包含虚拟同步发电机的暂态稳定约束最优潮流模型研究

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

摘要

【目的】随着能源转型的持续推进，电力系统“低惯量、低阻尼”特性愈发明显，大量虚拟同步发电机(VSG)被引入电力系统。但以往的研究中，很少有研究考虑VSG对电力系统暂态稳定性的影响，为了维持电力系统的稳定、经济运行，针对含有VSG的电力系统对暂态稳定约束最优潮流(TSCOPF)模型进行了优化，提出考虑VSG暂态稳定约束的最优潮流(V-TSCOPF)模型。【方法】首先，将VSG模型嵌入传统TSCOPF模型中，表征VSG的动态特性。其次，优化输入特征量，并将VSG的虚拟功角纳入暂态稳定指标(TSI)考虑范围内，利用时空图注意力网络(ST-GAT)挖掘输入特征量与TSI之间的联系。再将ST-GAT嵌入含VSG的TSCOPF模型中构成基于优化特征的V-TSCOPF模型，并使用量子遗传算法(QGA)对模型进行高效求解。最后，在10机39节点和16机68节点系统上进行仿真验证。【结果】在10机39节点和16机68节点系统中，基于优化特征的V-TSCOPF模型相较于基于传统特征的TSCOPF模型能显著提升系统稳定性。在对模型求取的运行方式进行仿真验证时，V-TSCOPF模型求取的运行方式在故障实际发生后使所有发电机功角收敛，TSCOPF模型求取的运行方式在故障实际发生后出现功角发散现象。ST-GAT模型取得0.988 4的R²值，经22次迭代收敛，系统优化成本为422 919元人民币。QGA算法在收敛速度和求解精度方面均表现优异。【结论】基于优化特征的V-TSCOPF模型通过优化输入特征量和融合VSG虚拟功角，使电力系统的稳定性得到了有效保障，实现了VSG参数的在线调节，为解决VSG并网系统的暂态稳定问题提供了新的思路。ST-GAT与QGA算法的协同应用，实现了暂态稳定约束的精准表征与复杂模型的快速求解，为新型电力系统安全经济运行提供了解决方案。

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.

关键词

暂态稳定约束最优潮流 / 虚拟同步发电机 / 暂态稳定判据 / 量子遗传算法 / 时空图注意力网络 / 虚拟阻尼系数 / 虚拟惯性系数 / 影响因素

Key words

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

基金资助

国家自然科学基金项目(52407118)

梯级水电站运行与控制湖北省重点实验室(三峡大学)开放基金课题(2023KJX06)

电力系统智能运行与安全防御宜昌重点实验室(三峡大学)开放基金课题(2020DLXY06)