A Statistical Learning Approach to Reactive Power Control in Distribution Systems
This work addresses the problem of efficient reactive power control for distribution systems with renewable energy variability, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackles the challenge of real-time optimal reactive power control in distribution systems by using a deep neural network to learn the input-output relationship from historical and simulated data, achieving computational efficiency and robustness with numerical tests on a 47-bus network using real data.
Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the `optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits.