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Due to the advances in computing and sensing, deep learning (DL) has widelybeen applied in smart energy systems (SESs). These DL-based solutions haveproved their potentials in improving the effectiveness and adaptiveness of thecontrol systems. However, in recent years, increasing evidence shows that DLtechniques can be manipulated by adversarial attacks with carefully-craftedperturbations. Adversarial attacks have been studied in computer vision andnatural language processing. However, there is very limited work focusing onthe adversarial attack deployment and mitigation in energy systems. In thisregard, to better prepare the SESs against potential adversarial attacks, wepropose an innovative adversarial attack model that can practically compromisedynamical controls of energy system. We also optimize the deployment of theproposed 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 adversarialattack model using standard IEEE 9-bus system.

A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems