2) independent battery energy storage for each user, (3) shared battery energy storage, and (4) shared battery–flywheel hybrid energy storage. Typical-day load curve simulations are performed for each case. The results indicate that shared battery energy storage can significantly reduce daily operating costs and improve resource utilization, achieving a 4.1% reduction in total cost compared with an independent storage configuration for each user. The shared battery–flywheel hybrid storage demonstrates millisecond-level responses to second-level high-frequency power fluctuations, effectively lowering peak demand and smoothing the power curve for high-fluctuation users. Compared with shared battery-only storage, the hybrid scheme achieves a 4.6% reduction in peak-period electricity purchase costs, a 0.6% reduction in total electricity consumption, and a 0.39% decrease in overall system operating costs. The proposed method and hybrid storage topology outperform conventional configurations in both technical performance and economic benefits, providing theoretical and empirical support for engineering applications of shared energy storage under high-frequency fluctuation load scenarios.

The large-scale integration of coordinated offshore wind and offshore photovoltaic (PV) generation introduces pronounced power fluctuations due to the intrinsic randomness and intermittency of renewable energy sources (RESs). These fluctuations pose significant challenges to the secure, stable, and economical operation of modern power systems. To address this issue, this study proposes a hybrid energy storage system (HESS)-based optimization framework that simultaneously enhances fluctuation suppression performance, optimizes storage capacity allocation, and improves life-cycle economic efficiency. First, a K-means fuzzy clustering algorithm is employed to analyze historical RES power data, extracting representative daily fluctuation profiles to serve as accurate inputs for optimization. Second, the time-varying filter empirical mode decomposition (TVF-EMD) technique is applied to adaptively decompose the net power fluctuations. High-frequency components are allocated to a flywheel energy storage system (FESS), valued for its high power density, rapid response, and long cycle life, while low-frequency components are assigned to a battery energy storage system (BESS), characterized by high energy density and cost-effectiveness. This decomposition–allocation strategy fully exploits the complementary characteristics of different storage technologies. Simulation results for an integrated offshore wind–PV generation scenario demonstrate that the proposed method significantly reduces the fluctuation rate of RES power output while maintaining favorable economic performance. The approach achieves unified optimization of HESS sizing, fluctuation mitigation, and life-cycle cost, offering a viable reference for the planning and operation of large-scale offshore hybrid renewable plants.

Parallel interleaved three-level inverters (PITIs) are much appreciated in flywheel energy storage systems (FESS), for their great reliability and efficiency. However, the subsequent circulating currents generated by the common-dc-link and asynchronous carriers can lead to excessive harmonic distortion and power losses. To solve this issue, a multiobjective optimization based pulse-width modulation (MOOPWM) scheme is proposed in this article. First, a multiconstraint vector preselection criterion is developed to reconcile the targets such as eliminating circulating currents, diminishing common-mode voltage (CMV) amplitude, and executing the nearest equilateral/isosceles vector synthesis. On this basis, the optimal vector set is determined. Then, a novel carrier-based implementation strategy is developed to synthesize the above optimal vectors set of MOOPWM. The implementation strategy incorporates smooth segmented compensation to the reference signal alongside a nonclamping phase carrier exchange algorithm, which conducts the modulation with lower computational cost. Compared with existing modulation schemes, a simultaneous reduction of circulating currents, CMV and switching loss can be achieved with the proposed MOOPWM. Finally, comparative simulations and experiments on a 300-kW PITI fed FESS motor are carried out to validate the effectiveness of the proposed scheme.