Multi-Period Sparse Optimization for Proactive Grid Blackout Diagnosis
This work addresses the need for early warning diagnosis to enhance grid resilience against extreme events, representing an incremental improvement by integrating persistency constraints into existing optimization methods.
The paper tackles the problem of proactively identifying persistent vulnerabilities in power grids under increasing stress to prevent blackouts, achieving reliable tracking of vulnerability locations with an average solution time of 200 seconds per scenario on large systems with over 2000 buses.
Existing or planned power grids need to evaluate survivability under extreme events, like a number of peak load overloading conditions, which could possibly cause system collapses (i.e. blackouts). For realistic extreme events that are correlated or share similar patterns, it is reasonable to expect that the dominant vulnerability or failure sources behind them share the same locations but with different severity. Early warning diagnosis that proactively identifies the key vulnerabilities responsible for a number of system collapses of interest can significantly enhance resilience. This paper proposes a multi-period sparse optimization method, enabling the discovery of persistent failure sources across a sequence of collapsed systems with increasing system stress, such as rising demand or worsening contingencies. This work defines persistency and efficiently integrates persistency constraints to capture the ``hidden'' evolving vulnerabilities. Circuit-theory based power flow formulations and circuit-inspired optimization heuristics are used to facilitate the scalability of the method. Experiments on benchmark systems show that the method reliably tracks persistent vulnerability locations under increasing load stress, and solves with scalability to large systems (on average taking around 200 s per scenario on 2000+ bus systems).