Robust Corrective Control Measures in Power Systems with Dynamic Line Rating

TL;DR

This paper introduces two robust optimization approaches for corrective control in power systems with Dynamic Thermal Line Rating, enhancing operational flexibility and cost efficiency under forecast uncertainties.

Contribution

It presents novel robust optimization methods for corrective control in power systems with dynamic line ratings, including a decentralized affine policy approach.

Findings

Both approaches effectively reduce operational costs.

The affine policy enables decentralized control.

Robust methods improve reliability under forecast errors.

Abstract

Dynamic Thermal Line Rating (DLR) is deemed to be an effective way to increase transmission capacities and therefore enabling additional operational flexibility. The transmission capacities are dynamically determined based on current or expected weather conditions. First pilot projects have proven its efficiency. In this paper we present two approaches to determine the location and amount of ramping capabilities for corrective control measures in the case of errors in the forecast of the line rating. Both approaches result in robust optimization problems, where the first approach guarantees that there is a suitable remedial action for every realization of uncertainty in a given uncertainty set. The corrective control action is calculated once the forecast error is known. The second approach relies on affine policies which directly relate the current line rating to corrective control measures, if needed, which enables a decentralized operation. In case studies, the two approaches are compared and the reduction of overall operational costs is demonstrated exemplarily for different parameters.