Optimizing gas networks using adjoint gradients

TL;DR

This paper introduces an efficient optimization framework for gas network operation that minimizes compression costs under dynamic, time-dependent conditions using adjoint gradients and optimal control methods.

Contribution

It develops a novel optimization scheme combining transient gas flow modeling, adjoint-based gradient computation, and advanced control algorithms for gas network management.

Findings

The framework effectively reduces compression costs in dynamic scenarios.

Constraint lumping improves computational efficiency without sacrificing accuracy.

Both interior-point and SQP methods successfully solve the nonlinear optimal control problems.

Abstract

An increasing amount of gas-fired power plants are currently being installed in modern power grids worldwide. This is due to their low cost and the inherent flexibility offered to the electrical network, particularly in the face of increasing renewable generation. However, the integration and operation of gas generators poses additional challenges to gas network operators, mainly because they can induce rapid changes in the demand. This paper presents an efficient minimization scheme of gas compression costs under dynamic conditions where deliveries to customers are described by time-dependent mass flows. The optimization scheme is comprised of a set of transient nonlinear partial differential equations that model the isothermal gas flow in pipes, an adjoint problem for efficient calculation of the objective gradients and constraint Jacobians, and state-of-the-art optimal control methods for solving nonlinear programs. As the evaluation of constraint Jacobians can become computationally costly as the number of constraints increases, efficient constraint lumping schemes are proposed and investigated with respect to accuracy and performance. The resulting optimal control problems are solved using both interior-point and sequential quadratic programming methods. The proposed optimization framework is validated through several benchmark cases of increasing complexity.