Robust Optimal Scheduling Method of Electric-Gas-Traffic Network Coupling System Based on Traffic Flow Uncertainty

The volatility of traffic flow in the operation of new energy vehicles creates an imbalance between energy supply and demand. By taking into account the temporal aspect of traffic network functioning and the cost associated with energy consumption in the electric-gas coupling network, the objective is to minimize the cost of operating the day ahead. The data-driven algorithm and risk coefficient method are combined to describe the situation. The fuzzy uncertainty set of traffic flow is generated, and a three-layer robust optimal scheduling model is constructed taking into account the uncertainty of traffic flow. The approach involves integrating the optimality condition (Karush-Kuhn-Tucker, KKT) with the column-and-constraints generation technique. The efficacy of the proposed framework and solution approach is ultimately confirmed by a numerical illustration, demonstrating that the suggested strategy may proficiently address the energy demand uncertainty associated with new energy vehicle and achieve positive interaction between the network and the higher network.