Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems

AI-Enhanced Operation and Management of Renewable Energy-Integrated Power Systems

Huang, Yujia
Yang, Chao
Ruan, Jiaqi

Hardcover

General Science

ISBN10: 372587090X
ISBN13: 9783725870905
Publisher: Mdpi AG
Published: Mar 23 2026
Pages: 184
Weight: 1.32
Height: 0.63 Width: 6.69 Depth: 9.61
Language: English

This Reprint addresses the emerging paradigm of AI-enhanced operation and management in power systems with high penetration of renewable energy sources. As renewable-rich power systems become increasingly complex and uncertain, AI is poised to reshape traditional operational practices and decision-making processes. The works collated within this Reprint present recent advances in data-driven wind and photovoltaic forecasting, intelligent state estimation and sensing, adaptive and robust scheduling of integrated energy systems, and AI-enabled decision-making for active distribution networks and coupled infrastructures. These studies demonstrate that AI technologies can complement or extend conventional model-based approaches to better manage variability, uncertainty, and scalability challenges. Beyond individual methods, this Reprint highlights a broader shift from static, centralized operation toward adaptive, distributed, and system-aware management frameworks. Several contributions illustrate that AI can integrate heterogeneous information across generation, grids, loads, and external systems, enabling more responsive and low-carbon operational strategies, while others emphasize uncertainty-aware pricing, robust optimization, and voltage or flexibility support aligned with physical and market constraints. By bringing together these perspectives, this Reprint provides a timely overview of how AI-enhanced methodologies are shaping the next generation of renewable-rich power systems.

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