To address the problems of renewable energy accommodation and supply–demand imbalance caused by the dynamic evolution of load demand over the life cycle of western mining areas, a life-cycle dynamic configuration method for the energy system of underground coal mines is investigated. Based on the production organization in the construction, early-stage mining, mid-stage mining and late-stage mining periods, a multi-energy coupling framework for electricity–heat–cooling is constructed to reflect the differences between above-ground and underground loads. A multi-stage mixed-integer linear programming model is developed, which integrates photovoltaic (PV) generation, electrical energy storage, chillers and external power/heat supply. The objective is to minimize the total life-cycle cost consisting of investment, operation and maintenance, purchased energy and carbon emission costs by optimally sizing PV, energy storage and primary network equipment, and by representing the temporal evolution of source–load relationships through typical-day load profiles and year-by-year capacity expansion decisions. A typical western underground mine is used as a case study, and two scenarios are compared: traditional static one-shot configuration and life-cycle dynamic configuration. The results show that the dynamic configuration increases the average installed PV capacity and renewable penetration through staged PV and storage expansion in key years, while restraining the required capacity of primary network equipment and substantially reducing curtailment over the whole life cycle. Compared with the static configuration, the dynamic configuration reduces the total life-cycle cost by about 17.9% and the carbon emission cost by about 50.2%. The proposed life-cycle dynamic configuration method can satisfy secure energy supply for mining areas while balancing economic performance and low-carbon goals, and it provides a technical reference for planning clean energy systems in western mining areas and similar energy-intensive industrial parks.

To address the challenges of diversified participants in the electricity market and resolve conflicts of interest among multiple virtual power plants (VPPs), this paper proposes a three-layer hybrid game strategy involving the distribution system operator (DSO), virtual power plant operator (VPPO), and user aggregator (UA) to support collaborative optimization of multi-VPP systems. First, a UA coalition is formed by aggregating various flexible resources, including photovoltaic prosumers, electric vehicle charging stations, and integrated energy loads. Next, a 3-layer hybrid game model is constructed, encompassing hierarchical energy transactions among DSO, VPPO, and UA, as well as peer-to-peer energy trading among UA entities, structured as a "Stackelberg game - Stackelberg game - cooperative game" framework. Finally, the proposed model is solved using the bisection method, Karush-Kuhn-Tucker conditions, and the alternating direction multiplier method. Case studies demonstrate that the proposed multi-layer hybrid game strategy reduces the operating costs of the photovoltaic prosumer UA and the electric vehicle charging station UA by 4.5% and 15.3%, respectively. Meanwhile, guided by the dynamic pricing mechanism of the DSO, the total profit of the system operators increases by 2.7%. This strategy can effectively balance the interests of multiple stakeholders, optimizing the energy trading and benefit allocation mechanisms among different operators.

This study investigates the ash deposition behavior of anthracite during combustion, which significantly affects boiler safety and efficiency due to slagging tendencies. A pilot-scale one-dimensional settling furnace system was employed to conduct combustion experiments under varied operating conditions, including different loads, primary/secondary air ratios, and excess air coefficients. Ash samples were analyzed by fusion tests, SEM, XRD, XRF, and laser sizing. Results show ash composition (C, O, Si, Al) and crystalline phases (quartz, mullite, hematite) remain stable. Increasing the load from 0.2 MW to 0.3 MW raises the ash deformation temperature from 1259 °C to 1312 °C, while the slagging index increases from 1268 to 1319, indicating a significantly enhanced slagging tendency. When the mass ratio of primary air to secondary air is increased from 2/8 to 4/6, the unburned carbon content in ash increases from approximately 37.5% to 40%, and the median particle size enlarges from 22 μm to about 28 μm, resulting in a pronounced promotion of ash deposition. Excess air coefficient had limited impact on fusibility and slagging. Ash exhibited a bimodal size distribution: fine particles form an adhesive layer, while coarse particles deposit by impaction, jointly accelerating slagging. The results demonstrate that boiler load dominates slagging behavior, with air distribution affecting burnout and particle characteristics. This study provides pilot-scale experimental data and mechanistic insights for slagging prediction and combustion optimization of anthracite-fired boilers under wide-load operation.

