针对含混合储能微电网调度中忽略光伏/风电出力波动及荷电状态变化,导致并网波动超限及混合储能内能量调度效率降低等问题,提出一种计及初始荷电状态的双层跟踪调度方法。上层控制通过预测控制模型对微电网内光伏/风电出力进行优化,建立微电网跟踪调度模型。基于上层调度模型,下层控制中确定混合储能调度日的荷电状态初值,应用动态规划算法对混合储能中蓄电池、超级电容的容量进行分配,并将荷电状态变化量反馈至上层预测控制模型,优化并网功率交互。以并网功率波动最小与混合储能系统的荷电状态偏差最小为目标对系统进行仿真实验。实验分析结果表明：采用的控制策略具有并网抑制功能,实现了良好的跟踪调度,兼顾了混合储能内不同储能元件的充放电特性,实现混合储能内功率协调配置。
Abstract
Numerous problems exist in operating a microgrid with a hybrid energy storage system （HESS）. These include excessive grid-connected power fluctuations and low dispatch efficiency. These issues are induced by ignoring the fluctuations in photovoltaic/wind power output and the dynamic changes in the state of charge of the hybrid energy storage. To address these issues, this study proposed a two-layer microgrid control method taking into account the initial state of charge of the HESS. Specifically, the photovoltaic/wind power output in the microgrid was optimized in the upper layer with the predictive control model, and the microgrid trace dispatch model was established. The initial state of charge on the HESS dispatch day was determined based on the dispatch model in the upper layer, and the capacities of the storage battery and the super-capacitor in the HESS were distributed with the dynamic planning algorithm. Moreover, the change in the state of charge was fed back to the predictive control model in the upper layer for optimizing the grid-connected power interaction. Next, the simulation was performed on the system focusing on minimizing both grid-connected power fluctuations and the state-of-charge deviation of the HESS. The study shows that the proposed control strategy displays desirable grid-connected suppression performance and achieves favorable trace dispatch. The proposed strategy consideres the charge and discharge characteristics of different energy storage elements to achieve coordinated power dispatch in the HESS.
关键词
混合储能系统 /
模型预测控制 /
荷电状态 /
双层控制
Key words
hybrid energy storage system /
model predictive control /
state of charge /
two-layer control
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基金
江苏省“青蓝工程”; 校科研基金（JSEIY2021003）
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