Karst-influenced deep excavations involve coupled geological, hydraulic, structural, and observational uncertainties that cannot be represented adequately by deterministic checks alone. This study develops a monitoring-updated reliability framework for the Baiyun Dongping Station excavation in Guangzhou, China. Project documents, karst investigation records, a documented water-inrush event, field monitoring data, APDL-derived geometry, and direct global–local coupled simulations are integrated through a source-path-response interpretation. Formal probability statements are restricted to two traceable criteria: a 24 mm wall-displacement warning threshold and a 270 m3/h severe-flow threshold, while support force, groundwater change, and adjacent-asset response are treated as diagnostic or validation evidence. Bayesian updating is used to reduce uncertainty in physically meaningful parameters, including rock stiffness, cavity-roof tensile strength, fracture conductance, and effective hydraulic head. Posterior standard deviations decrease by 38.5–81.8 %. Direct simulations give severe-flow exceedance probabilities of 0.0407, 0.1386, and 0.0631 at Stages 4, 5, and 6, respectively. Effective head and fracture conductance dominate post-connection flow, whereas roof strength controls pathway initiation and soil-rock stiffness controls stress transfer. External predictive coverage is 0.70 for wall response but only 0.20 for held-out settlement, indicating that the framework supports hydraulic reconstruction and wall-warning assessment, but not a general adjacent-asset reliability claims under the available field evidence.
With the development of renewable energy and the increase of carbon emission limits, the operation optimization of power systems under the consideration of the coupling of the electric energy market, carbon emission market, and green certificate (GC) market has received wide attention. However, the high nonlinearity and dynamic characteristics of the power system after introducing renewable energy sources make it difficult to obtain the optimal operation decision under the coupled electricity‑carbon-green certificate market. To solve this, we formulate a hierarchical game between the VPP operator and multiple aggregators. Specifically, a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based MADRL algorithm employs a centralized training with decentralized execution (CTDE) framework to optimize the operator's multi-aggregator dispatch strategy. Meanwhile, multi-type resource aggregator models are developed to minimize operational costs, with a generalized regression neural network (GRNN) introduced to approximate the nonlinear mapping between the VPP operator's dispatch decisions and the aggregators' optimal responses. The proposed MATD3-GRNN hybrid architecture accelerates convergence by training offline agent policies for real-time deployment, achieving adaptive decision-making under evolving system conditions. Simulation results demonstrate that the proposed strategy significantly enhances operational revenue while maintaining computational efficiency in complex multi-market environments.
Abstract Addressing the challenges of complex terrain modeling and poor real-time interactivity in geologically sensitive areas, this study presents a digital twin system construction method based on the Unreal Engine 5 (UE5) platform. Taking the Lianziya dangerous rock mass in the Three Gorges area as a case study, high-precision terrain data were acquired using an unmanned aerial vehicle equipped with light detection and ranging, fused with oblique photography imagery. A cascade filtering pipeline was implemented to optimize point cloud quality, and the quadric error metrics algorithm was applied for model simplification. The results show that the method reduced the mesh polygon count by 50% while maintaining level of detail 4 accuracy, with a mean geometric error (Hausdorff distance) of only 0.018 m. The resulting system enables dynamic weather simulation and adaptive path planning, maintaining a stable frame rate of over 60 FPS at 4K resolution, effectively resolving rendering stalls in large-scale scenes. This workflow provides a reproducible, high-fidelity technical solution for the digital preservation and intelligent management of geological hazard sites.
Desert-Gobi-Arid (DGA) zones are key regions for large-scale photovoltaic and wind-energy development and represent fragile dryland ecosystems with strong water limitation and slow ecological recovery. This paper critically reviews the ecological impacts of renewable-energy infrastructure in DGA-type landscapes, using China’s DGA bases as the main empirical context and evidence from ecologically comparable drylands for comparison. It examines how photovoltaic and wind facilities affect surface environments, ecological processes, and carbon cycling. Existing studies indicate that these facilities influence local microclimate, soil conditions, vegetation dynamics, and carbon exchange through shading, surface disturbance, airflow alteration, and hydrothermal redistribution. These impacts are site- and scale-dependent and are jointly controlled by facility layout, disturbance intensity, surface substrate, vegetation background, climate conditions, and management practices. Under low-disturbance design and effective management, they may support soil moisture retention, vegetation recovery, soil carbon accumulation, and ecosystem resilience, while higher disturbance may increase ecological risks such as water stress and habitat fragmentation. This review links direct emission reductions with indirect ecological effects and governance considerations and provides a case-based reference for assessing renewable-energy development in fragile dryland environments.
Hydrogen-based integrated energy systems (HIESs) are increasingly adopted in industrial parks to support low-carbon and multi-energy coordination. However, progressive and heterogeneous fuel cell degradation reshapes power margins and induces degradation-driven operational risk, which is not explicitly considered in existing cost-oriented planning frameworks. Although recent studies incorporate lifecycle cost or degradation effects, a unified framework that simultaneously captures degradation evolution, operational risk, and system resilience remains lacking. This paper proposes a resilience-oriented planning and operation framework for HIESs with multi-module fuel cell storage. A health-state evolution model is first developed to characterize load-dependent and heterogeneous degradation across modules. Based on this, a degradation-aware operational risk is quantified by coupling state of health with real-time load intensity. An integrated optimization model is then formulated to jointly consider lifecycle economic performance and risk regulation, where degradation-induced replacement impacts are transformed into equivalent annual costs to bridge long-term planning and operational scheduling. Case study results demonstrate that the resilience-oriented planning and operation framework effectively redistributes operational stress and suppresses risk concentration among fuel cell modules. Compared with conventional strategies, annualized lifecycle cost is reduced by 3.43%, renewable energy accommodation increased from 85% to 95%, and degradation-related replacement cost is significantly mitigated. Under islanded operation, the critical load supply ratio improved from 78% to 92%, demonstrating enhanced system resilience under extreme conditions.
