不确定性感知的暂态稳定约束预防性再调度：一种分布强化学习方法
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach
AI总结：
本文首次提出图神经网络引导的分布深度强化学习方法GD2RL，通过图神经网络暂态仿真器与分布强化学习相结合，求解不确定性感知的暂态稳定约束预防性再调度问题，并在新英格兰39节点系统上验证了其有效性。
摘要：
暂态稳定约束的预防性再调度在保障电力系统安全与稳定方面发挥着关键作用。由于再调度策略需要同时满足复杂的暂态约束和经济性需求，基于模型的建模与优化变得极具挑战性。此外，可再生能源引入的日益增长的不确定性和波动性，正推动系统稳定性考量从确定性转向概率性，这进一步加剧了复杂性。本文首次提出了一种图神经网络引导的分布深度强化学习（GD2RL）方法，用于求解不确定性感知的暂态稳定约束预防性再调度问题。首先，通过监督学习训练一个基于图神经网络的暂态仿真器，以稳态和故障作为输入，高效生成故障后转子角曲线，该仿真器在强化学习的环境交互过程中充当运行状态的特征提取器和代理时域仿真器。随后，应用具有系统运行条件显式不确定性分布的分布深度强化学习来生成再调度策略，以平衡用户指定的概率稳定性性能和经济性偏好。再调度后暂态稳定指标的完整分布直接作为输出提供。在改进的新英格兰39节点系统上的案例研究验证了所提方法的有效性。
英文摘要：
Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need, model-based formulation and optimization become extremely challenging. In addition, the increasing uncertainty and variability introduced by renewable sources start to drive the system stability consideration from deterministic to probabilistic, which further exaggerates the complexity. In this paper, a Graph neural network guided Distributional Deep Reinforcement Learning (GD2RL) method is proposed, for the first time, to solve the uncertainty-aware transient stability-constrained preventive redispatch problem. First, a graph neural network-based transient simulator is trained by supervised learning to efficiently generate post-contingency rotor angle curves with the steady-state and contingency as inputs, which serves as a feature extractor for operating states and a surrogate time-domain simulator during the environment interaction for reinforcement learning. Distributional deep reinforcement learning with explicit uncertainty distribution of system operational conditions is then applied to generate the redispatch strategy to balance the user-specified probabilistic stability performance and economy preferences. The full distribution of the post-redispatch transient stability index is directly provided as the output. Case studies on the modified New England 39-bus system validate the proposed method.
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