Distributionally Robust Frequency Constrained Scheduling for an Integrated Electricity-Gas System

Abstract

Power systems are shifted from conventional bulk generation toward renewable generation. This trend leads to the frequency security problem due to the decline of system inertia. On the other hand, natural gas-fired units are frequently scheduled to provide operational flexibility due to their fast adjustment ability. The interdependence between the power system and natural gas system is thus intensified. In this context, this paper considers the frequency constrained scheduling problem from the perspective of an integrated electricity-gas system under wind power uncertainty. A distributionally robust (DR) joint chance-constrained optimization model is proposed to co-optimize the unit commitment and virtual inertia provision from wind farm systems. This model incorporates both frequency constraints and natural gas system (NGS) operational constraints and addresses the wind power uncertainty by designing DR joint chance constraints. It is shown that this model admits a mixed-integer second-order cone programming. Case studies demonstrate that the proposed method can provide a highly reliable and computationally efficient solution and show the importance of incorporating NGS operational constraints in the frequency constrained scheduling problem.