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Overcoming decision paralysis—A digital twin for decision making in energy system design

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  • Granacher, Julia
  • Nguyen, Tuong-Van
  • Castro-Amoedo, Rafael
  • Maréchal, François

Abstract

The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of process and energy system design that interactively translates needs and preferences of decision makers into an optimization-based model and generates meaningful solutions. The Interactive Digital Twin (InDiT) assists decision makers in steering the exploration of the solution space and guiding them towards relevant system design decisions, taking into account multiple aspects such as the impact of uncertainties and multi-criteria analysis. InDiT enhances step-by-step communication with the decision maker, relying on visual aids to keep the communication during solution generation and exploration intuitive and flexible. In this way, decision makers are guided towards relevant solutions and improve their understanding of relations between the problem definition and system design decisions, while InDiT builds on the decision makers’ preferences and can, after training, suggest solutions that are best-suited to their interests. The novelty of this work lies in the holistic approach of addressing both (i) the systematic generation and exploration of solutions with the assistance of a digital consultant, which translates the decision maker’s needs into machine language and vice versa, and (ii) the interactive step-by-step technique on filtering and evaluating solutions intuitively. This guarantees that the decision maker does not only get solutions based on the design specifications made, but that personal preferences are taken into account during the solution synthesis step, and that the solution space can easily be explored under different criteria. The proposed methodology is demonstrated and applied to the design case of an integrated multi-product biorefinery.

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  • Granacher, Julia & Nguyen, Tuong-Van & Castro-Amoedo, Rafael & Maréchal, François, 2022. "Overcoming decision paralysis—A digital twin for decision making in energy system design," Applied Energy, Elsevier, vol. 306(PA).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012629
    DOI: 10.1016/j.apenergy.2021.117954
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    3. Machado, Diogo Ortiz & Chicaiza, William D. & Escaño, Juan M. & Gallego, Antonio J. & de Andrade, Gustavo A. & Normey-Rico, Julio E. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Digital twin of an absorption chiller for solar cooling," Renewable Energy, Elsevier, vol. 208(C), pages 36-51.
    4. Namita Kumari & Ankush Sharma & Binh Tran & Naveen Chilamkurti & Damminda Alahakoon, 2023. "A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced," Energies, MDPI, vol. 16(14), pages 1-19, July.
    5. Razeen Hashmi & Huai Liu & Ali Yavari, 2024. "Digital Twins for Enhancing Efficiency and Assuring Safety in Renewable Energy Systems: A Systematic Literature Review," Energies, MDPI, vol. 17(11), pages 1-34, May.
    6. Ullah, Sami & Niu, Ben & Meo, Muhammad Saeed, 2024. "Digital inclusion and environmental taxes: A dynamic duo for energy transition in green economies," Applied Energy, Elsevier, vol. 361(C).
    7. Julio, Alisson Aparecido Vitoriano & Castro-Amoedo, Rafael & Maréchal, François & González, Aldemar Martínez & Escobar Palacio, José Carlos, 2023. "Exergy and economic analysis of the trade-off for design of post-combustion CO2 capture plant by chemical absorption with MEA," Energy, Elsevier, vol. 280(C).
    8. Ning, Jiajun & Xiong, Lixin, 2024. "Analysis of the dynamic evolution process of the digital transformation of renewable energy enterprises based on the cooperative and evolutionary game model," Energy, Elsevier, vol. 288(C).
    9. Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T. & Díaz-Ibarra, Oscar H., 2023. "Atikokan Digital Twin, Part B: Bayesian decision theory for process optimization in a biomass energy system," Applied Energy, Elsevier, vol. 334(C).
    10. Hua, Weiqi & Stephen, Bruce & Wallom, David C.H., 2023. "Digital twin based reinforcement learning for extracting network structures and load patterns in planning and operation of distribution systems," Applied Energy, Elsevier, vol. 342(C).
    11. Fuwen Hu & Song Bi & Yuanzhi Zhu, 2024. "Cooperative Game-Based Digital Twin Drives Decision Making: Overall Framework, Basic Formalization and Application Case," Mathematics, MDPI, vol. 12(2), pages 1-22, January.

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