基于约束型深度强化学习的主动配电网电压控制策略

Volt/Var control strategy for active distribution network based on constrained deep reinforcement learning

随着分布式电源与随机性负荷的大量接入,配电网的电压波动问题变得愈发严重。主动配电网能通过各种电压无功控制器平抑电压波动,但通常需要求解一个复杂的混合整数二阶锥规划问题,难以做到实时控制。文中利用深度强化学习建立了一个主动配电网实时电压控制模型,能快速得到满足潮流约束的控制策略。采集节点有功、节点无功、设备档位、时间步作为环境状态变量;以和网损及设备操作相关的费用作为回报函数来协调三个控制设备;通过基于长短时记忆网络的约束型强化学习来求解,从而建立主动配电网实时电压控制模型。基于4节点测试系统和IEEE-33节点测试系统进行了仿真,仿真结果表明,所提的深度强化学习方法能确保潮流约束,电压控制模型能实时控制电压无功控制器,以保证配电网的电压质量。

With the access of distributed power and random load,the problem of voltage fluctuation in distribution network becomes more and more serious.Active distribution network can suppress voltage fluctuation through various voltage and reactive power controllers,but it is usually necessary to solve a complex mixed integer second-order cone programming problem,which is difficult to achieve real-time control.In this paper,a real-time voltage control model of active distribution network is established by using deep reinforcement learning,which can quickly get the control strategy satisfying the power flow constraints.It collects node active power,node reactive power,transformer voltage regulating gear and time step as environmental state variables.It coordinates the three control equipments with the cost related to network loss and equipment operation as return function.It solves the problem through constraint-based reinforcement learning based on long short-term memory network,so as to establish a real-time voltage control model of active distribution network.Based on the 4-node test system and IEEE 33-node test system,the simulation results show that the proposed deep reinforcement learning method can ensure the power flow constraints,and the voltage control model can control the voltage and reactive power controller in real time to ensure the voltage quality of the distribution network.