With the rapid development of China’s electricity spot market, the participation of Integrated Energy Systems (IESs) with multi-energy complementarity has become an inevitable trend in future energy development. However, IESs face difficulties in effectively matching heterogeneous resource capabilities with the diverse requirements of the multi-timescale spot market. Therefore, this paper proposes an optimization strategy for integrated energy system operation based on the hierarchical dispatch of flexibility resources, aiming to enhance the adaptability of different resources to multi-period markets. Firstly, a quantitative flexibility assessment framework is established from three key dimensions—power regulation range, energy shifting capacity, and dynamic response speed—to evaluate the market adaptability of various adjustable resources. Subsequently, the flexibility assessment results are converted into dynamic market participation ratios, which are incorporated as constraints into a Model Predictive Control (MPC)-based optimization model. In the day-ahead scheduling stage, the model prioritizes meeting fundamental electricity demand while dynamically reserving a portion of flexible capacity for participation in more profitable intra-day and real-time market services. Case studies demonstrate that the proposed strategy achieves real-time computational feasibility, significantly improves the economic performance of park-level IESs, and maintains stable dispatch behavior under market uncertainties and forecast deviations. The results indicate that the proposed hierarchical flexibility-oriented dispatch framework provides a practical and scalable solution for enabling IES participation in multi-timescale electricity spot markets.

Despite the continuous development of wind power forecasting methods, forecasting errors remain unavoidable, especially during extreme weather events. However, current research on quantifying these errors is quite limited. This paper proposes an adaptive error risk early warning method that can directly predict the magnitude of forecast errors and classify and warn of risks, thereby achieving proactive risk management. This method comprises three core designs. First, mechanism-based feature engineering captures the driving factors of error generation, including numerical weather prediction bias, atmospheric instability, and meteorological dynamics, all of which are key factors leading to forecast bias. Second, a stacked ensemble method integrates quantile regression, random forest, and gradient booster, utilizing complementary learning capabilities to handle high-dimensional non-stationary error patterns. Third, K-means clustering establishes a dynamic risk threshold that adapts to changes in seasonal error distribution, overcoming the limitations of fixed thresholds. Validation using actual wind farm operation data demonstrates significant improvements: the proposed ensemble model reduces the Root Mean Square Error (RMSE) by 2.5% compared to the best single model, and the dynamic threshold mechanism increases the High-Risk Recall rate from 89.7% to 96.9%. These results confirm that the method can effectively warn of high-error events and provide timely and actionable decision support to enhance grid stability and security.

Accurate wind power forecasting is essential for the stability of modern power systems. However, current probabilistic forecasting frameworks often encounter a fundamental conflict between the computational efficiency required for high-dimensional meteorological pattern matching and the physical consistency of the resulting probability distributions. Existing methods frequently fail to maintain the logical monotonicity of quantiles or overlook the fine-grained temporal correlations in massive historical datasets. To address these critical gaps, this research develops a comprehensive framework that synergizes a hierarchical similarity filtering mechanism with a structurally constrained non-crossing quantile regression model. First, the target sample is partitioned into several weather segments, and a new two-stage high-similarity weather pattern matching method is developed to screen multiple sets of historical samples that are highly similar to the target weather pattern. Second, a deep learning model for probabilistic wind power quantile forecasting is proposed, which incorporates historical data augmentation. The model utilizes an attention mechanism to extract the correlation between the target and historical segments, while an improved non-crossing quantile regression model is adopted to ensure the validity of the output quantiles. Finally, the effectiveness of the proposed method is validated through case studies using real-world data from an actual wind farm.