| 引用本文: | 周雪松,王馨悦,马幼捷,等.基于信息熵量化评价的深度强化学习储能微网柔性补偿自抗扰稳压技术[J].电力系统保护与控制,2025,53(23):127-138. |
| ZHOU Xuesong,WANG Xinyue,MA Youjie,et al.Flexible disturbance-rejection voltage regulation for energy storage microgrids using deep reinforcement learning with information entropy-based quantitative evaluation[J].Power System Protection and Control,2025,53(23):127-138 |
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| 摘要: |
| 直流微电网母线电压稳定是实现新能源高水平利用的重要前提。为解决源荷互动不确定性引起的母线电压波动问题，提出了一种基于信息熵量化评价的深度强化学习储能微网柔性补偿自抗扰稳压技术。首先，分析了传统扰动补偿在能量交互不确定性下的局限。其次，运用信息熵评价指标量化不确定性，以无序系统有序化为目标引入深度强化学习设计柔性补偿，确保算法在母线电压稳定范围内收敛。进而，从变结构、变参数、变阻尼三方面剖析柔性补偿改善系统特性的内涵。最后，通过仿真实验验证得到，柔性补偿技术在源荷储多种不确定性工况下具有显著抗扰性能优势，可改善直流母线电压稳定性。 |
| 关键词: 储能变换器 自抗扰 深度强化学习 信息熵 抗扰性 |
| DOI：10.19783/j.cnki.pspc.250175 |
| 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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| Flexible disturbance-rejection voltage regulation for energy storage microgrids using deep reinforcement learning with information entropy-based quantitative evaluation |
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ZHOU Xuesong1, WANG Xinyue1, MA Youjie1, TAO Long1, WEN Hulong2, ZHAO Ming3
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1. Tianjin University of Technology, Tianjin 300384, China; 2. Tianjin Ruineng Electric Co., Ltd., Tianjin 300381, China;
3. Chengde Electric Zhishang Energy Saving Technology Co., Ltd., Chengde 067000, China
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| Abstract: |
| Stable DC bus voltage is a critical prerequisite for achieving high utilization of new energy in DC microgrids. To address bus voltage fluctuations caused by uncertainties in source-load interactions, a flexible disturbance-rejection voltage regulation technique for energy-storage microgrids is proposed, based on deep reinforcement learning (DRL) with information entropy-based uncertainty quantification. First, the limitations of traditional disturbance compensation under uncertain energy interactions are analyzed. Second, information entropy evaluation index is used to quantify uncertainty, and DRL is introduced with the objective of transforming a disordered system into an ordered one, enabling flexible compensation and ensuring algorithmic convergence within the allowable voltage range. Furthermore, mechanism by which flexible compensation enhances system performance is examined from three perspectives: variable structure, variable parameters, and variable damping. Finally, simulation results verify that the proposed flexible compensation technology offers strong disturbance-refection capability under a wide range of uncertainties in source-load-storage interactions, significantly improving DC bus voltage stability. |
| Key words: energy storage converter active disturbance rejection control deep reinforcement learning information entropy disturbance rejection performance |
