Search Results (108)

Water functional zoning plays a crucial role in water resource allocation, pollution prevention, and ecological protection. With the increasing intensity of human activities, there is a significant mismatch between current water functional zoning and the economic, social development needs and ecological protection goals. Existing water functional zoning methods mainly rely on expert experience for qualitative judgment, which is highly subjective and inefficient. In response, this paper presents a transferable quantitative feature system and introduces a machine learning-based progressive zoning framework for water functions, validated through a case study of the Yangtze River Basin. The results show that the overall accuracy of the framework is 0.78, which is 4–7% higher compared to traditional single models. In terms of spatial distribution, the transformation of protection and reserved zones in 2020 mainly occurred in the middle and lower reaches, where human activities are frequent, particularly in Sichuan and Jiangxi provinces. The development zones are highly concentrated in the downstream areas, with some regions transitioning into protection or reserved zones, mainly in Hubei and Chongqing provinces. Adjustments to buffer zones are primarily concentrated along inter-provincial boundary areas, such as the junction between Hubei and Anhui provinces. This framework helps managers quickly identify key areas for optimizing water functional zones, providing valuable reference for the precise management of water resources and the formulation of ecological protection strategies in the basin. Full article

Accurate short-term wind speed forecasting is essential for mitigating wind power variability and supporting stable grid operation. This study proposes a model selection forecasting system (MSFS) that dynamically integrates six deep learning models to enhance predictive accuracy and robustness. Using multi-turbine data from a wind farm in northwest China, the framework identifies the optimal model at each time step through iterative evaluation and retrains the selected models to further improve performance. The Kruskal–Wallis test shows that all forecasting models, including MSFS, maintain statistical consistency with the real wind speed distribution at the 95% confidence level. Uncertainty analysis demonstrates that MSFS more reliable forecasting interval. By coupling MSFS-derived wind speed forecasts with turbine-specific power curves, the system enables reliable theoretical power estimation, offering critical reference information for dispatch planning, reserve allocation, and distinguishing resource-driven variability from turbine performance deviations. The slightly conservative yet highly stable forecasting behavior of MSFS reduces overestimation risks and enhances decision reliability. Overall, the proposed MSFS framework provides a robust, interpretable, and operationally valuable solution for short-term wind energy forecasting, with strong potential for wind farm operation and power system management. Full article

In the realm of financial management, optimizing the allocation of funds in banking companies is vital to their operational efficiency. Banks manage their funds by allocating them into reserve and credit funds as the main activities of banking. Optimizing these allocations ensures that all assets are effectively utilized. However, real-life optimization problems often involve uncertainty, making deterministic data assumptions insufficient. Robust Optimization is a methodology that addresses these uncertainties by incorporating computational tools to solve optimization problems with uncertain data. The uncertainty approach used in robust optimization is polyhedral sets. In the context of banking, uncertainties influencing the allocation of reserve and credit funds include financial risks and returns. These risks can be quantified using Conditional Value-at-Risk (CVaR), a suitable measure for banking fund allocation due to its ability to accommodate varying risk characteristics under different business conditions. This study focuses on developing an optimization model for reserve and credit fund allocation in banking companies using a Multi-objective Robust CVaR approach with lexicographic, informed by business risk data and credit instruments. The resulting optimization model yields optimal allocations for reserve and credit funds, ensuring efficient asset utilization to support banking operations. This approach offers new perspectives for banks to achieve fund allocations that are not only regulatory compliant but also optimal. The implications of such optimal allocations include mitigating risks associated with reserve fund imbalances and enhancing profitability through optimal credit returns. Full article

With the global trend towards “carbon neutrality,” the use of electric vehicles is becoming increasingly widespread, leading to new impacts on urban spaces. In the process of allocating resources for urban charging stations, there are widespread issues such as a singular planning approach and inadequate adaptation to actual travel demands. Therefore, this study adopts a method of integrating multi-source data to optimize the planning and layout of public electric vehicle charging facilities in Macau, striving to achieve breakthroughs in theoretical methods and key technologies. The study obtained a determination coefficient of R² = 0.43 through quantitative analysis, which is within a reasonable range of fitting spatial syntax and charging facility layout. This indicates that there is a moderate positive correlation between the distribution of charging facilities and core indicators such as road network integration and accessibility—about 43% of layout differences can be explained by spatial syntax indicators, and the remaining 57% of differences reserve space for optimizing multiple factors such as population density and parking lot distribution. On this basis, this study compares the layout experience of medium to high-density cities such as Hong Kong and Singapore, and combines the common characteristics of old parishes on Macau Island and new urban areas on outlying islands to explore innovative sustainable development technology paths that are suitable for Macau. This study not only summarizes the key factors and optimization breakthroughs that affect the spatial distribution of charging facilities in Macau, providing basic data and methodological strategies for charging facility planning, but also helps Macau save energy and reduce emissions, build a green city through layout optimization, provide practical reference for the development of land reclamation areas, and provide reference for carbon neutrality and smart city construction in the Guangdong Hong Kong Macau Greater Bay Area. Full article

