On Robust Tie-line Scheduling in Multi-Area Power Systems

TL;DR

This paper introduces algorithms for robust tie-line scheduling in multi-area power systems using multi-parametric linear programming, ensuring finite convergence, privacy preservation, and optimality in deterministic and uncertain scenarios.

Contribution

It develops novel algorithms for robust tie-line scheduling that guarantee finite convergence and preserve system operators' privacy.

Findings

Algorithms converge to optimal schedules within finite steps.

Privacy of system operators' data is maintained during optimization.

Performance validated on multiple power system examples.

Abstract

The tie-line scheduling problem in a multi-area power system seeks to optimize tie-line power flows across areas that are independently operated by different system operators (SOs). In this paper, we leverage the theory of multi-parametric linear programming to propose algorithms for optimal tie-line scheduling within a deterministic and a robust optimization framework. Through a coordinator, the proposed algorithms are proved to converge to the optimal schedule within a finite number of iterations. A key feature of the proposed algorithms, besides their finite step convergence, is the privacy of the information exchanges; the SO in an area does not need to reveal its dispatch cost structure, network constraints, or the nature of the uncertainty set to the coordinator. The performance of the algorithms is evaluated using several power system examples.