Issue
Sci. Tech. Energ. Transition
Volume 79, 2024
Emerging Advances in Hybrid Renewable Energy Systems and Integration
Article Number 80
Number of page(s) 13
DOI https://doi.org/10.2516/stet/2024045
Published online 10 October 2024
  • Roy P.K., Ghosh M. (2017) Combined heat and power dispatch using hybrid genetic algorithm and biogeography-based optimization, Int. J. Energy Optimiz. Eng. (IJEOE) 6, 1, 49–65. [Google Scholar]
  • Shuai H., He H. (2020) Online scheduling of a residential microgrid via Monte-Carlo tree search and a learned model, IEEE Trans. Smart Grid 12, 2, 1073–1087. [Google Scholar]
  • Dan M., Srinivasan S., Sundaram S., Easwaran A., Glielmo L. (2020) A scenario-based branch-and-bound approach for MES scheduling in urban buildings, IEEE Trans. Industr. Inform. 16, 12, 7510–7520. [CrossRef] [Google Scholar]
  • Chen M., Shen Z., Wang L., Zhang G. (2022) Intelligent energy scheduling in renewable integrated microgrid with bidirectional electricity-to-hydrogen conversion, IEEE Trans. Netw. Sci. Eng. 9, 4, 2212–2223. [CrossRef] [Google Scholar]
  • Suchetha C., Ramprabhakar J. (2018) Optimization techniques for operation and control of microgrids a review, J. Green Eng. 8, 4, 621–644. [CrossRef] [Google Scholar]
  • Sun Q., Fan R., Li Y., Huang B., Ma D. (2019) A distributed double-consensus algorithm for residential we-energy, IEEE Trans. Industr. Inform. 15, 8, 4830–4842. [Google Scholar]
  • Pei W., Ma X., Deng W., Chen X., Sun H., Li D. (2019) Industrial multi-energy and production management scheme in cyber-physical environments: a case study in a battery manufacturing plant, IET Cyber-Phys. Syst. Theor. Appl. 4, 1, 13–21. [CrossRef] [Google Scholar]
  • Zhou X., Zou S., Wang P., Ma Z. (2020) Optimal energy management in combined heat and power system via a decentralized consensus-based ADMM, IFAC-Papers OnLine 53, 2, 4026–4031. [CrossRef] [Google Scholar]
  • Yang L., Xie P., Zhang R., Cheng Y., Cai B., Wang R. (2019) HIES: cases for hydrogen energy and I-energy, Int. J. Hyd. Energy 44, 56, 29785–29804. [CrossRef] [Google Scholar]
  • Wang Y., Wang Y., Huang Y., Yu H., Du R., Zhang F., Zhu J. (2018) Optimal scheduling of the regional integrated energy system considering economy and environment, IEEE Trans. Sustain. Energy 10, 4, 1939–1949. [Google Scholar]
  • Ren M., Jiang X., Yuan J. (2020) Wind power integration and emission reduction via coal power retrofits in China’s quota-based dispatch system: a case study of Jilin Province, Environ. Sci. Pollut. Res. 27, 10, 11364–11374. [CrossRef] [PubMed] [Google Scholar]
  • Wang D., Wang C., Lei Y., Zhang Z., Zhang N. (2019) Prospects for key technologies of new-type urban integrated energy system, Glob. Energy Interconnect. 2, 5, 402–412. [CrossRef] [Google Scholar]
  • Zhang C., Su Q., Zhu Y. (2023) Urban park system on public health: underlying driving mechanism and planning thinking, Front. Public Health 11, 1193604. [CrossRef] [Google Scholar]
  • Zhou J., Li B., Zhang D., Yuan J., Zhang W., Cai Z. (2023) UGIF-Net: an efficient fully guided information flow network for underwater image enhancement, IEEE Trans. Geosci. Remote Sens. 61, 1–17, Art no. 4206117, doi: 10.1109/TGRS.2023.3293912. [Google Scholar]
  • Ali J., Shan G., Gul N., Roh B.H. (2023) An intelligent blockchain-based secure link failure recovery framework for software-defined internet-of-things, J. Grid Comput. 21, 4, 57. [CrossRef] [Google Scholar]
  • Ali J., Jhaveri R.H., Alswailim M., Roh B.-H. (2023) ESCALB: an effective slave controller allocation-based load balancing scheme for multi-domain SDN-enabled-IoT networks, J. King Saud Univ.-Comput. Inform. Sci. 35, 6, 101566. [Google Scholar]
  • Dong Q., Liu X. (2023) Optimization practice of university innovation and entrepreneurship education based on the perspective of OBE, J. Combinat. Math. Combinat. Comput. 118, 181–189. [CrossRef] [Google Scholar]
  • Liao Q. (2023) English teaching project quality evaluation based on deep decision-making and rule association analysis, J. Combinat. Math. Combinat. Comput. 118, 119–127. [CrossRef] [Google Scholar]
  • Zhou X., Wang J., Wang X., Chen S. (2023) Optimal dispatch of integrated energy system based on deep reinforcement learning, Energy Rep. 9, 373–378. [CrossRef] [Google Scholar]
  • Sage M., Staniszewski M., Zhao Y.F. (2023) Optimal economic gas turbine dispatch with deep reinforcement learning, IFAC-Papers OnLine 56, 2, 10039–10044. [CrossRef] [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.