A two-stage decision-dependent stochastic strategy for de-icing maintenance of ice-covered transmission lines

Heavy ice accretions covering transmission lines potentially cause major damage to transmission infrastructure. The timely de-icing maintenance for ice-covered transmission lines (ITL) is paramount in mitigating damages caused by ice storms. Aiming to determine the optimal maintenance sequence and operation strategies, this study proposes a two-stage stochastic optimization method that incorporates decision-dependent uncertainty (DDU). First, to capture the inherent dependency of potential fault scenario probabilities on de-icing decisions, the DDU is incorporated into the stochastic optimization model. An equivalent transformation method is adopted to convert the nonlinear scenario-decision relationship into a mixed-integer linear programming (MILP) model. Second, a two-stage optimization model with DDU is established, where the first stage coordinates the de-icing maintenance sequence and unit commitment, and the second stage refines the operation strategy based on specific scenarios. A Markov chain is constructed to represent the probability transition matrix for the states of iced transmission lines, with a backward reduction method applied to manage the scenario dimensionality and prevent explosion. Case studies on the modified IEEE 30-bus system validate the effectiveness and robustness of the proposed approach.