With the increasing penetration of renewable energy, power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and accurate control actions derived in real time are vital to ensure system security and economics. To this end, solving alternating current (AC) optimal power flow (OPF) with operational constraints remains an important yet challenging optimization problem for secure and economic operation of the power grid. This paper adopts a novel method to derive fast OPF solutions using state-of-the-art deep reinforcement learning (DRL) algorithm, which can greatly assist power grid operators in making rapid and effective decisions. The presented method adopts imitation learning to generate initial weights for the neural network (NN), and a proximal policy optimization algorithm to train and test stable and robust artificial intelligence (AI) agents. Training and testing procedures are conducted on the IEEE 14-bus and the Illinois 200-bus systems. The results show the effectiveness of the method with significant potential for assisting power grid operators in real-time operations.

中文翻译：

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通过深度强化学习的快速交流最优潮流解决方案的数据驱动方法

随着可再生能源的普及，电网运营商每天都在观察功率和电压曲线的快速波动和大幅度波动。实时，快速，准确的控制动作对于确保系统安全性和经济性至关重要。为此，对于电网的安全和经济运行而言，解决具有运行约束的交流电（AC）最佳功率流（OPF）仍然是一个重要而又极具挑战性的优化问题。本文采用一种新颖的方法，利用最新的深度强化学习（DRL）算法来推导快速OPF解决方案，该算法可以极大地帮助电网运营商做出快速有效的决策。该方法采用模仿学习为神经网络（NN）生成初始权重，以及用于训练和测试稳定可靠的人工智能（AI）代理的近端策略优化算法。培训和测试过程在IEEE 14总线和伊利诺伊州200总线系统上进行。结果表明该方法的有效性，对协助电网运营商进行实时运营具有巨大的潜力。