  Due to the advances in computing and sensing, deep learning (DL) has widely
been applied in smart energy systems (SESs). These DL-based solutions have
proved their potentials in improving the effectiveness and adaptiveness of the
control systems. However, in recent years, increasing evidence shows that DL
techniques can be manipulated by adversarial attacks with carefully-crafted
perturbations. Adversarial attacks have been studied in computer vision and
natural language processing. However, there is very limited work focusing on
the adversarial attack deployment and mitigation in energy systems. In this
regard, to better prepare the SESs against potential adversarial attacks, we
propose an innovative adversarial attack model that can practically compromise
dynamical controls of energy system. We also optimize the deployment of the
proposed adversarial attack model by employing deep reinforcement learning (RL)
techniques. In this paper, we present our first-stage work in this direction.
In simulation section, we evaluate the performance of our proposed adversarial
attack model using standard IEEE 9-bus system.
