Microgrid Digital Twins: Concepts, Applications, and Future Trends

Najmeh Bazmohammadi; Ahmad Madary; Juan Carlos Vasquez Quintero; Hamid Bazmohammadi; Baseem Khan; Ying Wu; Josep M. Guerrero

doi:10.1109/ACCESS.2021.3138990

Microgrid Digital Twins: Concepts, Applications, and Future Trends

Najmeh Bazmohammadi, Ahmad Madary, Juan Carlos Vasquez Quintero, Hamid Bazmohammadi, Baseem Khan, Ying Wu, Josep M. Guerrero

Department of Mechanical and Production Engineering - Workshop, MPE

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

105 Citations (Scopus)

Abstract

Following the fourth industrial revolution, and with the recent advances in information and communication technologies, the digital twinning concept is attracting the attention of both academia and industry worldwide. A microgrid digital twin (MGDT) refers to the digital representation of a microgrid (MG), which mirrors the behavior of its physical counterpart by using high-fidelity models and simulation platforms as well as real-time bi-directional data exchange with the real twin. With the massive deployment of sensor networks and IoT technologies in MGs, a huge volume of data is continuously generated, which contains valuable information to enhance the performance of MGs. MGDTs provide a powerful tool to manage the huge historical data and real-time data stream in an efficient and secure manner and support MGs' operation by assisting in their design, operation management, and maintenance. In this paper, the concept of the digital twin (DT) and its key characteristics are introduced. Moreover, a workflow for establishing MGDTs is presented. The goal is to explore different applications of DTs in MGs, namely in design, control, operator training, forecasting, fault diagnosis, expansion planning, and policy-making. Besides, an up-to-date overview of studies that applied the DT concept to power systems and specifically MGs is provided. Considering the significance of situational awareness, security, and resilient operation for MGs, their potential enhancement in light of digital twinning is thoroughly analyzed and a conceptual model for resilient operation management of MGs is presented. Finally, future trends in MGDTs are discussed.

Original language	English
Journal	IEEE Access
Volume	10
Pages (from-to)	2284-2302
Number of pages	19
ISSN	2169-3536
DOIs	https://doi.org/10.1109/ACCESS.2021.3138990
Publication status	Published - 2022

Keywords

Artificial intelligence
Automatic learning
Big data
Decision support system
Digital twin
Industry 4.0
Microgrids
Industries
Adaptation models
Industry 4
Analytical models
BIG DATA
microgrids
TERM WIND-SPEED
SITUATION AWARENESS
big data
ENERGY MANAGEMENT
Computational modeling
RESILIENCE
digital twin
CHALLENGES
0
FAULT-DIAGNOSIS
decision support system
FRAMEWORK
SYSTEMS
Data models
GENERATION
automatic learning

Access to Document

10.1109/ACCESS.2021.3138990Licence: CC BY

https://ieeexplore.ieee.org/document/9663369/authors#authors

Cite this

@article{a7531c21748b4a2ebd0b2af6ec0d603f,

title = "Microgrid Digital Twins: Concepts, Applications, and Future Trends",

abstract = "Following the fourth industrial revolution, and with the recent advances in information and communication technologies, the digital twinning concept is attracting the attention of both academia and industry worldwide. A microgrid digital twin (MGDT) refers to the digital representation of a microgrid (MG), which mirrors the behavior of its physical counterpart by using high-fidelity models and simulation platforms as well as real-time bi-directional data exchange with the real twin. With the massive deployment of sensor networks and IoT technologies in MGs, a huge volume of data is continuously generated, which contains valuable information to enhance the performance of MGs. MGDTs provide a powerful tool to manage the huge historical data and real-time data stream in an efficient and secure manner and support MGs' operation by assisting in their design, operation management, and maintenance. In this paper, the concept of the digital twin (DT) and its key characteristics are introduced. Moreover, a workflow for establishing MGDTs is presented. The goal is to explore different applications of DTs in MGs, namely in design, control, operator training, forecasting, fault diagnosis, expansion planning, and policy-making. Besides, an up-to-date overview of studies that applied the DT concept to power systems and specifically MGs is provided. Considering the significance of situational awareness, security, and resilient operation for MGs, their potential enhancement in light of digital twinning is thoroughly analyzed and a conceptual model for resilient operation management of MGs is presented. Finally, future trends in MGDTs are discussed.",

keywords = "Artificial intelligence, Automatic learning, Big data, Decision support system, Digital twin, Industry 4.0, Microgrids, Industries, Adaptation models, Industry 4, Analytical models, BIG DATA, microgrids, TERM WIND-SPEED, SITUATION AWARENESS, big data, ENERGY MANAGEMENT, Computational modeling, RESILIENCE, digital twin, CHALLENGES, 0, FAULT-DIAGNOSIS, decision support system, FRAMEWORK, SYSTEMS, Data models, GENERATION, automatic learning",

author = "Najmeh Bazmohammadi and Ahmad Madary and Quintero, {Juan Carlos Vasquez} and Hamid Bazmohammadi and Baseem Khan and Ying Wu and Guerrero, {Josep M.}",

year = "2022",

doi = "10.1109/ACCESS.2021.3138990",

language = "English",

volume = "10",

pages = "2284--2302",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Microgrid Digital Twins: Concepts, Applications, and Future Trends

