Assessment of spatiotemporally coordinated cyberattacks on renewable energy forecasting in smart energy system

The employment of deep learning (DL) in renewable energy forecasting (REF) may bring novel cyberthreats. However, this fact has not drawn sufficient attention in the existing literature. To fill the gap, this article, among the first, investigates the latent cyberthreat incurred by the DL-based REF model. First, a spatiotemporally coordinated cyberattack strategy is proposed by considering attackable geo-distributed renewable energy resources (RESs) and periods. It is fulfilled by compromising meteorological data transmitted from external online weather forecast interfaces which are required for REF but also exposed to potential adversaries. In this manner, the malicious adversary is able to deteriorate the REF performance, thereby jeopardizing the operation of the smart energy system. Second, the cyberattack optimization models under the white-box and black-box scenarios are designed. In the white-box scenario, the adversary has full knowledge of these operator-own REF models to obtain false data that should be injected into the meteorological data for launching cyberattacks; in the black-box scenario, the adversary will train surrogate models to replace these operator-own models for attack guidance. Third, an iterative solving method is proposed to solve the proposed white-box and black-box cyberattack optimization models. As DL models (sealed, non-differentiable, and highly non-linear) are introduced to the optimization problem, conventional optimization solvers are unable to handle the cyberattack optimization problem. The proposed solving method utilizes the proximal gradient descent principle, and is feasible to explore the near-optimal solution to the problem. At last, the efficacy of the proposed cyberattack strategy under different RES penetration levels and attack strengths and its catastrophic impacts are comprehensively assessed on the IEEE 39-bus benchmark. The results reveal that the cyberattack would cause enormous economic losses and even collapse the smart energy system.