Decentralized Federated Learning Architecture for Networked Microgrids

Ilyes Naidji, Chams Choucha, Mohamed Ramdani

2023

Abstract

The expansion of large-scale distributed renewable energy drives the emergence of networked microgrids systems, necessitating the development of an efficient energy management approach to minimize costs and maintain energy self-sufficiency. The use of smart systems that are based on deep learning algorithms has be-come prevalent while addressing the energy management problem due to its real-time scheduling capabilities. However, training deep-learning algorithms requires substantial energy operation data from these microgrids, which raises concerns regarding privacy and data security when collecting data from various microgrids. To address this challenging problem, this article proposes a decentralized federated learning architecture for networked microgrids. The architecture incorporates a distributed federated learning (FL) mechanism to guarantee data privacy and security and prevent the system from signle point of failure. A decentralized networked microgrids model is constructed, where each participating microgrid has an energy management system responsible for managing its energy. The goal of the EMS is to minimize economic costs and maintain energy self-sufficiency. Initially, MGs independently undergo self-training using local energy operation data to train their individual models. Subsequently, these local models are regularly exchanged, and their parameters are aggregated to create a global model. This approach allows sharing of experiences among the microgrids without transmitting energy operation data, thereby safeguarding privacy and ensuring data security and preventing from single point of failure.

Paper Citation

in Harvard Style

Naidji I., Choucha C. and Ramdani M. (2023). Decentralized Federated Learning Architecture for Networked Microgrids. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 291-294. DOI: 10.5220/0012215200003543

in Bibtex Style

@conference{icinco23,
author={Ilyes Naidji and Chams Choucha and Mohamed Ramdani},
title={Decentralized Federated Learning Architecture for Networked Microgrids},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={291-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012215200003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Decentralized Federated Learning Architecture for Networked Microgrids
SN - 978-989-758-670-5
AU - Naidji I.
AU - Choucha C.
AU - Ramdani M.
PY - 2023
SP - 291
EP - 294
DO - 10.5220/0012215200003543
PB - SciTePress