Distributed learning for optimal allocation of synchronous and converter-based generation

TL;DR

This paper develops a distributed learning algorithm to optimally allocate synchronous and converter-based generators in power grids, ensuring stability and robustness despite uncertainties and perturbations.

Contribution

It introduces a novel log-linear learning approach for mixed power generation allocation, accounting for uncertainties and demonstrating convergence guarantees.

Findings

Guaranteed probabilistic convergence to optimal configurations

Robustness against susceptance drops and power deviations

Successful simulation validation with six-generation-unit network

Abstract

Motivated by the penetration of converter-based generation into the electrical grid, we revisit the classical log-linear learning algorithm for optimal allocation {of synchronous machines and converters} for mixed power generation. The objective is to assign to each generator unit a type (either synchronous machine or DC/AC converter in closed-loop with droop control), while minimizing the steady state angle deviation relative to an optimum induced by unknown optimal configuration of synchronous and DC/AC converter-based generation. Additionally, we study the robustness of the learning algorithm against a uniform drop in the line susceptances and with respect to a well-defined feasibility region describing admissible power deviations. We show guaranteed probabilistic convergence to maximizers of the perturbed potential function with feasible power flows and demonstrate our theoretical findings via simulative examples of power network with six generation units.