Deep policy gradient for reactive power control in distribution systems

Qiuling Yang, Alireza Sadeghi, Gang Wang, Georgios B. Giannakis, Jian Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 languageEnglish (US)
Title of host publication2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161273
DOIs
StatePublished - Nov 11 2020
Externally publishedYes
Event2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020 - Tempe, United States
Duration: Nov 11 2020Nov 13 2020

Publication series

Name2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020

Conference

Conference2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
CountryUnited States
CityTempe
Period11/11/2011/13/20

Bibliographical note

Funding 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.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Deep neural network
  • Distribution systems
  • Policy gradient
  • Reactive power control

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