Digital twin simulation for integration of blockchain and internet of things for optimal smart management of PV-based connected microgrids

Li, Yihong; Qi, Tao; Gong, Yadong

doi:10.1016/j.solener.2023.01.013

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…The work in [6] on the other hand, creates a digital twin for the integration of blockchain into the field of photovoltaic-connected microgrids. Their work models the power flow and the blockchain using their detailed mathematical models and evaluate the performance of their digital twin in terms of required computing power, energy cost, and grid voltage deviation.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

A Real-Time Cosimulation Testbed for Electric Vehicle Charging and Smart Grid Security

Sarieddine

Sayed

Jafarigiv

et al. 2023

IEEE Secur. Privacy

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Section: Related Work and Research Gapsmentioning

confidence: 99%

A Real-Time Cosimulation Testbed for Electric Vehicle Charging and Smart Grid Security

Sarieddine

Sayed

Jafarigiv

et al. 2023

IEEE Secur. Privacy

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“…AI techniques can contribute to EDT developments in multiple ways, such as optimization for the reduction of intractable search-spaces, generating models for complex systems, and time series forecasting for modeling extreme events [145]. In order for AI and ML algorithms to be applied in this field, they must achieve a level of maturity based on a concrete understanding of what they can contribute to the energy industry.…”

Section: Digital Twins Coupled With Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Yousef,

Rocha-Meneses

2023

Energies

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This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

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A Framework for Enabling Manufacturing Flexibility and Optimizing Industrial Demand Response Services

Wan,

Ranaboldo,

Burgio

et al. 2023

IFIP Advances in Information and Communication Technology

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Digital twin simulation for integration of blockchain and internet of things for optimal smart management of PV-based connected microgrids

Cited by 11 publications

References 26 publications

A Real-Time Cosimulation Testbed for Electric Vehicle Charging and Smart Grid Security

A Real-Time Cosimulation Testbed for Electric Vehicle Charging and Smart Grid Security

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

A Framework for Enabling Manufacturing Flexibility and Optimizing Industrial Demand Response Services

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