Unsupervised detection and open-set classification of fast-ramped flexibility activation events
This addresses a domain-specific challenge for distribution system operators in managing grid stability due to flexibility activations, representing an incremental improvement in monitoring techniques.
The paper tackles the problem of monitoring flexibility activations in power systems by proposing a data processing pipeline for real-time identification of fast-ramped events, enabling distribution system operators to detect market-driven activations early and verify DSO-requested ones to prevent critical grid situations, with feasibility evaluated on real data.
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.