IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v241y2019icp291-301.html
   My bibliography  Save this item

A reinforcement learning framework for optimal operation and maintenance of power grids

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
  2. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
  3. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  4. Wu, Tianyi & Yang, Li & Ma, Xiaobing & Zhang, Zihan & Zhao, Yu, 2020. "Dynamic maintenance strategy with iteratively updated group information," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
  5. Xue, Lin & Wang, Jianxue & Zhang, Yao & Yong, Weizhen & Qi, Jie & Li, Haotian, 2023. "Model-data-event based community integrated energy system low-carbon economic scheduling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  6. Fuat Kosanoglu & Mahir Atmis & Hasan Hüseyin Turan, 2024. "A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem," Annals of Operations Research, Springer, vol. 339(1), pages 79-110, August.
  7. Andriotis, C.P. & Papakonstantinou, K.G., 2021. "Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
  8. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  9. Shi, Guannan & Zhang, Xiaohong & Zeng, Jianchao & Liao, Haitao & Shi, Hui & Niu, Huifang & Wang, Jinhe, 2024. "A chance-constrained net revenue model for online dynamic predictive maintenance decision-making," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
  10. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
  11. Stover, Oliver & Karve, Pranav & Mahadevan, Sankaran, 2023. "Reliability and risk metrics to assess operational adequacy and flexibility of power grids," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
  12. Azar, Kamyar & Hajiakhondi-Meybodi, Zohreh & Naderkhani, Farnoosh, 2022. "Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  13. Ning Wang & Weisheng Xu & Weihui Shao & Zhiyu Xu, 2019. "A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids," Energies, MDPI, vol. 12(15), pages 1-26, July.
  14. Tsianikas, Stamatis & Yousefi, Nooshin & Zhou, Jian & Rodgers, Mark D. & Coit, David, 2021. "A storage expansion planning framework using reinforcement learning and simulation-based optimization," Applied Energy, Elsevier, vol. 290(C).
  15. Yi Kuang & Xiuli Wang & Hongyang Zhao & Yijun Huang & Xianlong Chen & Xifan Wang, 2020. "Agent-Based Energy Sharing Mechanism Using Deep Deterministic Policy Gradient Algorithm," Energies, MDPI, vol. 13(19), pages 1-20, September.
  16. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
  17. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
  18. Hugo Gaspar Hernandez-Palma & Vladimir Sousa Santos & Adalberto Ospino Castro & Angélica Jiménez Coronado & Roberto Morales Espinoza & Jonny Rafael Plazas Alvarado, 2024. "Sustainable Projects Based on the Intersection of Clean Energy with the Health Sector: A Bibliometric Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 489-496, May.
  19. Daeil Lee & Seoryong Koo & Inseok Jang & Jonghyun Kim, 2022. "Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation," Energies, MDPI, vol. 15(8), pages 1-25, April.
  20. Nguyen, Van-Thai & Do, Phuc & Vosin, Alexandre & Iung, Benoit, 2022. "Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
  21. Najafi, Seyedvahid & Lee, Chi-Guhn, 2023. "A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
  22. Ahmed H. Sakr & Ayman Aboelhassan & Soumaya Yacout & Samuel Bassetto, 2023. "Simulation and deep reinforcement learning for adaptive dispatching in semiconductor manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1311-1324, March.
  23. Dehghani, Nariman L. & Jeddi, Ashkan B. & Shafieezadeh, Abdollah, 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning," Applied Energy, Elsevier, vol. 285(C).
  24. Shang, Yuwei & Wu, Wenchuan & Guo, Jianbo & Ma, Zhao & Sheng, Wanxing & Lv, Zhe & Fu, Chenran, 2020. "Stochastic dispatch of energy storage in microgrids: An augmented reinforcement learning approach," Applied Energy, Elsevier, vol. 261(C).
  25. Luca Pinciroli & Piero Baraldi & Guido Ballabio & Michele Compare & Enrico Zio, 2021. "Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews," Energies, MDPI, vol. 14(20), pages 1-17, October.
  26. Goedhart, Joost & Haijema, René & Akkerman, Renzo, 2023. "Modelling the influence of returns for an omni-channel retailer," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1248-1263.
  27. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
  28. Shi, Yan & Lu, Zhenzhou & Huang, Hongzhong & Liu, Yu & Li, Yanfeng & Zio, Enrico & Zhou, Yicheng, 2022. "A new preventive maintenance strategy optimization model considering lifecycle safety," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
  29. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
  30. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
  31. Guan, Xiaoshu & Xiang, Zhengliang & Bao, Yuequan & Li, Hui, 2022. "Structural dominant failure modes searching method based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
  32. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  33. Compare, Michele & Antonello, Federico & Pinciroli, Luca & Zio, Enrico, 2022. "A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
  34. Mohammadi, Reza & He, Qing, 2022. "A deep reinforcement learning approach for rail renewal and maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  35. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
  36. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
  37. Totaro, Simone & Boukas, Ioannis & Jonsson, Anders & Cornélusse, Bertrand, 2021. "Lifelong control of off-grid microgrid with model-based reinforcement learning," Energy, Elsevier, vol. 232(C).
  38. Dimitrios Vamvakas & Panagiotis Michailidis & Christos Korkas & Elias Kosmatopoulos, 2023. "Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications," Energies, MDPI, vol. 16(14), pages 1-38, July.
  39. Koziel, Sylvie & Hilber, Patrik & Westerlund, Per & Shayesteh, Ebrahim, 2021. "Investments in data quality: Evaluating impacts of faulty data on asset management in power systems," Applied Energy, Elsevier, vol. 281(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.