换流器-电网交互稳定性保证的储能系统电网频率支撑安全深度强化学习
Converter-Grid Interaction Stability Guaranteed Safe Deep Reinforcement Learning for Energy Storage Systems in Grid Frequency Support
浏览论文内容
中文总结 AI 辅助
针对储能系统并网换流器与电网交互稳定性问题，提出一种安全深度强化学习方法，通过双DNN稳定域和稳定性投影层硬约束，实现100%稳定性的频率调节，提升调节性能。
中文摘要 AI 辅助
换流器接口可再生能源（RESs）的日益普及加剧了稳定性挑战。储能系统（ESS）能够提供快速灵活的频率支撑以缓解频率偏差。然而，ESS的接口换流器可能遭遇换流器-电网交互稳定性问题。本文提出一种换流器-电网交互稳定性保证的安全深度强化学习（CIS-DRL）方法，用于集成ESS的电力系统频率调节。我们首先获得一个基于双深度神经网络（DNN）的稳定域，以识别保证的换流器-电网交互稳定性。接着，设计了一种新颖的换流器-电网交互稳定性安全TD3（CIS-STD3）算法，该算法集成一个稳定性可行性投影层，在执行前将不安全动作映射到稳定动作集，从而在整个学习过程中将换流器-电网交互稳定性作为硬约束强制执行。所提方法使ESS能够以100%的换流器-电网交互稳定性无违规地提供电网频率支撑。实验结果表明，所提出的CIS-DRL方法在防止不稳定运行点的同时实现了改进的频率调节性能，展示了其在实时ESS频率支撑中的实际适用性。
英文摘要
The growing integration of converter interfaced renewable energy resources (RESs) intensifies stability challenges. Energy storage system (ESS) can provide fast and flexible frequency support to mitigate frequency deviations. However, the interface converter of ESS may encounter converter-grid interaction stability issues. This paper proposes a converter-grid interaction stability guaranteed safe DRL (CIS-DRL) method for ESS integrated power systems to achieve frequency regulation. We first obtain a double DNN-based stability region to identify the guaranteed converter-grid interaction stability. Next, a novel converter-grid interaction stability Safe-TD3 (CIS-STD3) algorithm is designed that integrates a stability feasibility projection layer to map unsafe actions into stable action set before execution, enforcing converter-grid interaction stability as a hard constraint throughout learning process. The proposed approach enables ESS for grid frequency support with 100% converter-grid interaction stability without violations. Experimental results show that the proposed CIS-DRL method achieves improved frequency regulation performance while preventing unstable operating points, demonstrating its practical applicability for real time ESS frequency support.
