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.

通过设计和比较不同的监督学习算法来计算 ACOPF 的成本，我们提出了一种快速计算 OPF 成本的方法，能够在多重时间协调框架中预测短期决策结果，从而避免了实际模拟和优化的需要，并且在保证均值误差小于 1% 的情况下，运行时间比精确计算低几个数量级。