发表机构
Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对微电网频率控制的泛化与适应性难题，本文提出Prompt-DT架构，结合少样本提示、自监督对比学习与物理信息提示设计，实现高效控制与良好泛化性。
AI 中文摘要
能源结构的快速演进使微电网成为下一代电力系统的关键组成部分，具备更强的弹性与可再生能源整合能力。但微电网固有的低惯性、复杂动态特性及较差的模型条件，亟需先进的数据驱动型频率控制策略。尽管强化学习（RL）已展现出一定潜力与优势，现有RL方法往往难以在不同微电网配置间实现泛化，且对未知环境的适应性不足，尤其在无法获取显式系统参数时。为应对这些挑战，本文提出一种新型提示决策变换器（Prompt-DT）架构用于微电网频率控制。与依赖难以获取的环境特征参数的传统方法不同，该方法利用少样本专家历史轨迹作为提示，引导自主感知与自适应决策。此外，本文提出一种结合自监督对比学习的上下文感知训练与执行机制，以提升环境识别与提示利用效率；还提出一种基于累积奖励与频率波动率筛选提示的物理信息提示设计技术，确保在线执行时获得高质量物理指导。最后，为在数据有限的未知环境中保证泛化性，本文开发了一种轻量级微调方法，仅需极少调整即可达到与全参数微调相当的性能。
英文摘要
The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilience and renewable energy integration. However, the inherent low inertia, complex dynamics, and poor model conditions of microgrids necessitate advanced data-driven frequency control strategies. Although reinforcement learning (RL) has demonstrated certain potential and advantages, existing RL methods often struggle with generalization across diverse microgrid configurations and lack adaptability to unseen environments, particularly when explicit system parameters are unavailable. To address these challenges, in this paper, we introduce a novel prompt decision transformer (Prompt-DT) architecture for microgrid frequency control. Unlike traditional approaches that rely on hard-to-obtain environmental characteristic parameters, the proposed method leverages few-shot expert historical trajectories as prompts to guide autonomous perception and adaptive decision-making. In addition, we propose a context-aware training and execution mechanism utilizing self-supervised contrastive learning to enhance environment recognition and prompt utilization efficiency. In addition, a physics-informed prompt design technique that filters prompts based on cumulative reward and frequency volatility is proposed, ensuring high-quality physical guidance during online execution. Finally, to ensure generalization in unseen environments with limited data, we develop a lightweight finetuning approach that achieves performance comparable to full-parameter finetuning with minimal adjustments.