AU - Bazmohammadi, Najmeh

AU - Madary, Ahmad

AU - Quintero, Juan Carlos Vasquez

AU - Bazmohammadi, Hamid

AU - Khan, Baseem

AU - Wu, Ying

AU - Guerrero, Josep M.

PY - 2022

Y1 - 2022

N2 - Following the fourth industrial revolution, and with the recent advances in information and communication technologies, the digital twinning concept is attracting the attention of both academia and industry worldwide. A microgrid digital twin (MGDT) refers to the digital representation of a microgrid (MG), which mirrors the behavior of its physical counterpart by using high-fidelity models and simulation platforms as well as real-time bi-directional data exchange with the real twin. With the massive deployment of sensor networks and IoT technologies in MGs, a huge volume of data is continuously generated, which contains valuable information to enhance the performance of MGs. MGDTs provide a powerful tool to manage the huge historical data and real-time data stream in an efficient and secure manner and support MGs' operation by assisting in their design, operation management, and maintenance. In this paper, the concept of the digital twin (DT) and its key characteristics are introduced. Moreover, a workflow for establishing MGDTs is presented. The goal is to explore different applications of DTs in MGs, namely in design, control, operator training, forecasting, fault diagnosis, expansion planning, and policy-making. Besides, an up-to-date overview of studies that applied the DT concept to power systems and specifically MGs is provided. Considering the significance of situational awareness, security, and resilient operation for MGs, their potential enhancement in light of digital twinning is thoroughly analyzed and a conceptual model for resilient operation management of MGs is presented. Finally, future trends in MGDTs are discussed.

AB - Following the fourth industrial revolution, and with the recent advances in information and communication technologies, the digital twinning concept is attracting the attention of both academia and industry worldwide. A microgrid digital twin (MGDT) refers to the digital representation of a microgrid (MG), which mirrors the behavior of its physical counterpart by using high-fidelity models and simulation platforms as well as real-time bi-directional data exchange with the real twin. With the massive deployment of sensor networks and IoT technologies in MGs, a huge volume of data is continuously generated, which contains valuable information to enhance the performance of MGs. MGDTs provide a powerful tool to manage the huge historical data and real-time data stream in an efficient and secure manner and support MGs' operation by assisting in their design, operation management, and maintenance. In this paper, the concept of the digital twin (DT) and its key characteristics are introduced. Moreover, a workflow for establishing MGDTs is presented. The goal is to explore different applications of DTs in MGs, namely in design, control, operator training, forecasting, fault diagnosis, expansion planning, and policy-making. Besides, an up-to-date overview of studies that applied the DT concept to power systems and specifically MGs is provided. Considering the significance of situational awareness, security, and resilient operation for MGs, their potential enhancement in light of digital twinning is thoroughly analyzed and a conceptual model for resilient operation management of MGs is presented. Finally, future trends in MGDTs are discussed.

KW - Artificial intelligence

KW - Automatic learning

KW - Big data

KW - Decision support system

KW - Digital twin

KW - Industry 4.0

KW - Microgrids

KW - Industries

KW - Adaptation models

KW - Industry 4

KW - Analytical models

KW - BIG DATA

KW - microgrids

KW - TERM WIND-SPEED

KW - SITUATION AWARENESS

KW - big data

KW - ENERGY MANAGEMENT

KW - Computational modeling

KW - RESILIENCE

KW - digital twin

KW - CHALLENGES

KW - 0

KW - FAULT-DIAGNOSIS

KW - decision support system

KW - FRAMEWORK

KW - SYSTEMS

KW - Data models

KW - GENERATION

KW - automatic learning

U2 - 10.1109/ACCESS.2021.3138990

DO - 10.1109/ACCESS.2021.3138990

M3 - Journal article

SN - 2169-3536

VL - 10

SP - 2284

EP - 2302

JO - IEEE Access

JF - IEEE Access

ER -

Microgrid Digital Twins: Concepts, Applications, and Future Trends

Abstract

Keywords

Access to Document

Fingerprint

Cite this