Issue
Sci. Tech. Energ. Transition
Volume 79, 2024
Decarbonizing Energy Systems: Smart Grid and Renewable Technologies
Article Number 45
Number of page(s) 22
DOI https://doi.org/10.2516/stet/2024034
Published online 05 August 2024
  • Sen S., Kumar V. (2018) Microgrid control: A comprehensive survey, Annu. Rev. Control 45, 118–151. [CrossRef] [MathSciNet] [Google Scholar]
  • Chaudhary G., et al. (2021) Review of energy storage and energy management system control strategies in microgrids, Energies 14, 16, 4929. [CrossRef] [Google Scholar]
  • Muqeet H.A., et al. (2022) Sustainable solutions for advanced energy management system of campus microgrids: Model opportunities and future challenges, Sensors 22, 6, 2345. [CrossRef] [PubMed] [Google Scholar]
  • Hossain Md.A., et al. (2019) Evolution of microgrids with converter-interfaced generations: Challenges and opportunities, Int. J. Electr. Power Energy Syst. 109, 160–186. [CrossRef] [Google Scholar]
  • Haseeb J., et al. (2021) Optimal energy management of a campus microgrid considering financial and economic analysis with demand response strategies, Energies 2021, 14, 8501. [Google Scholar]
  • Bin L., et al. (2022) Scheduling and sizing of campus microgrid considering demand response and economic analysis, Sensors 22, 16, 6150. [CrossRef] [PubMed] [Google Scholar]
  • Elnady A., et al. (2022) A comprehensive review of centralized current/power control schemes for parallel inverters and AC microgrids, IEEE Access 10, 125061–125085. [CrossRef] [Google Scholar]
  • Kim H., et al. (2019) Direct energy trading of microgrids in distribution energy market, IEEE Trans. Power Syst. 35, 1, 639–651. [Google Scholar]
  • Farzin H., et al. (2017) A market mechanism to quantify emergency energy transactions value in a multi-microgrid system, IEEE Trans. Sustain. Energy 10, 1, 426–437. [Google Scholar]
  • Jumani T.A., et al. (2019) Optimal power flow controller for grid-connected microgrids using grasshopper optimization algorithm, Electronics 8, 1, 111. [CrossRef] [Google Scholar]
  • Jumani T.A., et al. (2020) Swarm intelligence-based optimization techniques for dynamic response and power quality enhancement of AC microgrids: A comprehensive review, IEEE Access 8, 75986–76001. [CrossRef] [Google Scholar]
  • Vishal V., et al. (2014) Online PI controller tuning for a nonlinear plant using genetic algorithm. In: 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH), IEEE. [Google Scholar]
  • Abdolrasol Maher G.M., et al. (2022) Optimal PI controller based PSO optimization for PV inverter using SPWM techniques, Energy Rep. 8, 1003–1011. [CrossRef] [Google Scholar]
  • Ramasamy V., et al. (2023) A comprehensive review on Advanced Process Control of cement kiln process with the focus on MPC tuning strategies, J. Process Control 121, 85–102. [CrossRef] [Google Scholar]
  • Jia L.Y., et al. (2023) A weighted-sum chaotic sparrow search algorithm for interdisciplinary feature selection and data classification, Sci. Rep. 13, 1, 14061. [CrossRef] [Google Scholar]
  • Al-Betar M.A., et al. (2023) Economic load dispatch using memetic sine cosine algorithm, J. Ambient Intell. Human. Comput. 14, 9, 11685–11713. [CrossRef] [Google Scholar]
  • Gupta S., Rong S. (2023) Diversity-enhanced modified sine cosine algorithm and its application in solving engineering design problems, J. Comput. Sci. 72, 102105. [CrossRef] [Google Scholar]
  • Pham V.H.S., Nguyen Dang N.T., Nguyen V.N. (2023) Hybrid sine cosine algorithm with integrated roulette wheel selection and opposition-based learning for engineering optimization problems, Int. J. Comput. Intell. Syst. 16, 1, 171. [CrossRef] [Google Scholar]
