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

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.