A Data-Driven Robust Optimization Scheduling of Coupled Electricity-Gas-Transportation Systems Considering Multiple Uncertainties

The deep coupling and favorable interaction among different energy flow networks such as electricity, natural gas and transportation has posed a great challenge to the coordinated optimization operation of diversified integrated energy system. In view of the multiple uncertainties in the coupled electricity-gas-transportation system, a data-driven robust coordination optimization scheduling model was proposed, which took into account the uncertainties of traffic flow, wind power and gas consumption by gas-fired units comprehensively. Firstly, the optimal flow distribution in transportation network was achieved by Wardrop user equilibrium principle, and the uncertainties from the transportation and gas networks were transformed into the distribution network for unified consideration according to the coupling constraints between networks. Secondly, an ambiguity set of high-dimensional source and load uncertainties was constructed to describe the probability distribution characteristics based on the historical data of wind power and traffic flow. Then, aiming at minimizing the day-ahead operation cost in the base prediction case and real-time adjustment cost in the worst-case distribution of uncertainties, a data-driven two-stage robust economic dispatch model was established, which was solved by column-and- constraint generation method in the master-subproblem cooperative solution framework. Finally, the simulation results on the test system demonstrated the effectiveness of the proposed model and solution approach. © 2021 Chin. Soc. for Elec. Eng.