Composition and Application of Power System Digital Twins Based on Ontological Modeling

Andryushkevich, Sergey K.; Kovalyov, Serge P.; Nefedov, Evgeny

doi:10.1109/indin41052.2019.8972267

Cited by 41 publications

(11 citation statements)

References 15 publications

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“…The data includes the historical data obtained from the system under various operating conditions. The complete model of a system is achieved by integrating models of all subsystems and their interactions [36].…”

Section: A Modeling Of Physical Systems and Processesmentioning

confidence: 99%

Microgrid Digital Twins: Concepts, Applications, and Future Trends

et al. 2022

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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.

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Section: A Modeling Of Physical Systems and Processesmentioning

confidence: 99%

Microgrid Digital Twins: Concepts, Applications, and Future Trends

et al. 2022

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“…Further, the work in [174] discusses the application of DT for energy benchmarking to achieve optimal energy decision making with energy retrofitting and real-time energy management systems. Similarly, in [175,176], the DT-based multilayered approach is adopted for developing an energy model to achieve efficient energy consumption in a power system network. From all the literature discussed above, the major aspects of DTs in the modeling, control, and monitoring of the grid-connected PV system components is identified with reference to AI techniques as shown in Figure 14.…”

Section: Application Of Ai For Reliabilitymentioning

confidence: 99%

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

et al. 2021

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The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.

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“…Such a dynamic virtual model mirrors in real time a complex physical system in production from a certain perspective, for example, electric power network online analysis, and has built-in intelligence to address the associated concerns, for example, power grid security assessment. However, various problems associated with the development, updating, and application of digital twins in the energy sector have not been solved yet and turn to be the subjects of intensive research [5]. The technologies, such as Generative Design [6] that allow one to automatically find the optimal design solutions for power supply [7], are developed very slowly.…”

Section: Digital Twin Concept For Power Grid Through Reinforcement Lementioning

confidence: 99%

“…Some studies show [5] that reinforcement learning addresses the focal issue of improving digital twins through learning. The advantage of this method is that the created virtual environment can go through an infinite number of repetitions and scenarios in order to train agents remembering all the situations that have arisen and the ways out of them that gave the maximum reward.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Development of Digital Twin for Load Center on the Example of Distribution Network of an Urban District

et al. 2020

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The paper proposes a concept of building a digital twin based on the reinforcement learning method. This concept allows implementing an accurate digital model of an electrical network with bidirectional automatic data exchange, used for modeling, optimization, and control. The core of such a model is an agent (potential digital twin). The agent, while constantly interacting with a physical object (electrical grid), searches for an optimal strategy for active network management, which involves short-term strategies capable of controlling the power supplied by generators and/ or consumed by the load to avoid overload or voltage problems. Such an agent can verify its training with the initial default policy, which can be considered as a teacher’s advice. The effectiveness of this approach is demonstrated on a test 77-node scheme and a real 17-node network diagram of the Akademgorodok microdistrict (Irkutsk) according to the data from smart electricity meters.

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Composition and Application of Power System Digital Twins Based on Ontological Modeling

Cited by 41 publications

References 15 publications

Microgrid Digital Twins: Concepts, Applications, and Future Trends

Microgrid Digital Twins: Concepts, Applications, and Future Trends

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

Development of Digital Twin for Load Center on the Example of Distribution Network of an Urban District

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