Game-Theoretic Learning for Power System Dynamic Ancillary Service Provisions

This letter studies the problem of coordinating aggregators in the power system to provide fast frequency response as dynamic ancillary services. We approach the problem from the perspective of suboptimal
${\mathcal {H}}_{\infty }$
control, and propose an efficient and tractable formulation. We further develop a distributed solution method for the investigated problem, which enables aggregator agents to learn their optimal provisions in an adaptive way. More precisely, we reformulate the original problem into a state-based potential game, where the agents interact with each other towards our designed Nash equilibrium. The proposed game-theoretic learning approach decouples the coupling Linear Matrix Inequality constraint, guarantees the convergence to the equilibrium which is close enough to the original optimum. The learning process is also robust to the changes in communication graphs. We demonstrate the efficacy of our proposed approach with a case study on a 3-aggregator system.