Generative AI-based spatiotemporal resilience, green and low-carbon transformation strategy of smart renewable energy systems

Abstract

The extensive integration of AI with renewable energy systems is a major trend in technological advancement, but its energy consumption and carbon emissions are also a major challenge. Generative AI can quickly generate human-like content responding to cues, with excellent reasoning and generative capabilities. Generative AI-based renewable energy systems can cope with dynamic system changes and have great potential for resilience optimization and green low-carbon transition. In this paper, we first explore the role that generative AI can play in renewable energy systems and explain shock incidents. Secondly, intelligent maintenance strategies of renewable energy systems under different failure modes are developed based on generative AI. Then spatiotemporal resilience is introduced and a spatiotemporal resilience optimization model is proposed. A green and low-carbon transformation strategy for smart renewable energy systems has also been proposed. Finally, a case study is used to illustrate the utilization of the proposed method by using a wind power system as an example of a renewable energy system.

Graphical abstract

Keywords

generative AI / spatiotemporal resilience / carbon transformation / smart renewable energy

References

[1] Ahmed Z, Shanto S S, Rime M H K, Morol M K, Fahad N, Hossen M J, Abdullah-Al-Jubair M, (2024). The generative AI landscape in education: Mapping the terrain of opportunities, challenges, and student perception. IEEE Access: Practical Innovations, Open Solutions, 12: 147023–147050

[2] Albahri A S, Zahid I A, Yaseen M G, Aljanabi M, Ali A H, Kaleel A, (2024). Harnessing the tide of innovation: The dual faces of generative AI in applied sciences; letter to editor. Applied Data Science and Analysis, 2024: 1–3

[3] Bandi A, Adapa P, Kuchi Y, (2023). The power of generative AI: A review of requirements, models, input–output formats, evaluation metrics, and challenges. Future Internet, 15( 8): 260–320

[4] Berrade M D, Calvo E, Badía F G, (2023). Maintenance of systems with critical components. Prevention of early failures and wear-out. Computers & Industrial Engineering, 181: 109–291

[5] Bruneau M, Chang S E, Eguchi R T, Lee G C, D.O’Rourke T, Reinhorn A M, Shinozuka M, Tierney K, A.Wallace W, VON Winterfeldt D, (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19( 4): 733–752

[6] Cadini F, Agliardi G L, Zio E, (2017). A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions. Applied Energy, 185: 267–279

[7] Che H, Zeng S, Guo J, Wang Y, (2018). Reliability modeling for dependent competing failure processes with mutually dependent degradation process and shock process. Reliability Engineering & System Safety, 180: 168–178

[8] Cimellaro G P, Reinhorn A M, Bruneau M, (2010). Framework for analytical quantification of disaster resilience. Engineering Structures, 32( 11): 3639–3649

[9] Coello C A C, Pulido G T, Lechuga M S, (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8( 3): 256–279

[10] Dekker R, Wildeman R E, van der Duyn Schouten F A, (1997). A review of multi-component maintenance models with economic dependence. Mathematical Methods of Operations Research, 45( 3): 411–435

[11] Ding F, Tian Z, (2011). Opportunistic maintenance optimization for wind turbine systems considering imperfect maintenance actions. International Journal of Reliability Quality and Safety Engineering, 18( 5): 463–481

[12] Ding F, Tian Z, (2012). Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds. Renewable Energy, 45: 175–182

[13] Dong S J, Gao X Y, Mostafavi A, Gao J X, Gangwal U, (2023). Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability. Reliability Engineering & System Safety, 232: 109071

[14] Dui H, Liu M, Song J, Wu S, (2023a). Importance measure-based resilience management: Review, methodology and perspectives on maintenance. Reliability Engineering & System Safety, 237: 109383

[15] DuiHWuXWuSXieM (2024a). Importance measure-based maintenance strategy optimization: Fundamentals, applications and future directions in AI and IoT. Frontiers of Engineering Management, 1–26

[16] Dui H, Zhang Y, Bai G, (2024b). Analysis of variable system cost and maintenance strategy in life cycle considering different failure modes. Reliability Engineering & System Safety, 243: 109824

[17] Dui H, Zhang Y, Zhang Y, (2023b). Grouping maintenance policy for improving reliability of wind turbine systems considering variable cost. Mathematics, 11( 8): 1954

[18] Dui H, Zhu Y, Tao J, (2024c). Multi-phased resilience methodology of urban sewage treatment network based on the phase and node recovery importance in IoT. Reliability Engineering & System Safety, 247: 110–130

[19] Erguido A, Crespo Márquez A, Castellano E, Gómez Fernández J F, (2017). A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms. Renewable Energy, 114( B): 843–856

[20] Ferrario E, Poulos A, Castro S, de la Llera J C, Lorca A, (2022). Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks. Reliability Engineering & System Safety, 217: 108040

