This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states from its neighboring agents. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Experimental results in two microgrid setups show that the developed PowerNet outperforms a conventional model-based control, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

中文翻译：

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PowerNet：用于可扩展Powergrid控制的多智能体深度强化学习

本文开发了一种有效的多智能体深度强化学习算法，用于电网中的协同控制。具体来说，我们考虑分布式发电机（DGs）中基于分散逆变器的二次电压控制问题，该问题首先被公式化为协作式多智能体强化学习（MARL）问题。然后，我们提出一种新颖的基于策略的MARL算法PowerNet，其中每个代理（DG）都基于（子）全局奖励但从其相邻代理获取本地状态来学习控制策略。由于来自一个代理的本地控制对与其远离的代理的影响有限，因此，我们利用一种新颖的空间折扣因子来减少来自远程代理的影响，以加快训练过程并提高可伸缩性。此外，基于学习的通信协议用于促进相邻代理之间的协作。另外，为了减轻在策略学习过程中引入的系统不确定性和随机噪声的影响，我们利用动作平滑因子来稳定策略执行。为了促进培训和评估，我们开发了PGSim，这是一种高效，高保真的Powergrid仿真平台。在两个微电网设置中的实验结果表明，所开发的PowerNet优于传统的基于模型的控制以及几种最新的MARL算法。分散式学习方案和高采样效率也使其适用于大型电网。我们利用行动平滑因素来稳定政策执行。为了促进培训和评估，我们开发了PGSim，这是一种高效，高保真的Powergrid仿真平台。在两个微电网设置中的实验结果表明，所开发的PowerNet优于传统的基于模型的控制以及几种最新的MARL算法。分散式学习方案和高采样效率也使其适用于大型电网。我们利用行动平滑因素来稳定政策执行。为了促进培训和评估，我们开发了PGSim，这是一种高效，高保真的Powergrid仿真平台。在两个微电网设置中的实验结果表明，所开发的PowerNet优于传统的基于模型的控制以及几种最新的MARL算法。分散式学习方案和高采样效率也使其适用于大型电网。