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储能系统技术 储能系统 ★ 5.0

变惯量电力系统中用于频率控制的在线事件触发切换方法

Online Event-Triggered Switching for Frequency Control in Power Grids With Variable Inertia

作者 Jie Feng · Wenqi Cui · Jorge Cortés · Yuanyuan Shi
期刊 IEEE Transactions on Power Systems
出版日期 2025年1月
技术分类 储能系统技术
技术标签 储能系统
相关度评分 ★★★★★ 5.0 / 5.0
关键词 可再生能源 时变惯性 频率控制 神经比例积分控制器 在线切换控制算法
语言:

中文摘要

可再生能源的高比例接入导致电力系统惯量时变,进而恶化频率动态性能。通过调节电力电子接口资源的功率设定值参与一次调频是一种有效缓解手段,但在时变惯量下控制器设计面临稳定性与最优性挑战。本文将时变惯量下的频率动态建模为非线性切换系统,各模式对应不同惯量水平下的非线性转子运动方程。提出一种神经比例-积分(Neural-PI)控制器结构,可保证各模式下输入-状态指数稳定。进一步设计在线事件触发切换机制,从针对不同惯量优化的Neural-PI控制器集中动态选取最优控制器。IEEE 39节点系统的仿真验证了所提方法在保证稳定性的同时显著提升变惯量下的频率控制性能。

English Abstract

The increasing integration of renewable energy resources into power grids has led to time-varying system inertia and consequent degradation in frequency dynamics. A promising solution to alleviate performance degradation is using power electronics interfaced energy resources, such as renewable generators and battery energy storage for primary frequency control, by adjusting their power output set-points in response to frequency deviations. However, designing a frequency controller under time-varying inertia is challenging. Specifically, the stability or optimality of controllers designed for time-invariant systems can be compromised once applied to a time-varying system. We model the frequency dynamics under time-varying inertia as a nonlinear switching system, where the frequency dynamics under each mode are described by the nonlinear swing equations and different modes represent different inertia levels. We identify a key controller structure, named Neural Proportional-Integral (Neural-PI) controller, that guarantees exponential input-to-state stability for each mode. To further improve performance, we present an online event-triggered switching algorithm to select the most suitable controller from a set of Neural-PI controllers, each optimized for specific inertia levels. Simulations on the IEEE 39-bus system validate the effectiveness of the proposed online switching control method with stability guarantees and optimized performance for frequency control under time-varying inertia.
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SunView 深度解读

该变惯量频率控制技术对阳光电源ST系列储能变流器和PowerTitan大型储能系统具有重要应用价值。文章提出的Neural-PI控制器与在线事件触发切换机制,可直接应用于阳光电源构网型GFM控制策略优化:在高比例新能源场景下,系统惯量随光伏SG逆变器出力波动而剧烈变化,传统固定参数VSG难以兼顾稳定性与动态性能。所提方法通过实时识别惯量水平并动态切换最优控制器,可显著提升ST储能系统的一次调频响应速度和频率稳定裕度。建议将该切换控制思想集成到iSolarCloud平台的智能调度模块,实现储能-光伏协同的自适应惯量支撑,增强电网友好性,提升产品在新型电力系统中的竞争力。