Digital Twin Framework for Hybrid Energy Portfolio Management: Integrating Oil/Gas Assets with Renewable Energy Transition Planning

Vijay Bhalani ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 8, 11 - 20

Abstract: Power companies are increasingly under pressure to reconcile conventional hydrocarbon business with integrating renewables and staying profitable and within environmental goals. Digital twin technology is a game-changer in this respect, as it develops complex computational models of physical infrastructure that exchange data in real-time with field equipment. These systems integrate sophisticated computer vision for asset tracking, predictive analytics to anticipate equipment failure ahead of traditional means, and multi-objective optimization platforms aligning economic returns with sustainability targets. The architectural sophistication required to integrate offshore platform, refinery, wind farm, and solar installation data necessitates forward-looking microservice design and cloud infrastructure. Artificial intelligence integrated into these systems analyzes enormous volumes of data, uncovering latent relationships among weather conditions, equipment performance, and market conditions. This facilitates strategic, proactive maintenance plans and dispatch scheduling optimization over varied asset bases. Hybrid operating schemes showcase excellent synergies as solar thermal networks augment oil recovery processes while in-place pipeline infrastructure is readied for transporting hydrogen. Financial model innovation through generative AI produces artificial scenarios that challenge portfolio robustness to extreme market environments, while new hedging tools address weather-risked generation. The convergence of computational intelligence with physical infrastructure makes energy transformation from a burden into an opportunity, demonstrating that environmental responsibility and shareholder value proceed hand-in-hand through technological innovation.

Keywords: Digital Twin Technology; Hybrid Energy Management; Renewable Energy Integration; Predictive Analytics; Infrastructure Transformation (search for similar items in EconPapers)
Date: 2025
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