Optimization of computational budget for power system risk assessment

Benjamin Donnot (1, 2, 3) , Isabelle Guyon (1, 2, 4) , Antoine Marot (3) , Marc Schoenauer (2, 1) , Patrick Panciatici (3)

1 TAU - TAckling the Underspecified
2 LRI - Laboratoire de Recherche en Informatique
3 RTE - Réseau de Transport d'Electricité [Paris]
4 UP11 - Université Paris-Sud - Paris 11

We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a probabilistic risk-based security criterion. However, these approaches suffer from high requirements in terms of tractability. Here, we propose a new method to assess the risk. This method uses both machine learning techniques (artificial neural networks) and more standard simulators based on physical laws. More specifically we train neural networks to estimate the overall dangerousness of a grid state. A classical benchmark problem (manpower 118 buses test case) is used to show the strengths of the proposed method.

Domains: Electric power; Other Statistics [stat.ML]

Benjamin Donnot, Isabelle Guyon, Antoine Marot, Marc Schoenauer, Patrick Panciatici. Optimization of computational budget for power system risk assessment. 2018. ⟨hal-01783685⟩