High penetration of renewable energy is transforming modern power systems into low-inertia and weak-grid networks, where frequency and voltage no longer respond independently to active and reactive power disturbances. Under such conditions, the conventional decoupled paradigm, which assigns active power mainly to frequency regulation and reactive power mainly to voltage control, may overlook cross-channel interactions and lead to inaccurate security assessment. To address this problem, this paper proposes an analytical framework for quantifying frequency-voltage coupling strength and assessing the corresponding joint security region in renewable-dominated power systems. First, a quasi-steady-state linearized coupling model is established by integrating source-side regulation, load response, and network impedance characteristics. Based on this model, a frequency-voltage coupling matrix is derived to explicitly map active-reactive power disturbances to coupled frequency and voltage deviations. Then, the relative gain array theory is introduced to construct a dimensionless interaction factor for evaluating coupling strength and identifying strongly coupled buses. Furthermore, the coupled response is reformulated as a linear mapping problem, through which equal-power disturbance boundaries are transformed into frequency-voltage response trajectories and regions. Finally, prescribed frequency and voltage security limits are inversely mapped to the active-reactive power input space to obtain a joint input power perturbation security region. Case studies show that frequency-voltage coupling is more pronounced at load and passive connection buses and in weak-grid areas. Stronger coupling rotates the response trajectory, deforms the coupled response region, and compresses the security input power disturbance region.
Design optimization of offshore wind turbine (OWT) monopiles presents a high-dimensional challenge, as the pile is discretized into multiple segments (typically 2-3 m in length), each with independent diameter and wall thickness. Furthermore, the optimization is governed by various design constraints (ultimate, fatigue, and serviceability limit states), numerous design load cases (DLCs), and complex multi-physics involving aero-hydro-servo-elastic-soil interactions. These factors necessitate computationally expensive time-domain simulations. Conventional optimization methods are susceptible to local optima and struggle with the high-dimensionality, multiple constraints, and numerous load cases inherent to this problem. Moreover, current monopile designs are seldom optimized using integrated multi-physics simulations, which can lead to suboptimal or potentially over-conservative designs. This study proposes an efficient global optimization framework for high-dimensional, multi-constrained, integrated design of monopiles under various load cases. The framework comprises three key components: (i) an integrated aero-hydro-servo-elastic-soil model for providing high-fidelity dynamic responses; (ii) the subset simulation optimization (SSO) algorithm for efficient global exploration of the highdimensional design space; and (iii) a high-fidelity multivariate metamodel based on Gaussian process regression (GPR) for accelerating the optimization process. The effectiveness of the proposed framework is demonstrated by optimizing a monopile for a 15 MW OWT. Benchmarking against a suite of optimization algorithms (GD, GA, PSO, SA) demonstrated the superiority of SSO, which achieved a 9% reduction in mass and reduced the required computational time by 38%. The results identify fatigue, rather than ultimate strength, as the governing constraint for monopile optimization. The framework equips designers with a computationally efficient, globally optimal route to lighter offshore wind turbine foundations, cutting costs while sustaining structural integrity.
Rapid urbanization profoundly alters interactions between human societies and natural ecosystems, thereby increasing uncertainty in social–ecological systems (SESs). Understanding the nonlinear responses of SESs to urbanization can help anticipate future risks and guide adaptive regional governance. Taking 296 prefecture-level and larger cities in China as the study area, this study develops an integrated state-structure framework that combines composite state assessment, network analysis, and change-point detection to characterize evolutionary dynamics and identify consensus transition intervals and regime stages along the urbanization gradient. The results show that changes in SES along the urbanization gradient do not follow a linear trajectory of either improvement or degradation. Instead, SESs transition among distinct regime stages through the combined effects of changes in system state and network reorganization. Greater network integration did not consistently correspond to improved system performance. At certain levels of urbanization, greater connectivity coexisted with a lower or declining composite SES state, indicating a mismatch between structural integration and system performance. The joint detection approach identified four consensus transition intervals and five regime stages: coordinated system improvement, continued structural strengthening, marked state deterioration, low-level stabilization, and recovery-oriented optimization. Multi-scenario projections further show that regime trajectories and transition risks vary across urban development scenarios. Cities located near consensus transition intervals or projected to undergo changes in regime-shift types were identified as priority areas for dynamic early warning and adaptive governance. These findings provide a state-structure perspective on urbanization-driven regime shifts in SESs and inform differentiated adaptive governance for sustainable urban development.
Abstract Drought disasters are the most severe natural disasters affecting China’s grain yields, characterized by prolonged duration and extensive impact. Under the integrity of the ecosystem and the interaction among factors, drought not only directly affects grain yields in the affected areas, but also further influences grain yields in neighboring regions by changing agricultural production conditions. Based on the panel data of 31 provinces in China from 1989 to 2023, this paper employs Moran’s I to analyze the spatial autocorrelation and agglomeration characteristics of grain yields. The spatial Durbin model is further utilized to examine the spatial spillover effects of drought disasters on grain yields and their regional heterogeneity. The empirical results reveal significant spatial autocorrelation in grain yields, with local characteristics of high–high and low–low agglomeration. Drought disasters exert a significant inhibitory effect on grain yields and generate negative spillover effects that transcend administrative boundaries, impacting surrounding regions. These effects exhibit pronounced regional heterogeneity: from the perspective of economic development, significant spillover effects are observed in the eastern and northeastern regions, but not in the central region. In terms of industrial attributes, the intensity of spillover effects is greater in major grain-producing areas compared to nonmajor grain-producing areas. The findings of this paper provide insights for integrating disaster response into the national food security industrial belt planning, enabling a closed-loop management framework that encompasses “disaster impact, spatial transmission, and policy response.”
Water hammer protection design for long-distance water conveyance systems faces two major challenges: the high computational cost of transient simulations and the difficulty of accurately characterizing safety boundaries. To overcome these limitations, we propose a systematic framework that integrates active-learning-based surrogate modeling with closed-loop progressive optimization for robust cost reduction in air-vessel design under multiple hydraulic safety constraints. To reduce the arbitrariness of high-dimensional optimization, the air vessel installation location and the initial parameter search space are first derived from the propagation mechanism of water hammer waves. An active learning strategy combining query-by-committee with hybrid sampling is then developed to progressively characterize the non-convex feasible domain by adaptively enriching high-value samples in constraint-sensitive regions. A closed-loop optimization procedure centered on FAR-constrained calibration is further established, through which the epistemic uncertainty of the surrogate model is transformed into an explicit feasibility margin, thereby improving the reliability of feasibility screening near the hydraulic constraint boundary. Finally, we employ SHAP analysis to reveal the nonlinear coupled contributions of key variables to extreme pressure responses. Application to a real-world water conveyance project demonstrates that the proposed method achieves substantial cost reduction and improved computational efficiency while satisfying the prescribed hydraulic safety constraints.
