基于深度强化学习的电网自主控制与决策技术

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

高比例可再生能源的并网和电力电子设备的不断增加给电力系统运行与实时控制带来诸多挑战。人工智能技术的飞速发展为解决高维度、高非线性、高时变性优化控制和决策问题提供了新的思路。文中基于深度强化学习技术,提出了具有在线学习功能的电网自主优化控制和决策框架,即“电网脑”系统。该系统可通过离线和在线学习不断积累经验,从而在亚秒时间内(1 s以内)根据电网实时量测数据给出调度控制指令及预期控制效果。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。为验证“电网脑”理论框架的可行性,文中以电网自主电压控制和联络线潮流控制为例,介绍了电力系统自主控制与决策方法及其实现流程,并通过数值实验验证了所提方法学习能力及其应用于电力系统自主控制与决策的可行性。

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.