论文标题
完全分散的加强学习基于分配网格中光伏的控制，以共同提供真实和反应能力
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power
论文作者
论文摘要
在本文中，我们引入了一个新框架，以解决具有深度光伏穿透性不平衡分布网格中电压调节的问题。在此框架中，在每个太阳能电池板智能逆变器上都明确控制了真实和反应性设定点，目标是同时最大程度地减少系统范围的电压偏差并最大化太阳能输出。我们将问题作为Markov决策过程，具有连续的动作空间，并使用近端策略优化（一种基于强化的学习方法）来解决它，而无需任何对网络拓扑或线路参数的预测或明确知识。通过以准稳态的方式代表系统，并通过仔细制定马尔可夫决策过程，我们降低了问题的复杂性，并允许完全分散（无通信的）政策，所有这些政策使训练有素的政策更加实用和可解释。基于美国中西部的真实网络的240节点不平衡分布网格上的数值模拟用于验证提出的框架和强化学习方法。
In this paper, we introduce a new framework to address the problem of voltage regulation in unbalanced distribution grids with deep photovoltaic penetration. In this framework, both real and reactive power setpoints are explicitly controlled at each solar panel smart inverter, and the objective is to simultaneously minimize system-wide voltage deviation and maximize solar power output. We formulate the problem as a Markov decision process with continuous action spaces and use proximal policy optimization, a reinforcement learning-based approach, to solve it, without the need for any forecast or explicit knowledge of network topology or line parameters. By representing the system in a quasi-steady state manner, and by carefully formulating the Markov decision process, we reduce the complexity of the problem and allow for fully decentralized (communication-free) policies, all of which make the trained policies much more practical and interpretable. Numerical simulations on a 240-node unbalanced distribution grid, based on a real network in Midwest U.S., are used to validate the proposed framework and reinforcement learning approach.
