Economic Research Institute

Tunnel engineering plays a critical role in modern infrastructure development. During its service life, tunnels are inevitably subjected to dynamic loads, such as earthquakes, Tunnel Boring Machine (TBM) excavation, and rockburst events. These forces can lead to mechanical property degradation of jointed rock mass, thereby affecting the safety of tunnel engineering. Grouting technology, a crucial method for enhancing the mechanical properties of jointed rock mass, has been widely utilized in tunnel engineering. Therefore, investigating the impact mechanisms of filled jointed rocks is of significant importance. This study examines the impact behavior of filled jointed rock specimens using a split Hopkinson pressure bar apparatus. The results indicate that the dynamic mechanical properties of filled jointed rocks exhibit significant strain-rate dependence. With an increase in the joint surface roughness coefficient of composite-filled specimens, higher dynamic compressive strength and lower failure rates are observed compared to specimens filled with portland cement materials. Specimens with higher joint surface roughness coefficients showed lower damage factors and greater stability under dynamic loading. The failure process and mechanical properties of filled jointed rock under dynamic loading were shown and analyzed specifically based on the numerical model built in this paper. At high strain rates, the relationship between the dynamic increase factor and strain rate for filled jointed rocks follows the Grady equation, which can be used to predict and analyze their dynamic compressive strength. This study provides valuable insights into the impact mechanisms of grouted jointed rocks.

This paper constructs and estimates a novel two-sector Dynamic Stochastic General Equilibrium (DSGE) model to analyze the macroeconomics of Kazakhstan’s dual-energy structure, where a large fossil fuel sector coexists with an emerging renewable segment. The model’s key innovation is its integration of an endogenous, depletable oil stock and a dual-inflation Taylor-type rule, which together capture the specific transmission channels between hydrocarbon dependence and green investment. By differentiating between oil-driven and core inflation, the framework quantifies how oil price volatility transmits monetary conditions to the renewable sector. Bayesian estimation, using sectoral data from national accounts, reveals a pronounced asymmetry: oil stock/discovery dynamics and oil revenue fluctuations dominate macroeconomic volatility, while the renewable sector exhibits stable output but remains vulnerable to oil-driven monetary tightening transmitted mainly through indirect channels. The results indicate that Kazakhstan’s ongoing energy transition offers a stabilizing diversification benefit in principle but remains structurally constrained by macroeconomic dynamics and fiscal patterns anchored to hydrocarbon conditions. These findings provide a quantitative basis for designing transition policies that mitigate cross-sector spillovers and support effective diversification in resource-dependent economies.

To address the operational optimization of distribution grids in commercial integrated-industrial parks, this study proposes a non-cooperative three-player game involving an energy operator, distributed photovoltaic (PV) prosumers, and electric-vehicle (EV) charging aggregators. A multienergy-flow scenario coupling electricity and heat is designed to define the dispatch framework and strategic logic of each entity, with all participants simultaneously determining their energy-scheduling strategies. A tractable mathematical program is employed to solve for the Nash equilibrium, where payoff functions quantify the operator’s regulation cost, the prosumer’s PV self-consumption surplus, and the aggregator’s load adjustment expenditure, while operational constraints calibrate parameters. Simulations show that at equilibrium: the operator reduces grid injection during mid-day PV peaks and increases district-heating output in office hours to ease distribution grid load; PV prosumers boost local self-consumption and supply residual energy to EV aggregators, reducing PV surplus back flow pressure; EV aggregators absorb mid-day PV surplus and shift evening charging loads, mitigating early evening distribution-network stress. The proposed model significantly improves PV utilization (system PV absorption rate reaching 97.94%), enhances multi-energy complementary, and strengthens distribution grid stability without compromising individual entities’ operational rationality.

