Predicting Solutions to the Optimal Power Flow Problem
This paper discusses an implementation of gradient boosting regression to predict the output of the optimal power flow problem for transmission networks to reduce the computation time. The optimal power flow problem is an important optimization to minimize the cost of operating a transmission network. The inputs are the load demanded at each node and the cost of generation. The outputs are the power and voltage of each generator in the network. We used the IEEE 30 bus network as our static network model, and load data from PJM. The ground truth solutions were solved using MATPOWER. Gradient boosting regression provided the highest accuracy based on the mean squared difference between each output to the true optimal solution. Over 90% of predictions were within 5% of the true solution. However, approximately 60% of predictions violated one of the network constraints. This is avoided by using the predicted solution as a starting point to the optimal power flow problem. A case study shows that with highly variable load due to renewable penetration, the computation time of running the optimal power flow this way is reduced by up to 30%