Unsupervised detection and open-set classification of fast-ramped flexibility activation events

TL;DR

This paper introduces an unsupervised and open-set classification pipeline for real-time detection of fast-ramped flexibility activation events in power systems, aiding DSOs in early identification and verification of critical grid activities.

Contribution

It presents a novel data processing pipeline combining unsupervised detection and open-set classification for real-time identification of flexibility events in power grids.

Findings

Feasibility demonstrated on real load and flexibility data.

Early detection of market-driven flexibility activations.

Verification of DSO-requested flexibility activations.

Abstract

The continuous electrification of the mobility and heating sectors adds much-needed flexibility to the power system. However, flexibility utilization also introduces new challenges to distribution system operators (DSOs), who need mechanisms to supervise flexibility activations and monitor their effect on distribution network operation. Flexibility activations can be broadly categorized to those originating from electricity markets and those initiated by the DSO to avoid constraint violations. Simultaneous electricity market driven flexibility activations may cause voltage quality or temporary overloading issues, and the failure of flexibility activations initiated by the DSO might leave critical grid states unresolved. This work proposes a novel data processing pipeline for automated real-time identification of fast-ramped flexibility activation events. Its practical value is twofold: i) potentially critical flexibility activations originating from electricity markets can be detected by the DSO at an early stage, and ii) successful activation of DSO-requested flexibility can be verified by the operator. In both cases the increased awareness would allow the DSO to take counteractions to avoid potentially critical grid situations. The proposed pipeline combines techniques from unsupervised detection and open-set classification. For both building blocks feasibility is systematically evaluated and proofed on real load and flexibility activation data.