Temperature Overloads in Power Grids Under Uncertainty: a Large Deviations Approach

TL;DR

This paper applies large deviations techniques to analyze overload probabilities in power grids with uncertain renewable energy inputs, proposing less conservative capacity regions based on temperature constraints.

Contribution

It introduces a large deviations framework for assessing overload risks in stochastic power grids and demonstrates capacity improvements using temperature-based constraints.

Findings

Temperature constraints lead to less conservative capacity estimates.

Large deviations provide a computationally feasible way to evaluate overload risks.

Stochastic constraints can improve grid capacity management.

Abstract

The advent of renewable energy has huge implications for the design and control of power grids. Due to increasing supply-side uncertainty, traditional reliability constraints such as strict bounds on current, voltage and temperature in a transmission line have to be replaced by computationally demanding chance constraints. In this paper we use large deviations techniques to study the probability of current and temperature overloads in power grids with stochastic power injections, and develop corresponding safe capacity regions. In particular, we characterize the set of admissible power injections such that the probability of overloading of any line over a given time interval stays below a fixed target. We show how enforcing (stochastic) constraints on temperature, rather than on current, results in a less conservative approach and can thus lead to capacity gains.