Forecast based consensus control for DC microgrids using distributed long short-term memory deep learning models

Amir Alavi, S, Mehran, K, Vahidinasab, V ORCID: https://orcid.org/0000-0002-0779-8727 and Catalão, JPS, 2021. Forecast based consensus control for DC microgrids using distributed long short-term memory deep learning models. IEEE Transactions on Smart Grid. ISSN 1949-3053

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Abstract

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Smart Grid
Creators:	Amir Alavi, S., Mehran, K., Vahidinasab, V. and Catalão, J.P.S.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	5 April 2021
ISSN:	1949-3053
Identifiers:	Number Type 10.1109/tsg.2021.3070959 DOI 1431271 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	21 Apr 2021 11:29
Last Modified:	31 May 2021 15:03
URI:	https://irep.ntu.ac.uk/id/eprint/42737

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