Vulnerabilities of Power System Operations to Load Forecasting Data Injection Attacks

We study the security threats of power system operations from a class of data injection attacks on load forecasting algorithms. In particular, we design an attack strategy on input features for load forecasting algorithms which can be implemented by an attacker with minimal system knowledge. System operators can be oblivious of such wrong load forecasts, which lead to uneconomical or even insecure decisions in commitment and dispatch. This paper brings up the security issues of load forecasting algorithms and shows that accurate load forecasting algorithm is not necessarily robust to malicious attacks. If power grid topology information is exploited, more severe attacks can be designed. We demonstrate the impact of load forecasting attacks on two IEEE test cases. We show our attack strategy is able to cause load shedding with high probability under various settings in the 14-bus test case, and also demonstrate system-wide threats in the 118-bus test case with limited local attacks.