As the core equipment of UHV DC transmission systems, the safe operation of converter transformer tap-changers directly impacts grid stability. Early dynamic voltage control strategies reduced tap-changer operations by narrowing the DC voltage deadband. However, in high-capacity transmission scenarios, frequent strategy activation/deactivation due to power overloads has led to a surge in tap-changer over-polarity events, threatening equipment safety. This paper addresses this challenge by analyzing the adaptability of different control strategies and proposing an over-polarity suppression scheme tailored for high-capacity UHV DC systems. By optimizing the coordination between voltage deadband and power thresholds, the scheme balances tap-changer operation frequency with risks to filter requirements and DC protection systems, providing technical support for enhancing DC system reliability.

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes.

Accurate classification under class imbalance remains a significant challenge in machine learning. While deep learning offers potent solutions for classification tasks, purely data-driven models often function as black boxes. The lack of interpretability can obscure the reasoning behind predictions, potentially leading to adverse outcomes. Integrating domain knowledge with contrastive learning presents a promising avenue to address these issues. In this work, we propose a novel prediction model for imbalanced classification based on a Supervised Contrastive Feature Optimization (SCFO) framework. The SCFO framework introduces a knowledge association module to quantify and evaluate identified influence factors, ensuring alignment between the selected features and established domain principles. Subsequently, a supervised contrastive feature module extracts discriminative, statistical representations from the selected feature samples. Our approach is particularly advantageous for imbalanced datasets, as it improves the model's ability to discern minority classes. The effectiveness of our method is validated using real-world datasets. Experimental results demonstrate that SCFO successfully extracts meaningful statistical contrastive representations and delivers significant performance improvements for the target imbalanced classification task.

Extreme meteorological events pose critical threats to the operational reliability of contemporary distribution networks. This study develops a comprehensive disaster mitigation framework for grid-forming photovoltaic (PV) systems integrated with mult

With the continuous increase in the penetration rate of renewable energy in the new power system, voltage stability is facing greater challenges. This article proposes a voltage stability prediction and control strategy based on artificial intelligence. To address the issues of evaluation and control decoupling, insufficient robustness under dynamic conditions, and neglect of practical constraints in existing methods, an integrated predictive control framework is constructed to integrate multi-source data for online risk prediction and multi-scale voltage regulation. By modeling voltage stability, feature fusion, and spatiotemporal correlation, a predictive model is trained using multi task learning, and a closed-loop control strategy guided by prediction is designed. The simulation results show that this strategy effectively reduces the over limit rate of key nodes, improves voltage stability, and verifies its superiority.

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications.

Accurate prediction of goaf settlement is essential for evaluating surface movement and deformation and for understanding overburden failure mechanisms in sandstone coal mine goafs. Coal seam extraction commonly induces significant overburden failure, stress redistribution, surface settlement, and ground fissures, which pose threats to mining safety and ecological stability, particularly under conditions of thick loose strata and thin bedrock. In this study, a sandstone coal mine in Ningxia, China, is selected as the research site. A three-dimensional discrete element model is established using 3DEC to simulate overburden deformation during coal seam extraction under complex geological conditions. The evolution of plastic zones, displacement fields, stress distribution, and surface settlement at different mining stages is systematically analyzed. Meanwhile, the probability integration method is employed to conduct dynamic prediction of surface settlement, with key parameters determined through inversion of field monitoring data. The results indicate that coal seam mining results in an elliptical settlement basin, with a maximum surface settlement of 3.77 m occurring at the goaf center. Shear failure is dominant along coal pillar edges, while significant stress concentration develops in the central goaf area. The settlement predictions obtained from the probability integration method show strong agreement with numerical simulation results, with prediction errors controlled within 3.6%–6.0%. These findings demonstrate that the combined application of discrete element numerical simulation and the probability integration method is effective for predicting surface settlement and analyzing overburden failure mechanisms under thick loose strata and thin bedrock conditions. The study provides a robust theoretical basis and practical reference for surface stability evaluation, hazard prevention, and ecological restoration in coal mining areas.