  • Mousa A.A., El-Shorbagy M.A., Farag M.A. (2020) Steady-state sine cosine genetic algorithm based chaotic search for nonlinear programming and engineering applications, IEEE Access 8, 212036–212054. [CrossRef] [Google Scholar]
  • Chun Y., Hua X. (2023) Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy, IEEE Access. [Google Scholar]
  • Alsoul M., et al. (2023) A new efficient hybrid approach for machine learning-based firefly optimization, Iraqi J. Sci. 4600–4612. [CrossRef] [Google Scholar]
  • Patel C., Malakar T., Sreejith S. (2023) Assessment of converter performance in hybrid AC–DC power system under optimal power flow with minimum number of DC link control variables, Energies 16, 15, 5800. [CrossRef] [Google Scholar]
  • Thiruvenkadam S., Kim H.-J., Ra I.-H. (2020) Hybrid fuzzy and flower pollination optimization algorithm for optimal dispatch of generating units in the existence of electric vehicles, in2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE. [Google Scholar]
  • Alweshah M., et al. (2020) Flower pollination algorithm for solving classification problems, Int. J. Adv. Soft Comput. Appl. 12, 1, 15–34. [Google Scholar]
  • Singh N., Kaur J. (2021) Hybridizing sine–cosine algorithm with harmony search strategy for optimization design problems, Soft Comput. 25, 16, 11053–11075. [CrossRef] [Google Scholar]
  • Dashtaki A.A., et al. (2023) Optimal management algorithm of microgrid connected to the distribution network considering renewable energy system uncertainties, Int. J. Electr. Power Energy Syst. 145, 108633. [CrossRef] [Google Scholar]
  • Fahim K.E., et al. (2023) A state-of-the-art review on optimization methods and techniques for economic load dispatch with photovoltaic systems: Progress, challenges, and recommendations, Sustainability 15, 15, 11837. [CrossRef] [Google Scholar]
  • Wang Y., et al. (2023) Adaptive chimp optimization algorithm with chaotic map for global numerical optimization problems, J. Supercomput. 79, 6, 6507–6537. [CrossRef] [Google Scholar]
  • Lawley S.D. (2023) Extreme statistics of superdiffusive Lévy flights and every other Lévy subordinate Brownian motion, J. Nonlinear Sci. 33, 4, 53. [CrossRef] [Google Scholar]
  • Zamee M.A., Han D., Won D. (2023) Integrated grid forming-grid following inverter fractional order controller based on Monte Carlo artificial Bee Colony optimization, Energy Rep. 9, 57–72. [CrossRef] [Google Scholar]
  • Sayed E.A., et al. (2023) Enhancement of PV performance by using hybrid TLBO-EO optimization, Ain Shams Eng. J. 14, 3, 101892. [CrossRef] [Google Scholar]
  • Abid S., et al. (2023) Development of slime mold optimizer with application for tuning cascaded PD-PI controller to enhance frequency stability in power systems, Mathematics 11, 8, 1796. [CrossRef] [Google Scholar]
  • El-Sehiemy R., et al. (2023) Proportional-integral-derivative controller based-artificial rabbits algorithm for load frequency control in multi-area power systems, Fractal Fract. 7, 1, 97. [CrossRef] [Google Scholar]
  • Hussien A.M., et al. (2022) Coot bird algorithms-based tuning PI controller for optimal microgrid autonomous operation, IEEE Access 10, 6442–6458. [CrossRef] [Google Scholar]
  • Huba M., et al. (2021) Making the PI and PID controller tuning inspired by Ziegler and Nichols precise and reliable, Sensors 21, 18, 6157. [CrossRef] [PubMed] [Google Scholar]
  • Song M., et al. (2022) Modified harris hawks optimization algorithm with exploration factor and random walk strategy, Comput. Intell. Neurosci.. [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.