EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 392 (2024) MATEC Web Conf., 392 (2024) 01183 References
Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01183
Number of page(s) 11
DOI https://doi.org/10.1051/matecconf/202439201183
Published online 18 March 2024
  1. Q. Geng, H. Yuan, and F. Liu, “Output regulation for decentralized networked control systems under jamming attacks and actuator saturation,” ISA Trans, vol. 102, pp. 33–42, Jul. 2020, doi: 10.1016/j.isatra.2020.03.010. [CrossRef] [Google Scholar]
  2. Y. Cui, B. Luo, Z. Feng, T. Huang, and X. Gong, “Resilient state containment of multi-agent systems against composite attacks via output feedback: A sampled-based event-triggered hierarchical approach,” Inf Sci (N Y), vol. 629, pp. 77–95, Jun. 2023, doi: 10.1016/j.ins.2023.01.125. [CrossRef] [Google Scholar]
  3. S. Li, Y. Pan, P. Xu, and N. Zhang, “A decentralized peer-to-peer control scheme for heating and cooling trading in distributed energy systems,” J Clean Prod, vol. 285, Feb. 2021, doi: 10.1016/j.jclepro.2020.124817. [Google Scholar]
  4. S. Talari, M. Khorasany, R. Razzaghi, W. Ketter, and A. S. Gazafroudi, “Mechanism design for decentralized peer-to-peer energy trading considering heterogeneous preferences,” Sustain Cities Soc, vol. 87, Dec. 2022, doi: 10.1016/j.scs.2022.104182. [CrossRef] [Google Scholar]
  5. G. Zhang, J. Li, Y. Xing, O. Bamisile, and Q. Huang, “Data-driven load frequency cooperative control for multi-area power system integrated with VSCs and EV aggregators under cyber-attacks,” ISA Trans, Dec. 2023, doi: 10.1016/j.isatra.2023.09.018. [Google Scholar]
  6. “Decentralized Control Strategies for Resilient Power Systems using Multi-Agent Systems – Search | ScienceDirect.com.” Accessed: Jan. 05, 2024. [Online]. Available: https://www.sciencedirect.com/search?qs=Decentralized%20Control%20Strategies%20for%20Resilient%20Power%20Systems%20using%20Multi-Agent%20Systems [Google Scholar]
  7. P. Ge, F. Teng, C. Konstantinou, and S. Hu, “A resilience-oriented centralised-to-decentralised framework for networked microgrids management,” Appl Energy, vol. 308, Feb. 2022, doi: 10.1016/j.apenergy.2021.118234. [Google Scholar]
  8. M. Tebkew and H. B. Atinkut, “Impact of forest decentralization on sustainable forest management and livelihoods in East Africa,” Trees, Forests and People, vol. 10, Dec. 2022, doi: 10.1016/j.tfp.2022.100346. [CrossRef] [Google Scholar]
  9. M. Mukherjee, T. Hardy, J. C. Fuller, and A. Bose, “Implementing multi-settlement decentralized electricity market design for transactive communities with imperfect communication,” Appl Energy, vol. 306, Jan. 2022, doi: 10.1016/j.apenergy.2021.117979. [CrossRef] [Google Scholar]
  10. C. Tang, M. Liu, Y. Dai, Z. Wang, and M. Xie, “Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids,” Appl Energy, vol. 252, Oct. 2019, doi: 10.1016/j.apenergy.2019.113361. [CrossRef] [Google Scholar]
  11. Y. L. Li, Y. P. Tsang, C. H. Wu, and C. K. M. Lee, “A multi-agent digital twin–enabled decision support system for sustainable and resilient supplier management,” Comput Ind Eng, vol. 187, p. 109838, Jan. 2024, doi: 10.1016/J.CIE.2023.109838. [CrossRef] [Google Scholar]
  12. H. Talebiyan and L. Dueñas-Osorio, “Auctions for resource allocation and decentralized restoration of interdependent networks,” Reliab Eng Syst Saf, vol. 237, Sep. 2023, doi: 10.1016/j.ress.2023.109301. [CrossRef] [Google Scholar]
  13. H. Qiu et al., “Decentralized mixed-integer optimization for robust integrated electricity and heat scheduling,” Appl Energy, vol. 350, Nov. 2023, doi: 10.1016/j.apenergy.2023.121693. [Google Scholar]
  14. I. Isakov, M. Vekić, M. Rapaić, I. Todorović, and S. Grabić, “Decentralized self-stabilizing primary control of microgrids,” International Journal of Electrical Power and Energy Systems, vol. 155, Jan. 2024, doi: 10.1016/j.ijepes.2023.109477. [CrossRef] [Google Scholar]
  15. E. Samadi, A. Badri, and R. Ebrahimpour, “Decentralized multi-agent based energy management of microgrid using reinforcement learning,” International Journal of, vol. 122, Nov. 2020, doi: 10.1016/j.ijepes.2020.106211. [Google Scholar]
  16. C. Qin and E. Pournaras, “Coordination of drones at scale: Decentralized energy-aware swarm intelligence for spatio-temporal sensing,” Transp Res Part C Emerg Technol, vol. 157, Dec. 2023, doi: 10.1016/j.trc.2023.104387. [Google Scholar]
