一种基于深度强化学习算法的电网有功安全校正方法

电力系统有功安全校正对于保障电网安全运行具有重要意义。传统有功安全校正方法无法综合考虑系统潮流分布状态和机组的调整性能,求解效率低、涉及调整的机组多,存在调整反复的现象,在实际应用中具有一定困难。因此,采用深度强化学习算法,提出一种基于深度Q网络(Deep Q Network, DQN)的有功安全校正策略。首先,建立系统有功安全校正模型。其次,采用卷积神经网络(Convolutional Neural Networks, CNN)挖掘电网运行状态深层特征。进一步利用DQN算法通过“状态-动作”机制,以“奖励”为媒介,构建电网运行状态与最优调整机组组合的映射模型,确定调整机组。最后,根据过载线路对调整机组的灵敏度,计算得到调整量。IEEE39节点系统的验证结果表明,所提出的有功安全校正策略在处理多线路过载时可综合考虑系统潮流分布的总体状况和机组调节性能,高效地消除线路过载。

A deep reinforcement learning algorithm-based active safety correction method for power grids

Active safety correction of a power system is of great importance in ensuring the safe operation of a power grid. The traditional active safety correction method cannot comprehensively consider the system power flow distribution state and the adjustment performance of the units, and has difficulties in practical application because of the low solution efficiency, the adjustment involved in many units, and the need for repeated adjustment. Therefore, an active safety correction strategy based on the deep Q network (DQN) by using a deep reinforcement learning algorithm is proposed. First, a system active safety correction model is established; secondly, convolutional neural networks (CNN) are used to explore the deep features of the grid operation state. The DQN algorithm is used to construct a mapping model of the combination of power grid operation state and optimal adjustment unit through the mechanism of "state-action" and the medium of "reward", and the adjustment unit is determined. Finally, the adjustment quantity is calculated according to the sensitivity of overload line to the adjusting unit. The validation results of the IEEE39-bus system show that the active safety correction strategy proposed can comprehensively consider the overall situation of system power flow distribution and unit regulation performance when dealing with multi-line overload, and effectively eliminate line overload.This work is supported by the Science and Technology Project of State Grid Corporation of China (No. SGTYHT/17-JS-199).