[21] Goerger S R, Madni A M, Eslinger O J, (2014). Engineered resilient systems: A DoD perspective. Procedia Computer Science, 28: 865–872

[22] Guan Y, (2024). Flight price prediction web-based platform: leveraging generative AI for real-time airfare forecasting. Journal of Web Engineering, 23( 2): 299–314

[23] Hosseini S, Barker K, Ramirez-Marquez J E, (2016). A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145: 47–61

[24] Iannacone L, Sharma N, Tabandeh A, Gardoni P, (2022). Modeling time-varying reliability and resilience of deteriorating infrastructure. Reliability Engineering & System Safety, 217: 108074

[25] Izquierdo J, Crespo Márquez A, Uribetxebarria J, Erguido A, (2019). Framework for managing maintenance of wind farms based on a clustering approach and dynamic opportunistic maintenance. Energies, 12( 11): 2036

[26] Kasneci E, Sessler K, Küchemann S, Bannert M, Dementieva D, Fischer F, Gasser U, Groh G, Günnemann S, Hüllermeier E, Krusche S, Kutyniok G, Michaeli T, Nerdel C, Pfeffer J, Poquet O, Sailer M, Schmidt A, Seidel T, Stadler M, Weller J, Kuhn J, Kasneci G, (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103: 102274

[27] Leonenko N, Scalas E, Trinh M, (2017). The fractional non-homogeneous Poisson process. Statistics & Probability Letters, 120: 147–156

[28] Li M, Jiang X, Negenborn R R, (2021). Opportunistic maintenance for offshore wind farms with multiple-component age-based preventive dispatch. Ocean Engineering, 231: 109062

[29] Li M, Wang M, Kang J, Sun L, Jin P, (2020). An opportunistic maintenance strategy for offshore wind turbine system considering optimal maintenance intervals of subsystems. Ocean Engineering, 216: 108067

[30] Liu H, Wang C, Ju P, Li H, (2022). A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures. Renewable & Sustainable Energy Reviews, 156: 111945

[31] McMorland J, Collu M, McMillan D, Carroll J, Coraddu A, (2023). Opportunistic maintenance for offshore wind: A review and proposal of future framework. Renewable & Sustainable Energy Reviews, 184: 113571

[32] MettasA (2000). Reliability allocation and optimization for complex systems. In: Annual Reliability and Maintainability Symposium. 2000 Proceedings. International symposium on product quality and integrity. IEEE, 216–221

[33] Nakagawa T, (1988). Sequential imperfect preventive maintenance policies. IEEE Transactions on Reliability, 37( 3): 295–298

[34] Rahnamay-Naeini M, Hayat M M, (2016). Cascading failures in interdependent infrastructures: An interdependent Markov-chain approach. IEEE Transactions on Smart Grid, 7( 4): 1997–2006

[35] Saleh A, Chiachío M, Salas J F, Kolios A, (2023). Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets. Reliability Engineering & System Safety, 231: 109013

[36] Sarker B R, Faiz T I, (2016). Minimizing maintenance cost for offshore wind turbines following multi-level opportunistic preventive strategy. Renewable Energy, 85: 104–113

[37] Talukder S, Ibrahim M, Kumar R, (2021). Resilience indices for power/cyberphysical systems. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 51( 4): 2159–2172

[38] Van Horenbeek A, Van Ostaeyen J, Duflou J R, Pintelon L, (2013). Quantifying the added value of an imperfectly performing condition monitoring system-application to a wind turbine gearbox. Reliability Engineering & System Safety, 111: 45–57

[39] WangHPhamH (2006). Optimal preparedness maintenance of multi-unit systems with imperfect maintenance and economic dependence, In: Springer Series in Reliability Engineering, Springer, 135–150

[40] Wang X, Zhao X, Wang S, Sun L, (2020). Reliability and maintenance for performance-balanced systems operating in a shock environment. Reliability Engineering & System Safety, 195: 106705

[41] Yan R, Dunnett S, (2022). Resilience assessment for nuclear power plants using Petri nets. Annals of Nuclear Energy, 176: 109282

[42] Zeng Z, Fang Y, Zhai Q, Du S, (2021). A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events. Reliability Engineering & System Safety, 209: 107443

[43] Zhang C, Qi F, Zhang N, Li Y, Huang H, (2022). Maintenance policy optimization for multi-component systems considering dynamic importance of components. Reliability Engineering & System Safety, 226: 108705

[44] Zhang R, Du H Y, Liu Y Q, Niyato D, Kang J W, Sun S M, Shen X M, Poor H V, (2024). Interactive AI with retrieval-augmented generation for next generation networking. IEEE Network, 38( 6): 414–424

[45] Zhao X, Guo X, Wang X, (2018). Reliability and maintenance policies for a two-stage shock model with self-healing mechanism. Reliability Engineering & System Safety, 172: 185–194

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