  17. D. Yu et al., “Trustworthy decentralized collaborative learning for edge intelligence: A survey,” High-Confidence Computing, vol. 3, no. 3, Sep. 2023, doi: 10.1016/j.hcc.2023.100150. [Google Scholar]
  18. S. E. Ahmadi, D. Sadeghi, M. Marzband, A. Abusorrah, and K. Sedraoui, “Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies,” Energy, vol. 245, Apr. 2022, doi: 10.1016/j.energy.2022.123223. [CrossRef] [Google Scholar]
  19. J. Leng et al., “Towards resilience in Industry 5.0: A decentralized autonomous manufacturing paradigm,” J Manuf Syst, vol. 71, pp. 95–114, Dec. 2023, doi: 10.1016/j.jmsy.2023.08.023. [CrossRef] [Google Scholar]
  20. X. Sun, H. Xie, D. Qiu, Y. Xiao, Z. Bie, and G. Strbac, “Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach,” Appl Energy, vol. 352, Dec. 2023, doi: 10.1016/j.apenergy.2023.121987. [Google Scholar]
  21. M. Khodadadi Arpanahi, A. Nateghi, E. Heydarian-Forushani, and M. Shafie-khah, “A non-cooperative decentralized model for Volt-VAr optimization of active distribution networks with multiple AC and DC microgrids,” International Journal of Electrical Power and Energy Systems, vol. 153, Nov. 2023, doi: 10.1016/j.ijepes.2023.109367. [CrossRef] [Google Scholar]
  22. R. Dutta, H. Kandath, S. Jayavelu, L. Xiaoli, S. Sundaram, and D. Pack, “A decentralized learning strategy to restore connectivity during multi-agent formation control,” Neurocomputing, vol. 520, pp. 33–45, Feb. 2023, doi: 10.1016/j.neucom.2022.11.054. [CrossRef] [Google Scholar]
  23. H. Su, Y. D. Zhong, J. Y. J. Chow, B. Dey, and L. Jin, “EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system,” Transp Res Part C Emerg Technol, vol. 146, Jan. 2023, doi: 10.1016/j.trc.2022.103955. [Google Scholar]
  24. B. Zhang, W. Hu, D. Cao, A. M. Y. M. Ghias, and Z. Chen, “Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach,” Appl Energy, vol. 339, Jun. 2023, doi: 10.1016/j.apenergy.2023.120902. [CrossRef] [Google Scholar]
  25. M. Ghadimi and S. M. Moghaddas-Tafreshi, “Enhancing the economic performance and resilience in a multi-area multi-microgrid system by a decentralized operation model,” Electric Power Systems Research, vol. 224, Nov. 2023, doi: 10.1016/j.epsr.2023.109692. [CrossRef] [Google Scholar]
  26. Md. Z. ul Haq, H. Sood, and R. Kumar, “Effect of using plastic waste on mechanical properties of fly ash based geopolymer concrete,” Mater Today Proc, 2022. [Google Scholar]
  27. M. Nandal, H. Sood, P. K. Gupta, and M. Z. U. Haq, “Morphological and physical characterization of construction and demolition waste,” Mater Today Proc, 2022. [Google Scholar]
  28. S. Kumar, A. Chopra, and M. Z. U. Haq, “EXPERIMENTAL INVESTIGATION ON MARBLE DUST, RICE HUSK ASH, AND FLY ASH BASED GEOPOLYMER BRICK”. [Google Scholar]
  29. V. S. Rana et al., “Assortment of latent heat storage materials using multi criterion decision making techniques in Scheffler solar reflector,” International Journal on Interactive Design and Manufacturing (IJIDeM), pp. 1–15, 2023. [Google Scholar]
  30. H. Sood, R. Kumar, P. C. Jena, and S. K. Joshi, “Optimizing the strength of geopolymer concrete incorporating waste plastic,” Mater Today Proc, 2023. [Google Scholar]
  31. S. Deep, S. Banerjee, S. Dixit, and N. I. Vatin, “Critical Factors Influencing the Performance of Highway Projects: Empirical Evaluation of Indian Projects,” Buildings, vol. 12, no. 6, Jun. 2022, doi: 10.3390/BUILDINGS12060849. [CrossRef] [Google Scholar]
  32. G. Upadhyay et al., “Development of Carbon Nanotube (CNT)-Reinforced Mg Alloys: Fabrication Routes and Mechanical Properties,” Metals (Basel), vol. 12, no. 8, Aug. 2022, doi: 10.3390/MET12081392. [Google Scholar]
  33. M. Makwana et al., “Effect of Mass on the Dynamic Characteristics of Single– and Double-Layered Graphene-Based Nano Resonators,” Materials, vol. 15, no. 16, Aug. 2022, doi: 10.3390/MA15165551. [CrossRef] [Google Scholar]
  34. Y. Kaushik, V. Verma, K. K. Saxena, C. Prakash, L. R. Gupta, and S. Dixit, “Effect of Al2O3 Nanoparticles on Performance and Emission Characteristics of Diesel Engine Fuelled with Diesel–Neem Biodiesel Blends,” Sustainability (Switzerland), vol. 14, no. 13, Jul. 2022, doi: 10.3390/SU14137913. [Google Scholar]
  35. L. Das et al., “Determination of Optimum Machining Parameters for Face Milling Process of Ti6A14V Metal Matrix Composite,” Materials, vol. 15, no. 14, Jul. 2022, doi: 10.3390/MA15144765. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.