The Number-Theoretic Transform is the primary performance bottleneck in hardware accelerators for post-quantum cryptography schemes like the Module-Lattice-based Key-Encapsulation Mechanism. A key design challenge is the trade-off between the massive parallelism required for low-latency computation and the prohibitive on-chip Block RAM consumption this typically entails. This paper introduces an NTT/INTT accelerator architecture that resolves this conflict, achieving a state-of-the-art latency of 40 clock cycles for a 256-point transform while utilizing only 5 BRAM blocks. Our architecture achieves this by pairing a 32-way parallel streaming datapath with a hybrid memory subsystem that strategically allocates on-chip storage resources. The core innovation is the use of distributed RAM instead of BRAM for high-bandwidth buffering of intermediate data between pipeline stages. This reserves the scarce BRAM resources for storing static twiddle factors and for system-level FIFO interfaces. By deliberately trading abundant logic fabric and dedicated DSP slices for BRAM efficiency, our work demonstrates a design point optimized for high-speed, BRAM-constrained System-on-Chip environments, proving that a focus on memory hierarchy is critical to developing PQC solutions that are both fast and practical for real-world integration. Full article

To address the challenges to power system frequency stability under high penetration of renewable energy, this paper proposes a coordinated inertia support strategy for wind–PV–thermal storage systems, overcoming the limitations of conventional inertia parameter adjustment. The core of the strategy lies in optimizing unit control activation logic and establishing a scenario-adaptive batch activation mechanism. Specifically, virtual inertia characteristic models for wind, PV, and storage units are developed, with key parameters optimized via fuzzy-logic-based coordinated control. An inertia demand assessment model under frequency security constraints is constructed to quantify the minimum system inertia requirement. Furthermore, disturbance reference power is generated based on the inertia reserve capability of each unit, and disturbance intervals are classified to achieve coordinated optimal allocation of virtual inertia. Simulation results on a built 3-machine, 9-node system demonstrate that the proposed strategy can intelligently coordinate the activation timing, role assignment, and regulation resources of wind, PV, and storage according to the type and severity of disturbances. Under various scenarios such as sudden load increase and decrease, the system effectively mobilizes resources to maintain frequency within the secure range while avoiding frequent actions of any single unit. The results verify that the strategy significantly enhances the system’s capability to handle bidirectional power disturbances and provide frequency support, offering a practical solution for inertia management in renewable-dominated power systems. Full article

Dynamic canal-system scheduling faces the fundamental challenge of determining the optimal reduction in the current period’s water allocation to reserve sufficient water for remaining periods, thereby hedging against potentially greater future water shortages. Although forecast information has been widely incorporated to address this hedging problem, its effectiveness is heavily dependent on forecast accuracy. Integrating abundant historical canal scheduling data with forecast information provides a promising pathway to improve scheduling performance, yet relevant studies remain limited. This study introduces the concept of Target Residual Lump-Sum Water Quota (TRLSWQ) for each time interval and develops a novel “Bi-level, Two-stage” (BT) model for dynamically updated canal-system scheduling that jointly leverages TRLSWQ and forecast information. The model defines clear canal scheduling rules and effectively adapts to the hierarchical structure in canal system scheduling. The model is applied to the summer–autumn irrigation scheduling of the Yongji main canal and six associated sub-canals in the Hetao Irrigation Area, Inner Mongolia, China. The results indicate that compared with the conventional model, the BT model reduces the total water shortage index of sub-canals from 40.81 to 31.44 (a decrease of 22.9%) and increases the utilization rate of the water quota from 89.3% to 92.9% (an increase of 3.9%). Furthermore, this study clarifies the mechanism of canal scheduling deviations caused by forecast errors: early-stage rainfall under-forecasting induces excessive early-stage allocation, leaving no water for later periods, whereas early-stage over-forecasting leads to withheld early allocation and unused residual lump-sum quota in later stages. The BT model effectively balances shortage risks between current and future periods and offers a practical and robust strategy for improving dynamic canal scheduling in irrigation districts. Full article

