On Vulnerability of Renewable Energy Forecasting: Adversarial Learning Attacks

Developing the deep learning (DL) technique is a promising way to improve renewable energy forecasting accuracy and offset the negative impacts of renewable energy on the power system. However, the application of the DL technique brings novel cyberthreats to the renewable energy forecast, and its cybersecurity has not received enough attention in previous literatures. To fill the gap, the vulnerability of renewable energy forecasting is, among the first, studied in-depth in this article. First, a novel cyberattack named adversarial learning attack (ALA) is proposed. The ALA is achieved by tampering with the meteorological data obtained by online weather forecasts from external application programming interfaces to undermine the renewable energy forecasting performance, which jeopardizes the power system operation. Then, an iterative algorithm is proposed to solve the ALA-based optimization problem. As the DL model is involved as optimization constraints, the optimization problem is nonconvex and NP-hard, which is unable to be solved by traditional approaches. The proposed algorithm utilizes the proximal gradient descent principle and is effective in iteratively exploring the near-optimal solution. At last, the impact of the ALA strategy on the power system operation is assessed, which considers the economic loss incurred and the potential hazards. The feasibility and efficacy of the ALA strategy are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus benchmarks. The simulation results reveal that the ALA is able to impose severe economic losses on the operation and even induces disastrous hazards, such as power system collapse.