Abstract
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.
Highlights
Future power distribution systems consist of multiple entities that interact with each other in real time
The energy cyber-physical systems (ECPSs) digital twin (DT) is demonstrated by implementing the physical andsharing, the cyber layers of the high-speed microgrids connectivity among the networked microgrids, communication configuration is networked (MG)
This paper provides a methodology for the design of digital twin models for the power system
Summary
Future power distribution systems consist of multiple entities that interact with each other in real time. Turning the current power grid into an IoT technology-dependent one means that a large number of data are harvested from the physical assets’ sensors and the cyber assets’ controllers. This will greatly affect our current understanding of the energy sector [6,7,8,9,10,11,12]. With the incoming stream of data and operational real-time requirements as well as the potential cyber-attacks on the communication network, there is a need for a conceptual framework that can monitor, collect, harness and interact with the physical components to ensure their optimal operation.
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