论文标题
基于智能分配系统中的基于伏特 - var的深度增强学习优化
Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems
论文作者
论文摘要
本文通过在不平衡的分布系统中通过多代理深钢筋学习（MADRL）开发了一种无模型的VAR优化（VVO）算法。此方法是新颖的，因为我们将VVO问题施加在不平衡的分配网络中，将VVO问题置于智能的深Q-Network（DQN）框架，该框架在面对系统的时变操作条件时避免直接求解特定的优化模型。我们将可切换电容器，电压调节器和安装在分布式发电机上的智能逆变器的状态/比率视为DQN代理的动作变量。精心设计的奖励函数指导这些代理与分配系统相互作用，并同时加强电压调节和功率损失。径向三相分布系统的前向后扫描方法提供了在DQN环境的一些迭代中提供准确的功率流。最后，拟议的多目标MADRL方法实现了VVO的双重目标。我们在不平衡的IEEE 13总线和123个总线系统上测试了该算法。数值模拟验证了该方法在电压调节和降低功率损耗方面的出色性能。
This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (MADRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in unbalanced distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions of the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the DQN agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DQN environment. Finally, the proposed multi-objective MADRL method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.
