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. 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 (a.k.a., policy) is parameterized in the present work by a deep neural network, whose unknown weights are updated by minimizing the accumulated power loss over a number of historical and simulated training pairs, using the policy gradient method. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control decision with only several matrix-vector multiplications. The merits of this novel deep policy gradient approach include its computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real solar and consumption data corroborate these practical merits.
|Original language||English (US)|
|Title of host publication||2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Nov 11 2020|
|Event||2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020 - Tempe, United States|
Duration: Nov 11 2020 → Nov 13 2020
|Name||2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020|
|Conference||2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020|
|Period||11/11/20 → 11/13/20|
Bibliographical noteFunding Information:
The work of Q. Yang, G. Wang, and J. Sun was supported in part by the National Natural Science Foundation of China under Grants 61522303, 61720106011, 61621063, and U1613225. Q. Yang was also supported by the China Scholarship Council. The work of A. Sadeghi and G. B. Giannakis was supported in part by the National Science Foundation under Grants 1711471 and 1901134.
© 2020 IEEE.
- Deep neural network
- Distribution systems
- Policy gradient
- Reactive power control