This study aims to reduce reverse power and improve frequency regulation performance in hydropower systems. To achieve this objective, a refined hydropower plant (HPP) simulation model is developed and coupled with a battery energy storage system (BESS), implementing an Integrated Adaptive Virtual Droop Control (IAVDC) strategy. The refined HPP model achieves a simulation accuracy of 98.5%, representing a 26.2% improvement over conventional simplified models. With the BESS integrated under the IAVDC strategy, reverse power is completely eliminated, and frequency regulation time is substantially shortened. The results demonstrate that the joint HPP-BESS frequency regulation effectively mitigates the adverse impact of water hammer, while the proposed IAVDC strategy enhances system responsiveness and reduces frequency recovery time, thereby improving the quality of primary frequency control.

This paper focuses on power system scheduling problems, aiming to enhance energy utilization efficiency through multi-energy complementarity. To support the “dual-carbon” strategic goals, this paper proposes a coordinated dispatch model for hydro–thermal–wind–solar–pumped storage integrated energy systems, aiming to enhance energy utilization efficiency and system flexibility while reducing carbon emissions. To address issues such as premature convergence and low computational efficiency in traditional optimization algorithms for multi-energy complementary dispatch, an improved Artificial Bee Colony algorithm named Super-quality Variation Burst Artificial Bee Colony (SVBABC) is developed, which incorporates elite solution guidance and an explosion variation mechanism. Simulation results based on a regional practical power system demonstrate that compared to classical methods (e.g., Artificial Bee Colony, Fireworks Algorithm, and Ant Lion Optimizer), SVBABC exhibits significant advantages in global optimization capability and convergence stability. This study provides an innovative solution for efficient dispatch of multi-energy complementary systems. Through synergistic regulation of pumped storage and thermal power, the accommodation capability of renewable energy is effectively enhanced, thereby providing critical technical support for the development of new power systems.

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on the comprehensive characteristic curves of the Francis turbine provided by the manufacturer, using the external characteristic method. Traditional modeling methods mostly adopt manual reading of points or the use of dedicated numerical software for curve tracing to discretely sample the comprehensive characteristic curves of the turbine. This method is labor-intensive, inefficient, and relies on manual experience, with a small sample size, which, to some extent, affects the accuracy and reliability of the numerical processing results and cannot meet the needs of transient process simulation analysis. To address these shortcomings, this paper proposes a refined modeling method based on image numerical processing and an RBF neural network. Taking the HLA685 Francis turbine as an example, the method first uses image processing to achieve large-scale automated discrete sampling of the turbine’s high-efficiency zone characteristic data, then reasonably extends the small-opening and low-speed regions, and finally uses the RBF neural network method for interpolation and extrapolation to obtain the full characteristic data. This method can effectively improve the efficiency and accuracy of comprehensive characteristic modeling of the turbine and has good reference significance for the comprehensive characteristic modeling of blade-type machinery.

This paper proposes an efficient analytical modeling method for the air-gap flux density in hydro-generators under eccentricity faults. The method specifically addresses the multi-factor coupling characteristics induced by the combined effects of stator slotting, salient-pole rotor structure, and eccentricity. Based on geometric analytical derivation, individual models for air-gap length considering stator slotting, rotor salient-pole structure, and eccentricity are established, and an analytical expression for the air-gap length under coupled conditions is derived. By incorporating the spatial distribution characteristics of the magnetomotive force, a mathematical model for no-load air-gap flux density is constructed, enabling the rapid calculation of air-gap magnetic field distribution. Finite element verification conducted on an 84-slot, 10-pole hydro-generator demonstrates that the proposed method accurately reflects the periodic fluctuations and amplitude variations in the magnetic flux density under both normal and eccentric conditions. Compared with the finite element method (FEM), the analytical approach significantly enhances computational efficiency while maintaining high accuracy, providing effective theoretical support for the structural optimization and eccentricity fault diagnosis of hydro-generators.

The increasing integration of high-penetration renewable energy has led to prominent issues in power grid stability. This paper proposes a hybrid modeling method that integrates Kernel Density Estimation (KDE) with the Stochastic Response Surface Method (SRSM), addressing the dual challenges of insufficient accuracy in traditional parametric distribution models and the low efficiency of Monte Carlo simulations. By employing non-parametric probabilistic modeling to accurately characterize the spatiotemporal characteristics of wind-solar output, a refined model for wind turbine and photovoltaic generation is established. Combined with SRSM, a coordinated regulation mechanism between hydropower and renewables under fault scenarios is constructed to support system stability control strategies. A case study on a certain hydropower station demonstrates that the proposed method can accurately reflect system responses, providing theoretical support for the secure operation of power grids with high renewable penetration.