| 引用本文: | 周雪松,马培铭,练继建,等.基于SAC算法的混储微网观测修正自抗扰稳压策略[J].电力系统保护与控制,2026,54(07):116-128. |
| ZHOU Xuesong,MA Peiming,LIAN Jijian,et al.Observation-corrected active disturbance rejection voltage stabilization strategy for hybrid storage microgrid based on SAC algorithm[J].Power System Protection and Control,2026,54(07):116-128 |
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| 摘要: |
| 新型电力系统中储能双向DC-DC变换器作为新能源高比例消纳的柔性枢纽，在微电网的多重不确定性影响下，面临着输出侧电压失稳风险。因此，提出一种基于柔性动作-评价(soft actor-critic, SAC)算法辅助寻优的观测修正自抗扰控制技术。首先，引入二维扰动信息至待补偿项中进行扰动状态量的协同观测，同时设计迟滞函数修正微分环节的固有缺陷，从而精准重构总和扰动。随后，量化参数整定准则，并借助SAC算法的最大熵学习框架与随机策略探索，实现控制器参数在多频域扰动下的柔性匹配，使储能系统能更加充分发挥“削峰填谷”的调控作用。最后，在不同工况的仿真对比下，验证了所提策略在多种内外不确定扰动下均具备良好的动态性能。 |
| 关键词: DC-DC变换器 混储微电网 SAC算法 迟滞函数 二维扰动 |
| DOI：10.19783/j.cnki.pspc.250857 |
| 分类号: |
| 基金项目:国家自然科学基金重大项目资助(U24B6011)；国家自然科学基金重点项目资助(U23B20142) |
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| Observation-corrected active disturbance rejection voltage stabilization strategy for hybrid storage microgrid based on SAC algorithm |
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ZHOU Xuesong1, MA Peiming1, LIAN Jijian2, LI Suyang1, TAO Long1, LIU Xiaolin1
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1. Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control (Tianjin University of
Technology), Tianjin 300384, China; 2. National Key Laboratory of Intelligent Construction and Operation and
Maintenance of Hydraulic Engineering, Tianjin University, Tianjin 300350, China
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| Abstract: |
| In new power systems, bidirectional DC-DC converters for energy storage serve as flexible hubs for accommodating high-penetration of renewable energy. However, under multiple uncertainties in microgrids, they face the risk of voltage instability. To address this issue, an observation-corrected active disturbance rejection control (ADRC) strategy assisted by the soft actor-critic (SAC) algorithm is proposed. First, two-dimensional disturbance information is incorporated into the compensation term to enable coordinated observation of disturbance state variables. Meanwhile, a hysteresis function is designed to compensate for the inherent shortcomings of the differential component, thereby accurately reconstructing the total disturbance. Subsequently, parameter tuning criteria are quantified, and the maximum entropy learning framework and stochastic policy exploration of the SAC algorithm are employed to achieve flexible matching of controller parameters under multiple disturbances, enabling the energy storage system to better perform peak shaving and valley filling. Finally, simulation comparisons under various operating conditions verify that the proposed strategy exhibits excellent dynamic performance under multiple internal and external uncertainties. |
| Key words: DC-DC converters hybrid storage microgrid SAC algorithm hysteresis function two-dimensional disturbance |
