Open Access

REVIEW

Next-Generation Wind Hybrid Energy Systems: Grid-Interactive, Hydrogen-Enabled, and AI-Orchestrated Pathways for Sustainable Electrification

Jalpa Thakkar¹, Sidhharth Shankar Mishra², V. Shanmugapriya³, Mohan Kolhe^4,*
1 Department of Electrical Engineering, UPL University of Sustainable Technology, Ankleshwar, India
2 Energy Cluster, University of Petroleum and Energy Studies, Dehradun, India
3 Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
4 Faculty of Engineering & Science, University of Agder, Kristiansand, Norway
* Corresponding Author: Mohan Kolhe. Email:
(This article belongs to the Special Issue: Advances in Grid Integration and Electrical Engineering of Wind Energy Systems: Innovations, Challenges, and Applications)

Energy Engineering https://doi.org/10.32604/ee.2026.078267

Received 27 December 2025; Accepted 06 March 2026; Published online 07 April 2026

Abstract

The big challenge in developing wind energy over the past century, which has focused on environmentally friendly production methods to meet the requirements of modern power systems, is the need for holistic architectures that can cope with variability, connection issues, and sector coupling far beyond conventional electricity-only models. This review offers a critically synthesized, process-level overview of progressive wind–hydrogen hybrids, offering a collective view of advancements in electrical layouts, hydrogen-driven conversion routes, and AI-driven control schemes. In contrast to previous surveys that consider these areas in isolation, we provide an explicit examination of the technical relationships among them, their deployment maturity, and operational trade-offs. The review is a knowledge synthesis of more than 160 peer-reviewed studies published between 2013 and 2026, covering simulation-based research, laboratory model tests, pilot demonstrations, and early utility model installations. Reported data are assessed in the context of experimental and model assumptions. Electrical conversion efficiencies for AC, DC, and hybrid AC/DC architectures typically range from 88%–97% in simulation and laboratory studies. They are lower for field-deployed systems due to protection, filtering, and grid-code compliance requirements—constraint satisfaction. Hence, harmonic distortion below 2%–4% can typically be obtained under controlled conditions by MMC; higher values have been measured in weak grids or unvalidated setups. AI-driven forecasting models, such as the rainbow LSTM hybrids, have achieved sub-10% errors mainly in data-rich simulation/digital twin environments, and real-world accuracy is highly dependent on data quality, spatial resolution, and retraining frequency (216). Documented maintenance cost and degradation-reduction benefits from AI-assisted control are shown to be scenario-specific and often based on simulated or pilot-scale studies with restricted operating conditions. A TRL-based perspective is used to distinguish advanced electrical subsystems (TRL 7–9) from proficient hydrogen conversion technology (TRL 3–8) and AI-enabled real-time control architectures that are predominantly at the demonstration or pilot stage. It discusses additional limitations based on the complexity of system protection, cybersecurity, data integrity, and techno-economic uncertainties, concluding that, in addition to efficiency improvements, scalability is determined by regulatory harmonization, hydrogen certification, and market readiness. In summary, these findings position hybrid wind–hydrogen systems as a promising yet unevenly developed path towards grid-interactive low-carbon energy infrastructures that will require concurrent advances in technology development, policy evolution, and system integration.

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Keywords

Hybrid wind–hydrogen systems; AI-orchestrated energy management; grid-interactive architectures; solid-state power electronics; digital twin optimization; quantum-assisted dispatch control; sustainable electrification

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