Economic Value Creation of Artificial Intelligence in Supporting Variable Renewable Energy Resource Integration to Power Systems: A Systematic Review

Arsalan Masood, Ubaid Ahmed, Syed Zulqadar Hassan, Ahsan Raza Khan and Anzar Mahmood ()
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Arsalan Masood: Department of Electrical Engineering, University of Sialkot, Sialkot 51310, Pakistan
Ubaid Ahmed: Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250, AJ&K, Pakistan
Syed Zulqadar Hassan: Department of Computer Science, Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore 54000, Pakistan
Ahsan Raza Khan: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Anzar Mahmood: Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250, AJ&K, Pakistan

Sustainability, 2025, vol. 17, issue 6, 1-42

Abstract: The integration of Variable Renewable Energy (VRE) sources in power systems is increased for a sustainable environment. However, due to the intermittent nature of VRE sources, formulating efficient economic dispatching strategies becomes challenging. This systematic review aims to elucidate the economic value creation of Artificial Intelligence (AI) in supporting the integration of VRE sources into power systems by reviewing the role of AI in mitigating costs related to balancing, profile, and grid with a focus on its applications for generation and demand forecasting, market design, demand response, storage solutions, power quality enhancement, and predictive maintenance. The proposed study evaluates the AI potential in economic efficiency and operational reliability improvement by analyzing the use cases with various Renewable Energy Resources (RERs), including wind, solar, geothermal, hydro, ocean, bioenergy, hydrogen, and hybrid systems. Furthermore, the study also highlights the development and limitations of AI-driven approaches in renewable energy sector. The findings of this review aim to highlight AI’s critical role in optimizing VRE integration, ultimately informing policymakers, researchers, and industry stakeholders about the potential of AI for an economically sustainable and resilient energy infrastructure.

Keywords: artificial intelligence; variable renewable energy; energy strategy; demand forecasting; policy strategies; AI economic value (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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