Research on transient stability-constrained optimal power flow model incorporating virtual synchronous generators

Abstract

[Objective] With the ongoing advancement of energy transition, the “low-inertia and low-damping” characteristics of power systems have become increasingly prominent, and a large number of virtual synchronous generators(VSGs) have been integrated into power systems. However, existing research has rarely considered the impact of VSGs on the transient stability of power systems. To maintain the stable and economic operation of power systems, the transient stability-constrained optimal power flow(TSCOPF) model is optimized for power systems incorporating VSGs, and a VSG-transient stability-constrained optimal power flow(V-TSCOPF) model is proposed. [Methods] First, the VSG model was embedded into the traditional TSCOPF model to characterize the dynamic characteristics of VSGs. Second, the input features were optimized, and the virtual rotor angle of VSGs was incorporated into the scope of the transient stability index(TSI). A spatiotemporal graph attention network(ST-GAT) was employed to extract the relationships between input features and TSI. Then, the ST-GAT was embedded into the TSCOPF model containing VSGs to form the V-TSCOPF model based on optimized features, and the model was solved efficiently using a quantum genetic algorithm(QGA). Finally, simulation validations were conducted on the 10-machine 39-bus and 16-machine 68-bus systems. [Results] The results showed that in the 10-machine 39-bus and 16-machine 68-bus systems, the V-TSCOPF model based on optimized features significantly improved system stability compared to the TSCOPF model based on traditional features. During the simulation validation of the obtained operating modes, the operating mode obtained by the V-TSCOPF model caused all generator rotor angles to converge after actual fault occurred, while the operating mode obtained by the TSCOPF model exhibited rotor angle divergence after the fault. The ST-GAT model achieved an R² value of 0.988 4, converged after 22 iterations, and the optimized system cost was 422 919 yuan RMB. The QGA algorithm performed excellently in terms of convergence speed and solution accuracy. [Conclusion] The results show that the V-TSCOPF model based on optimized features effectively ensures power system stability by optimizing input features and integrating the virtual rotor angle of VSGs, realizing online adjustment of VSG parameters, and providing novel insights for addressing transient stability issues in VSG grid-connected systems. The synergistic application of ST-GAT and QGA achieves precise characterization of transient stability constraints and rapid solving of complex models, providing solutions for the secure and economic operation of new-type power systems.

Keywords

transient stability-constrained optimal power flow / virtual synchronous generator / transient stability index / quantum genetic algorithm / spatiotemporal graph attention network / virtual damping coefficient / virtual inertia coefficient / influencing factors