Supervised Learning for Optimal Power Flow as a Real-Time Proxy
This provides a fast approximation method for power system operators to predict short-term decision outcomes without costly simulations, though it is incremental as it applies existing learning techniques to a specific domain.
The paper tackled the problem of quickly calculating the cost of Alternating Current Optimal Power Flow (ACOPF) for power network planning by using supervised learning algorithms as a proxy, achieving less than 1% error on average with run-times several orders of magnitude lower than exact computation.
In this work we design and compare different supervised learning algorithms to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization of them. Our method enables fast approximate calculation of OPF cost with less than 1% error on average, achieved in run-times that are several orders of magnitude lower than of exact computation. Several test-cases such as IEEE-RTS96 are used to demonstrate the efficiency of our approach.