This article addresses selected aspects of designing a Traffic Management System (TMS) for the railway component of Poland’s Central Communication Port (CPK) project. The primary objective was to determine train headway times while considering automated traffic conflict resolution and speed profile optimization in relation to traction energy consumption. The study utilized simulations in the MATLAB/Simulink (Version number: R2024a Update 3) environment, modeling the movement of an ETR610 (ED250) train on a line equipped with the European Train Control System (ETCS). The simulation results provided insights into the impact of the adopted assumptions on TMS operational efficiency under failure conditions and its capability to optimize train movements. The conclusions underscore the critical importance of time reserves in effective conflict resolution, the interplay between buffer allocation and speed restrictions, and the impact of minimizing train stops on energy consumption. They also highlight the necessity of adapting operational strategies to infrastructure characteristics and the influence of simulation time on the effectiveness of conflict resolution methods. Furthermore, the study emphasizes the need to broaden operational scenarios to include failures of traction vehicles and train control systems, along with appropriate planning for time reserves. Full article

With the in-depth construction of the new power system, the importance of demand-side resources is becoming more and more prominent. The virtual power plant (VPP) has become a powerful means to explore the potential value of distributed resources. However, the differentiated resources between different VPPs are not reasonably deployed, and the problem of realizing the sharing of resources and the distribution of revenues among multi-VPP needs to be urgently solved. A cooperative operation optimization strategy for multi-VPP to participate in the energy and reserve capacity markets is proposed, and the potential risks associated with uncertainty in distributed generators (DGs) output are quantitatively assessed using conditional value-at-risk (CVaR). Firstly, due to the good adjustable performance of electric vehicles (EVs) and thermostatically controlled loads (TCLs), their virtual energy storage (VES) models are established to participate in VPP scheduling. Secondly, based on the asymmetric Nash negotiation theory, a P2P trading method between VPPs in a multi-marketed environment is proposed, which is decomposed into a virtual power plant alliance (VPPA) benefit maximization subproblem and a cooperative revenue distribution subproblem. The alternating direction multiplier method is chosen to solve the model, which protects the privacy of each subject. Simulation results show that the proposed multi-VPP cooperative operation optimization strategy can effectively quantify the uncertainty risk, maximize the alliance benefit, and reasonably allocate the cooperative benefit based on the contribution size of each VPP. Full article

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

Predicting urban water consumption helps managers allocate, reserve, and schedule water resources in advance, avoiding supply–demand imbalances. In practical terms, the improved forecasting model can assist urban water managers in planning supply schedules, optimizing reservoir operations, and allocating resources efficiently, thereby supporting sustainable water management in rapidly developing tropical island tourist cities. Traditional forecasting models typically assume that the statistical properties of the data remain stable, an assumption often violated under changing environmental conditions. In addition, tropical island tourist cities have unique hydrological characteristics and frequently fluctuating tourist populations, making water consumption forecasting even more complex in these settings. To address the aforementioned problems, this study develops an improved fractional-order reverse accumulation grey model. Based on the principle of new information priority, the weighted processing of historical data enhances the model’s learning capability for new data. The optimal fractional order is determined using the Greater Cane Rat Algorithm, and the optimized fractional-order reverse accumulation grey model is then applied to forecast water consumption in Sanya City. The results demonstrate that the proposed model achieves a relative error of 4.28% for Sanya’s water consumption forecast, outperforming the traditional grey model (relative error 5.24%), the equally weighted fractional-order reverse accumulation model (relative error 4.37%), and the ARIMA model (relative error 6.92%). The Diebold–Mariano (DM) test further confirmed the statistically significant superiority of the proposed model over the traditional model. Full article

