Online routing for smart electricity network under hybrid uncertainty

In this study, an online routing problem for the optimal power flow (OPF) of smart grids subject to forecasting errors and cyber-attacks on data integrity is investigated. Energy data packages and flow-dispatch commands are transmitted over a communication network and corrupted by adversaries. In particular, we consider false data injection attacks that maliciously tamper with the data presented to grid operators. We develop an OPF model considering piecewise invariable load and power generation, which can be evaluated using a linear program. We extend the problem to an online setting, where data are sequentially observed, and adaptive strategies are required to optimize a metric, called the regret function, over time. We then incorporate the state-of-the-art adaptive change-point detection approach to develop an online routing algorithm that retains a sublinear regret in both the time horizon and number of change points. The applicability and effectiveness of the proposed algorithm were verified by numerical experiments on real-world smart grids.