Adjustable Robust Nonlinear Network Design Without Controllable Elements under Load Scenario Uncertainties

TL;DR

This paper develops an exact method for designing robust nonlinear networks under load uncertainties, ensuring feasibility across all scenarios by identifying worst-case loads through polynomially many nonlinear optimizations.

Contribution

It introduces a finite-scenario approach for robust feasibility in nonlinear network design, applicable to various utility networks, and provides an exact algorithm for optimal robust design.

Findings

Method computes robust gas networks resilient to load fluctuations.

Finite number of worst-case scenarios suffices for robustness verification.

Algorithm applies to general potential-based flow networks.

Abstract

We study network design problems for nonlinear and nonconvex flow models without controllable elements under load scenario uncertainties, i.e., under uncertain injections and withdrawals. To this end, we apply the concept of adjustable robust optimization to compute a network design that admits a feasible transport for all, possibly infinitely many, load scenarios within a given uncertainty set. For solving the corresponding adjustable robust mixed-integer nonlinear optimization problem, we show that a given network design is robust feasible, i.e., it admits a feasible transport for all load scenario uncertainties, if and only if a finite number of worst-case load scenarios can be routed through the network. We compute these worst-case scenarios by solving polynomially many nonlinear optimization problems. Embedding this result for robust feasibility in an adversarial approach leads to an exact algorithm that computes an optimal robust network design in a finite number of iterations. Since all of the results are valid for general potential-based flows, the approach can be applied to different utility networks such as gas, hydrogen, or water networks. We finally demonstrate the applicability of the method by computing robust gas networks that are protected from future load fluctuations.