With the integration of high-penetration renewable energy, existing uniform marginal pricing mechanisms face critical challenges, including difficulty in recovering flexibility resource capacity costs and free-riding phenomena caused by renewable energy’s variability. To address these issues, this paper proposes a differentiated pricing mechanism for renewable energy based on analytical uncertainty representation to avoid marginal price distortion and promote the rational allocation of ancillary service costs. Firstly, a joint clearing model for energy and reserve ancillary service is developed, incorporating a distributional robust chance constraint based on moment information to model the uncertainty of renewable energy. Then, the composition structure of the nodal marginal price for ancillary service demand is redefined, offering clearer and more explicit price signals compared with traditional uniform marginal pricing. After that, quantification of the impact of energy storage on renewable energy forecast errors and ancillary service pricing is conducted, with a systematic analysis of its role in reducing ancillary service costs and optimizing generation revenue. Simulation results on the modified IEEE 30-bus system demonstrate significant advantages over traditional uniform pricing: the proposed mechanism ensures fair cost allocation, effectively mitigates free-riding problems, and provides clear economic signals. With energy storage units regulating renewable power output, it could lead to a 12.9% reduction in ancillary service costs while increasing total generation revenue by 6.73%. Full article

To effectively address challenges posed by high-penetration renewable energy to power system operation and reserves, this paper proposes a novel research framework. The framework considers transmission–distribution coordinated dispatch and optimizes reserve capacity. First, the framework addresses the volatility and uncertainty of wind and solar power output. It constructs a three-dimensional objective function incorporating generation cost, spinning reserve cost, and linear wind/solar curtailment penalties as core components. The study uses the IEEE 30-bus system as the transmission network and the IEEE 33-bus system as the distribution network to build a transmission–distribution coordinated optimization model. Power dynamic mutual support across voltage levels is achieved through tie transformers. Second, the framework designs three typical scenarios for comparative analysis. These include separate dispatch of transmission and distribution networks, coordinated dispatch of transmission and distribution networks, and a fixed reserve ratio mode. The approach breaks through the limitations of traditional fixed reserve allocation. It optimizes the coordinated mechanism between reserve capacity spatiotemporal allocation and renewable energy accommodation. Case study results demonstrate that the proposed coordinated optimization scheme reduces total system operating costs and wind/solar curtailment rates. This is achieved by exploiting the potential of regulation resources on both the transmission and distribution sides. The results verify the significant advantages of transmission–distribution coordination in improving reserve resource allocation efficiency and promoting renewable energy accommodation. The approach helps enhance power grid operational economics and reliability. Full article

The Economic Dispatch Problem (EDP) plays a critical role in power system operations by trying to allocate power generation across multiple units at minimal cost while satisfying complex operational constraints. Traditional optimization techniques struggle with the non-convexities introduced by factors such as valve-point effects, prohibited operating zones, and spinning reserve requirements. While metaheuristics methods have shown promise, they often suffer from convergence issues and constraint-handling limitations. In this study, we introduce a novel application of Group Relative Policy Optimization (GRPO), a reinforcement learning framework that extends Proximal Policy Optimization by integrating group-based learning and relative performance assessments. The proposed GRPO approach incorporates smart initialization, adaptive exploration, and elite-guided updates tailored to the EDP’s structure. Our method consistently produces high-quality, feasible solutions with faster convergence compared to state-of-the-art metaheuristics and learning-based methods. For instance, in the case of the 15-unit system, GRPO achieved the best cost of USD 32,421.67/h with full constraint satisfaction in just 4.24 s, surpassing many previous solutions. The algorithm also demonstrates excellent scalability, generalizability, and stability across larger-scale systems without requiring parameter retuning. These results highlight GRPO’s potential as a robust and efficient tool for real-time energy scheduling in smart grid environments. Full article

This paper addresses the synergy between environmental and economic benefits in the green power trading market by constructing a collaborative game model for environmental rights value and electricity energy value. Based on this, a model for maximizing the benefits of flexible resource operation is proposed. Through the combination of non-cooperative and cooperative games, the conflict and synergy mechanisms of multiple stakeholders are quantified, and the Shapley value allocation rule is designed to achieve Pareto optimality. Simultaneously, considering the spatiotemporal regulation capability of flexible resources, dynamic weight adjustment, cross-period environmental rights reserve, and risk diversification strategies are proposed. Simulation results show that under the scenario of a carbon price of 50 CNY/ton (≈7.25 USD/ton) and a peak–valley electricity price difference of 0.9 CNY/kWh (≈0.13 USD/kWh), when the environmental weight coefficient α = 0.5, the total revenue reaches 6.857 × 10⁷ CNY (≈9.94 × 10⁶ USD), with environmental benefits accounting for 90%, a 15.3% reduction in carbon emission intensity, and a 1.74-fold increase in energy storage cycle utilization rate. This research provides theoretical support for green power market mechanism design and resource optimization scheduling under “dual-carbon” goals. Full article