A Reinforcement Learning Approach for Frequency Control of Inverted-Based Microgrids

A Reinforcement Learning Approach for Frequency Control of Inverted-Based Microgrids

Adibi, M. (TU Delft Mathematical Physics)
van der Woude, J.W. (TU Delft Mathematical Physics)

2019

In this paper, we present a reinforcement learning control scheme for optimal frequency synchronization in a lossy inverter-based microgrid. Compared to the existing methods in the literature, we relax the restrictions on the system, i.e. being a lossless microgrid, and the transmission lines and loads to have constant impedances. The proposed control scheme does not require a priori information about system parameters and can achieve frequency synchronization in the presence of dominantly resistive and/or inductive line and load impedances, model parameter uncertainties, time varying loads and disturbances. First, using Lyapunov theory a feedback control is formulated based on the unknown dynamics of the microgrid. Next, a performance function is defined based on cumulative rewards towards achieving convergence to the nominal frequency. The performance function is approximated by a critic neural network in real-time. An actor network is then simultaneously learning a parameterized approximation of the nonlinear dynamics and optimizing the approximated performance function obtained from the critic network. The performance of our control scheme is validated via simulation on a lossy microgrid case study in the presence of disturbances.

frequency synchronization
microgrids
reinforcement learning
stability

http://resolver.tudelft.nl/uuid:cb0b0207-8df5-40fc-ab99-9fe5939f780a

https://doi.org/10.1016/j.ifacol.2019.08.164

1474-6670

IFAC-PapersOnLine, 52 (4), 111-116

IFAC Workshop on Control of Smart Grid and Renewable Energy Systems, CSGRES 2019, 2019-06-10 → 2019-06-12, Jeju, Korea, Republic of

Institutional Repository

journal article

© 2019 M. Adibi, J.W. van der Woude