To address the optimization problem of resource aggregation and bidding decision-making for virtual power plants (VPPs) participating in power peak shaving, a decision optimization model based on resource response capability and information gap decision theory (IGDT) is proposed. Considering four dimensions—response potential,fluctuation degree, duration, and response speed—an aggregation indicator system for distributed resources is constructed. A multi-objective aggregation optimization model is established, balancing the maximization of expected response revenue and the minimization of deviation penalty risk, to screen the optimal resource portfolio. The market transaction framework and bidding decision mechanism for VPPs participating in power peak shaving are designed. The IGDT theory is introduced to characterize the uncertainty of peak shaving compensation prices, and a risk-averse (RA) model is constructed to optimize bidding strategies.The simulation results show that the multi-objective optimization model of virtual power plant aggregation can take into account both economic and risk considerations. It can provide a theoretical method for virtual power plant aggregators to screen resources and reduce the risk of deviation punishment of virtual power plants. The IGDT-based bidding decision optimization model can help avoid the transaction risk caused by the uncertainty of peak compensation price, so that the virtual power plant can obtain reasonable response benefits.

system demonstrates that integrated optimization of these technologies reduces total system costs by 5.98% and curtailment rates for wind and solar power by 15.68%. The results validate that the proposed collaborative planning approach effectively integrates complementary advantages across different technologies, significantly lowering system costs and alleviating pressure on renewable energy integration. It also reveals that the flexibility regulation capacity of coal-fired power plants is influenced by their node position within the grid.

To address the issues of existing regional distributed photovoltaic (PV) power forecasting, such as heavy reliance on meteorological data, high operation and maintenance costs, poor data quality and insufficient result credibility,a joint credible forecasting method for PV power and energy is proposed. First, power measurements from smart meters and daily frozen energy data are jointly filtered, fused, and normalized to enhance data set quality. Second, a multi-time-scale, high-accuracy sequence-to-sequence (Seq2Seq) forecasting framework is developed, integrating historical and forecast data from centralized regional PV plants; a multi-time-scale loss function that jointly accounts for both power and energy is employed to optimize prediction accuracy. Finally, a model integrity verification scheme based on commit-and-prove succinct non-interactive argument of knowledge (cp-SNARKs) is designed to ensure result credibility while preserving model confidentiality. Experimental validation using real-world data from a city in North China demonstrates that the proposed method significantly reduces forecasting errors for both power and energy, thereby improving PV power prediction accuracy. Requiring no meteorological inputs or system modifications, the approach features high data quality, superior prediction accuracy, low operational cost, and strong verifiability, making it readily extensible to other time-series forecasting tasks such as load forecasting and wind power prediction.

To address the scheduling failures, power imbalances, and economic losses in virtual power plants (VPPs) caused by multisource uncertainties—including stochastic renewable generation, load fluctuations, and parameter deviations—this paper develops a multi-timescale adaptive dispatch framework incorporating multi-source uncertainty modeling and online parameter correction. The framework employs two-stage robust optimization for day-ahead scheduling to generate a robust pre-dispatch plan, and introduces a state-feedback mechanism in the intra-day stage, where an improved quantum-inspired genetic algorithm is used to recursively correct critical parameters, thereby forming a closed-loop dispatch structure. Simulation experiments validate the effectiveness of the proposed approach. Results show that, under significant forecasting errors in renewable generation and electro-thermal loads, the method improves actual operational revenue by approximately 3.2% compared to conventional deterministic dispatch. Moreover, the online parameter correction strategy reduces system balancing costs by nearly 90% during most time periods. The framework effectively balances robustness, economic efficiency, and adaptability, offering a viable technical pathway for the secure and economical operation of VPPs under high uncertainty

Vibration monitoring and condition assessment are crucial technical means and management measures for ensuring the safe operation of large wind turbines， especially in the winter freezing environment of Yunnan-Guizhou Plateau， the vibration monitoring and state assessment of the antifreezing test wind turbine is particularly important. The icing random distribution of the wind turbine blade may lead to mass imbalance and aerodynamic shape change among three blades， combined with installation differences of new equipment or auxiliary materials in three blades of wind turbine for antiicing/deicing requirement， so these factors may collectively induce severe vibration of the wind turbine to result with equipment safety hazards. This study employs the active aerothermal method to construct three 5MW test wind turbines， and the primary influencing factors of turbine vibration is analyzed deeply. The vibration monitoring data during four months from three 5MW antifreezing test turbines were employed to calculate 9 key turbine vibration condition variables and comprehensively evaluate the vibration condition in combination with characteristic limit values， and the vibration development trend of each turbine was judged by longitudinally and horizontally comparing. The assessment results indicate that the vibration intensity and its difference among three test wind turbines are smaller， which can operate safely for a long period， and the impact of blade icing and antiicing modifications on vibration is not significant.