Risk-Based Distributionally Robust Optimal Gas-Power Flow With Wasserstein Distance

Gas-fired units and power-to-gas facilities provide pivotal backups for power systems with volatile renewable generations. The deepened system interdependence calls for elaborate consideration of network models of both natural gas and power systems, as well as uncertain factors. This paper proposes a data-driven distributionally robust optimization model for the optimal gas-power flow problem with uncertain wind generation. The concept of zonal line pack and line pack reserve are raised to topologically distinguish fuel suppliers of gas-fired units and ensure gas system operating security during reserve deployment. Wind power uncertainty is described by an ambiguity set, i.e., a family of candidate distributions around an empirical distribution in the sense of Wasserstein distance. A convex optimization-based solution procedure is developed, which entails solving only second-order cone programs. Computational results validate the effectiveness of the proposed models and methods.