Open Access
Issue
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
Article Number 13005
Number of page(s) 8
Section Other Renewable Energies
DOI https://doi.org/10.1051/e3sconf/202454013005
Published online 21 June 2024
  1. Y. Xu, C. Li, Z. Wang, N. Zhang and B. Peng, “Load Frequency Control of a Novel Renewable Energy Integrated Micro-Grid Containing Pumped Hydropower Energy Storage,” in IEEE Access, vol. 6, pp. 29067–29077, 2018, doi: 10.1109/ACCESS.2018.2826015. [CrossRef] [Google Scholar]
  2. C. Lv, R. Liang and Y. Chai, “Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations,” in Journal of Modern Power Systems and Clean Energy, vol. 11, no. 1, pp. 179–190, January 2023, doi: 10.35833/MPCE.2022.000436. [CrossRef] [Google Scholar]
  3. S. Sharma, A. Verma, Y. Xu and B. K. Panigrahi, “Robustly Coordinated Bi-Level Energy Management of a Multi-Energy Building Under Multiple Uncertainties,” in IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 3–13, Jan. 2021, doi: 10.1109/TSTE.2019.2962826. [CrossRef] [Google Scholar]
  4. L. Fabietti, T. T. Gorecki, F. A. Qureshi, A. Bitlislioğlu, I. Lymperopoulos and C. N. Jones, “Experimental Implementation of Frequency Regulation Services Using Commercial Buildings,” in IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 1657–1666, May 2018, doi: 10.1109/TSG.2016.2597002. [CrossRef] [Google Scholar]
  5. A. Bolzoni, A. Parisio, R. Todd and A. J. Forsyth, “Optimal Virtual Power Plant Management for Multiple Grid Support Services,” in IEEE Transactions on Energy Conversion, vol. 36, no. 2, pp. 1479–1490, June 2021, doi: 10.1109/TEC.2020.3044421. [CrossRef] [Google Scholar]
  6. L. Tziovani, L. Hadjidemetriou, P. Kolios, A. Astolfi, E. Kyriakides and S. Timotheou, “Energy Management and Control of Photovoltaic and Storage Systems in Active Distribution Grids,” in IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 1956–1968, May 2022, doi: 10.1109/TPWRS.2021.3118785. [CrossRef] [Google Scholar]
  7. N. Good and P. Mancarella, “Flexibility in Multi-Energy Communities With Electrical and Thermal Storage: A Stochastic, Robust Approach for Multi-Service Demand Response,” in IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 503–513, Jan. 2019, doi: 10.1109/TSG.2017.2745559. [CrossRef] [Google Scholar]
  8. K. Thirugnanam, M. S. El Moursi, V. Khadkikar, H. H. Zeineldin and M. A. Hosani, “Energy Management Strategy of a Reconfigurable Grid-Tied Hybrid AC/DC Microgrid for Commercial Building Applications,” in IEEE Transactions on Smart Grid, vol. 13, no. 3, pp. 1720–1738, May 2022, doi: 10.1109/TSG.2022.3141459. [CrossRef] [Google Scholar]
  9. J. J. Estrada-López, A. A. Castillo-Atoche, J. Vázquez-Castillo and E. Sánchez-Sinencio, “Smart Soil Parameters Estimation System Using an Autonomous Wireless Sensor Network With Dynamic Power Management Strategy,” in IEEE Sensors Journal, vol. 18, no. 21, pp. 8913–8923, 1 Nov.1, 2018, doi: 10.1109/JSEN.2018.2867432. [CrossRef] [Google Scholar]
  10. F. K. Shaikh, S. Karim, S. Zeadally and J. Nebhen, “Recent Trends in Internet-of-Things-Enabled Sensor Technologies for Smart Agriculture,” in IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23583–23598, 1 Dec.1, 2022, doi: 10.1109/JIOT.2022.3210154. [CrossRef] [Google Scholar]
  11. R. Kumar, P. Kumar, A. Aljuhani, A. K. M. N. Islam, A. Jolfaei and S. Garg, “Deep Learning and Smart Contract-Assisted Secure Data Sharing for IoT-Based Intelligent Agriculture,” in IEEE Intelligent Systems, vol. 38, no. 4, pp. 42–51, July-Aug. 2023, doi: 10.1109/MIS.2022.3201553. [CrossRef] [Google Scholar]
  12. E. -T. Bouali, M. R. Abid, E. -M. Boufounas, T. A. Hamed and D. Benhaddou, “Renewable Energy Integration Into Cloud & IoT-Based Smart Agriculture,” in IEEE Access, vol. 10, pp. 1175–1191, 2022, doi: 10.1109/ACCESS.2021.3138160. [CrossRef] [Google Scholar]
  13. O. Gulec, E. Haytaoglu and S. Tokat, “A Novel Distributed CDS Algorithm for Extending Lifetime of WSNs With Solar Energy Harvester Nodes for Smart Agriculture Applications,” in IEEE Access, vol. 8, pp. 58859–58873, 2020, doi: 10.1109/ACCESS.2020.2983112. [CrossRef] [Google Scholar]
  14. N. Abdullah, et al., “Towards Smart Agriculture Monitoring Using Fuzzy Systems,” in IEEE Access, vol. 9, pp. 4097–4111, 2021, doi: 10.1109/ACCESS.2020.3041597. [CrossRef] [Google Scholar]
  15. Mohammed, S. Q., & Hussein, M. A. E. (2023). Reducing False Negative Intrusions Rates of Ensemble Machine Learning Model based on Imbalanced Multiclass Datasets. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 14(2), 12–30. [CrossRef] [Google Scholar]
  16. Liloja, & Ranjana, P. (2023). An Intrusion Detection System Using a Machine Learning Approach in IOT-based Smart Cities. Journal of Internet Services and Information Security, 13(1), 11–21. [CrossRef] [Google Scholar]
  17. Sugumar R., et.al IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES, Revista de Gestao Social e Ambiental, V-17, I-4, 2023. [Google Scholar]

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