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

Kwabena Addo (), Musasa Kabeya and Evans Eshiemogie Ojo
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Kwabena Addo: Department of Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa
Musasa Kabeya: Department of Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa
Evans Eshiemogie Ojo: Department of Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa

Energies, 2025, vol. 18, issue 21, 1-25

Abstract: Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive control of distributed energy resources (DERs) and storage assets in distribution networks. The framework leverages deep reinforcement learning (DDPG) agents trained within a high-fidelity co-simulation environment that couples physical grid dynamics, weather disturbances, and cyber-physical control loops using HELICS middleware. Through real-time coordination of photovoltaic systems, wind turbines, battery storage, and demand side flexibility, the trained agent autonomously learns to minimize power losses, voltage violations, and load shedding under stochastic climate perturbations. Simulation results on the IEEE 33-bus radial test system augmented with ERA5 climate reanalysis data demonstrate improvements in voltage regulation, energy efficiency, and resilience metrics. The framework also exhibits strong generalization across unseen weather scenarios and outperforms baseline rule based controls by reducing energy loss by 14.6% and improving recovery time by 19.5%. These findings position AI-integrated digital twins as a promising paradigm for future-proof, climate-resilient smart grids.

Keywords: climate resilience; co-simulation; digital twin; distributed energy resources; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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