August 15, 2025Open Access

AI-Powered Digital Twin Co-Simulation Framework for Climate-Adaptive Renewable Energy Grids

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Authors

KAKwabena AddoDurban University of Technology MKMusasa KabeyaDurban University of Technology EOEvans Eshiemogie OjoDurban University of Technology

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Implication

This framework demonstrates improved energy efficiency and voltage regulation in renewable energy grids, indicating the potential of AI and digital twins for climate adaptation.

Key Points

MAIN FINDING: The AI-integrated digital twin framework enhances the stability and resilience of renewable energy grids under climate stress.
KEY EVIDENCE: Simulation results show a 14.6% reduction in energy loss and a 19.5% improvement in recovery time over baseline controls.
APPROACH: The framework uses deep reinforcement learning and real-time coordination in a high fidelity co-simulation environment with physical grid dynamics and climate data.
SIGNIFICANCE: This advancement highlights the role of AI in optimizing renewable energy systems for future climate challenges.

Cite This Study

Addo et al. (2025) studied this question.

synapsesocial.com/papers/689fc6912abb084d53ed28c7 https://doi.org/10.20944/preprints202508.0708.v1

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Implication

This framework demonstrates improved energy efficiency and voltage regulation in renewable energy grids, indicating the potential of AI and digital twins for climate adaptation.

AI-Powered Digital Twin Co-Simulation Framework for Climate-Adaptive Renewable Energy Grids

Discussion

Member takes

Implication

Key Points

Cite This Study

AI-Powered Digital Twin Co-Simulation Framework for Climate-Adaptive Renewable Energy Grids

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider