The waste heat energy efficiency for absorption heat pump recycling thermal power plant circulating water has been analyzed. After the operation of heat pump, the influences on power generation and heat generation of unit were taken into account. In the light of the characteristics of heat pump in different operation stages, the energy efficiency of heat pump was evaluated comprehensively on both sides of benefits belonging to electricity and benefits belonging to heat, which adopted the method of contrast test. Thus, the reference of energy efficiency for same type projects was provided.

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.

Conventional control strategies for battery energy storage systems (BESSs) fail to achieve symmetrical and coordinated control between the DC/DC and DC/AC conversion stages, resulting in unsatisfactory DC capacitor voltage fluctuation suppression and threatening the safe and stable operation of the system. To address this issue, this study proposes an improved model predictive control (MPC)-based control method that explicitly considers DC capacitor voltage fluctuation suppression. First, a dynamic mathematical model of the BESS is established by jointly considering its DC/DC and DC/AC energy conversion stages. The model is then discretized using the first-order forward Euler method to facilitate controller implementation. Second, the cost function of the proposed MPC-based control method is designed to simultaneously incorporate DC capacitor voltage fluctuation suppression and output current tracking errors on both the DC and AC sides. Finally, the switching states of the DC and AC converters are selected as the control set, and the optimal switching signals for the BESS are determined by optimizing the aforementioned cost function. Verification results demonstrate that, compared with traditional control strategies, the proposed strategy achieves more symmetrical stable and dynamic performance and reduces DC side capacitor voltage fluctuation by approximately 80%, thereby effectively ensuring the safe and stable operation of the system.

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.