Optimization of computational budget for power system risk assessment
This work addresses the computational bottleneck in probabilistic risk-based security assessment for power systems, offering a domain-specific incremental improvement.
The paper tackles the problem of efficiently assessing risk in high voltage power transmission networks by combining machine learning with physical simulators, achieving improved computational tractability on the IEEE 118-bus test case.
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.