Distributionally Robust Scheduling of Integrated Gas-Electricity Systems With Demand Response

Abstract

This paper proposes a distributionally robust scheduling model for the integrated gas-electricity system (IGES) with electricity and gas load uncertainties, and further studies the impact of integrated gas-electricity demand response (DR) on energy market clearing, as well as locational marginal electricity and gas prices (LMEPs and LMGPs). The proposed model maximizes the base-case system social welfare (i.e., revenue from price-sensitive DR loads minus energy production cost) minus the worst-case expected load shedding cost. Price-based gas-electricity DRs are formulated via price-sensitive demand bidding curves while considering DR participation levels and energy curtailment limits. By linearizing nonlinear Weymouth gas flow equations via Taylor series expansion and further approximating recourse decisions as affine functions of uncertainty parameters, the formulation is cast into a mixed-integer linear programming problem to enhance computational tractability. Case studies illustrate effectiveness of the proposed model for ensuring system security against uncertainties, avoiding potential transmission congestions, and increasing financial stability of DR providers.