基于深度强化学习的电网自主控制与决策技术

高比例可再生能源和电力电子设备渗透率的不断增加给电力系统运行与调控带来诸多挑战。本文基于深度强化学习技术（深度确定策略梯度, DDPG）提出了具有在线学习功能的电网自主优化控制与决策框架,即“电网脑”系统;通过不断的学习和经验累积,AI智能体可以在亚秒级时间内根据实时量测数据给出调控指令及预期效果。该系统近期可用于辅助调度员决策,远期可为自动调度提供技术手段。本文以电网电压和联络线潮流控制为例,从多方面详细介绍了自主调控的方法,包括问题描述、控制目标和样本设定、奖惩机制定义、状态空间和控制动作集定义、算法实现流程等。大量的数值仿真实验验证了所提方法强大的学习能力以及应用于电力系统自主控制与决策的可行性。

Deep-reinforcement-learning based autonomous control and decision making for power systems

Modern power grids are facing grand operational challenges due to highly intermittent and uncertain renewable energies as well as new types of loads, etc. In recent years, the rapid development of artificial intelligence (AI) technology has brought up new solutions for optimal control problems with high dimension, high nonlinearity and high dynamics. Based on deep reinforcement learning (DRL), a novel autonomous control platform is presented, which can realize online learning and decision making for power system dispatch and control. The target of the proposed control platform is to transform massive real-time measurements directly into control decisions within sub-second. In order to fully demonstrate the feasibility of the "grid mind", autonomous voltage control and line flow control are taken as two examples to formulate the methodology of DRL-based power system dispatch and control problem. Finally, both deep-Q-network and deep deterministic policy gradient algorithms are applied to demonstrate the strong learning capability of DRL agents and their effectiveness through extensive simulation results.