IoT-Based Digital Twin for Energy Cyber-Physical Systems: Design and Implementation

• Published in: Energies (Basel) Vol. 13; no. 18; p. 4762
• Main Authors: Saad, Ahmed, Faddel, Samy, Mohammed, Osama
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2020
• Subjects: Automation; Communication; Communications networks; Controllers; Cyber-physical systems; Decision making; digital twin; Digital twins; Distributed generation; distributed resources; Electricity distribution; Energy industry; Energy management; Energy resources; Energy sources; industrial internet of things; Internet of Things; Internet Protocol; Power plants; Security; Sensors; Web services
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13184762

With the emergence of distributed energy resources (DERs), with their associated communication and control complexities, there is a need for an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system. The digital twin (DT) is a new concept that can unleash tremendous opportunities and can be used at the different control and security levels of power systems. This paper provides a methodology for the modelling of the implementation of energy cyber-physical systems (ECPSs) that can be used for multiple applications. Two DT types are introduced to cover the high-bandwidth and the low-bandwidth applications that need centric oversight decision making. The concept of the digital twin is validated and tested using Amazon Web Services (AWS) as a cloud host that can incorporate physical and data models as well as being able to receive live measurements from the different actual power and control entities. The experimental results demonstrate the feasibility of the real-time implementation of the DT for the ECPS based on internet of things (IoT) and cloud computing technologies. The normalized mean-square error for the low-bandwidth DT case was 3.7%. In the case of a high-bandwidth DT, the proposed method showed superior performance in reconstructing the voltage estimates, with 98.2% accuracy from only the controllers’ states.