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储能系统技术 储能系统 调峰调频 ★ 5.0

面向快速频率响应的模块化电池性能感知控制

Performance-Aware Control of Modular Batteries for Fast Frequency Response

作者 Yutong He · Guangchun Ruan · Haiwang Zhong
期刊 IEEE Transactions on Sustainable Energy
出版日期 2025年4月
技术分类 储能系统技术
技术标签 储能系统 调峰调频
相关度评分 ★★★★★ 5.0 / 5.0
关键词 模块化电池 频率调节 调度方法 功率损耗成本 电池老化成本
语言:

中文摘要

模块化电池可聚合用于电力系统的频率调节。尽管利用电池模块的闲置容量具有显著经济效益,但其在动态运行效率与老化特性上的异质性仍带来优化挑战,且快速频率响应需在秒级完成实时决策。本文提出一种性能感知的电池模块调度方法,通过混合整数二次约束规划模型,综合考虑电池组及变换器的导通损耗与开关损耗,并结合基于循环的老化模型,引入老化次梯度计算与线性化方法量化频繁充放电下的老化成本。基于真实电池数据的案例研究表明,相比传统方法,该方法可降低功率损耗成本28%–57%,减少电池老化成本4%–15%,并有效改善荷电状态均衡性。

English Abstract

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%–57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance.
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SunView 深度解读

该模块化电池性能感知控制技术对阳光电源ST系列储能变流器及PowerTitan大型储能系统具有直接应用价值。研究提出的混合整数二次约束规划模型可优化阳光电源储能系统在电网调频服务中的模块调度策略,通过精确量化变换器导通损耗与开关损耗,结合循环老化模型,可显著提升ST变流器在快速频率响应场景下的经济性。该方法降低28%-57%功率损耗和4%-15%老化成本的效果,可直接应用于PowerTitan系统的能量管理系统(EMS)优化,改善电池簇SOC均衡性,延长系统寿命。技术思路对阳光电源开发面向辅助服务市场的智能调度算法、提升iSolarCloud云平台预测性维护能力具有重要参考价值。