Lei Wang

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.

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.