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.

Against the backdrop of increasing penetration of renewable energy sources such as wind and solar power, coupled with intermittent regional power restrictions, ensuring the quality of power transmission has become increasingly critical. The volatility and uncertainty of wind and photovoltaic output exacerbate dynamic fluctuations in net load on the grid side, necessitating hydroelectric units to undertake more frequent Automatic Generation Control (AGC) regulation tasks in complementary hydro–wind–solar operations. However, frequent regulation processes significantly intensify the operational stress on actuating mechanisms within the governor system, thereby accelerating wear and degradation of equipment such as hydraulic turbine servomotors. This study employs modeling and simulation to investigate the influence and mechanistic role of key control parameters in the AGC process on the wear of hydraulic turbine servomotors. Utilizing pulse count and pulse width metrics, a reasonable quantification of this impact is established. A multi-objective optimization framework for AGC parameters is constructed, and frontier solutions are selected based on quantified equipment wear values. Simulation results indicate that the optimized parameters achieve a balanced performance in terms of settling time, steady-state performance, and comprehensive dynamic metrics during power closed-loop transition processes. This approach effectively mitigates the actuation intensity of servomotors while satisfying regulation quality requirements, thereby enhancing the overall performance of the power closed-loop adjustment process.

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.

To address the key problems of low-frequency oscillation and insufficient regulation accuracy at the Point of Common Coupling (PCC) in hydro–wind–photovoltaic hybrid systems, which are caused by the randomness of wind and photovoltaic output, the water-hammer effect of hydropower units, and multi-source power coupling, a joint control strategy based on Fractional-Order Proportional Integral Derivative (FOPID) and Co-evolutionary Multi-objective Particle Swarm Optimization (CMOPSO) is proposed. First, a small-signal transfer function model of the system covering photovoltaic inverters, doubly fed induction generators (DFIGs), hydropower units and voltage-source converter-based high-voltage direct current (VSC-HVDC) converter stations is established to accurately characterize the water-hammer effect and multi-source dynamic coupling characteristics. Second, a Caputo-type FOPID controller is designed. Compared with traditional integer-order controllers with limited tuning flexibility, the FOPID controller utilizes its five degrees of freedom to address specific multi-source coupling challenges. This precisely compensates for the non-minimum phase lag caused by the water-hammer effect in hydropower units via the fractional derivative link, and effectively smooths the impact of stochastic wind–solar fluctuations on PCC voltage through the memory characteristics of the fractional integral link. This multi-parameter regulation mechanism prevents a trade-off between response speed and overshoot suppression, achieving effective decoupling of complex multi-source dynamic interactions. Third, a dual-objective optimization framework with the Integral of Time-weighted Absolute Error (ITAE) and Oscillatory Disturbance Risk Index (ODRI) as the objectives is constructed. The multi-population co-evolution mechanism of the CMOPSO algorithm is adopted to solve the Pareto-optimal solution set, realizing the coordinated optimization of dynamic response accuracy and oscillation instability risk. Finally, comparative simulations are carried out on the Simulink platform with traditional PI/FOPI controllers and optimization algorithms such as Multi-objective Particle Swarm Optimization based on the Decomposition/Simple Indicator-Based Evolutionary Algorithm (MPSOD/SIBEA). The results show that the proposed strategy can effectively suppress low-frequency oscillations in the range of 0~30 Hz. Compared with the traditional PI controller, the PCC voltage overshoot is reduced by more than 40%, the oscillation decay time is shortened by 33%, the ITAE and ODRI indices are decreased by 12.58% and 2.47%, respectively, and the stability of DC bus voltage is significantly improved. Its robustness and comprehensive control performance are superior to existing methods, providing an efficient and stable control scheme for power electronics-dominated complex new energy grid-connected systems.

Hydro-wind-solar hybrid power systems have become a mainstream configuration for new-type power systems. However, the high proportion of power-electronics-interfaced generation alters system inertia and damping characteristics, leading to complex high-dimensional frequency dynamics and severe stability challenges. This paper investigates the frequency response mechanism and Hopf bifurcation characteristics of the aggregated frequency response model for hydro-wind-solar hybrid power systems. First, primary frequency response models for hydropower, wind power, and photovoltaic (PV) generation are established under a small-signal analysis framework. On this basis, a tenth-order nonlinear dynamic model of the integrated system is constructed by considering hydraulic nonlinearities, virtual inertia control of wind power, and reserve-based frequency regulation of PV systems. Then, Hopf bifurcation theory is applied to analyze stability and oscillatory instability mechanisms of the high-dimensional system. The bifurcation conditions are derived via high-dimensional Jacobian matrix analysis and Routh-Hurwitz criterion, with supplementary normal form calculation and first Lyapunov coefficient derivation to identify the supercritical/subcritical nature of the bifurcation. Finally, numerical simulations under both small and large disturbances validate the theoretical analysis. The results demonstrate that variations in key control parameters may induce Hopf bifurcation, leading the high-dimensional system from a stable equilibrium to sustained low-frequency oscillations. The findings provide insights and practical guidance for stable operation and parameter tuning of hydro-wind-solar hybrid power systems.

Wind–photovoltaic (PV) modular-multilevel-converter-based high-voltage direct-current (MMC-HVDC) collection systems have limited inherent inertia and are vulnerable to frequency disturbances caused by renewable power fluctuations. Moreover, frequency deterioration and recovery impose different and partially conflicting requirements on virtual inertia and damping. To address this problem, an asymmetric Gaussian fuzzy coordination method is proposed for the virtual synchronous generator (VSG)-controlled sending-end MMC. A low-frequency model is first established to characterize the effects of wind–photovoltaic power variations on the collection-bus frequency. The frequency deviation and rate of change of frequency (RoCoF) are then used to identify the frequency-evolution state and generate coupled adjustments of virtual inertia and damping. Nonuniform and asymmetric Gaussian membership functions enhance sensitivity near the nominal operating point and distinguish the regulation requirements during deterioration and recovery. Simulations under three representative disturbance cases show that the proposed method reduces the maximum frequency deviation by 19.4–28.6% and shortens the recovery time by 17.5–24.4% compared with fixed-parameter control. The alternating-current voltage and current and direct-current-link voltage remain within acceptable ranges. Furthermore, 20 randomized tests demonstrate consistent frequency performance without current or voltage-limit violations.