47
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A modified fractional‑order-based future search algorithm for performance enhancement of a PEMFC-based CCHP

& ORCID Icon
Pages 12821-12843 | Received 11 Apr 2023, Accepted 24 Oct 2023, Published online: 11 Nov 2023

References

  • Aghajani, G., and N. Ghadimi. 2018. Multi-objective energy management in a micro-grid. Energy Reports 4:218–25. doi:10.1016/j.egyr.2017.10.002.
  • Azar, K. K., Kakouee, A., Mollajafari, M, Majdi, A., Ghadimi, N. and Ghadamyari, M. 2022. Developed design of battle royale optimizer for the optimum identification of solid oxide fuel cell. Sustainability 14 (16):9882.
  • Azar, A. T., A. G. Radwan, and S. Vaidyanathan. 2018. Fractional order systems: Optimization, control, circuit realizations and applications. Academic Press.
  • Bauen, A., and D. Hart. 2000. Assessment of the environmental benefits of transport and stationary fuel cells. Journal of Power Sources 86 (1–2):482–94. doi:10.1016/S0378-7753(99)00445-0.
  • Bo, G., Cheng, P, Dezhi, K., Xiping, W., Chaodong, L., Mingming, G. and Ghadimi, N. 2022. Optimum structure of a combined wind/photovoltaic/fuel cell-based on amended Dragon Fly optimization algorithm: A case study. Energy Sources, Part A Recovery, Utilization, & Environmental Effects 44 (3):7109–31.
  • Cai, W., R. Mohammaditab, G. Fathi, K. Wakil, A. G. Ebadi, and N. Ghadimi. 2019. Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach. Renewable Energy 143:1–8. doi:10.1016/j.renene.2019.05.008.
  • Chen, X., G. Gong, Z. Wan, C. Zhang, and Z. Tu. 2016. Performance study of a dual power source residential CCHP system based on PEMFC and PTSC. Energy Conversion and Management 119:163–76. doi:10.1016/j.enconman.2016.04.054.
  • Chen, L.,Huang, H, Tang, P., Yao, D., Yang, H. and Ghadimi, N. 2022. Optimal modeling of combined cooling, heating, and power systems using developed African vulture optimization: A case study in watersport complex. Energy Sources, Part A Recovery, Utilization, & Environmental Effects 44 (2):4296–317.
  • Couceiro, M., and P. Ghamisi. 2016. Fractional-order darwinian PSO. In Fractional order Darwinian particle swarm optimization, 11–20. Springer.
  • Cuevas, E., F. Fausto, and A. González. 2020. The locust swarm optimization algorithm, in new advancements in swarm algorithms: Operators and applications. Springer 139–59.
  • Dehghani, M., M. Ghiasi, T. Niknam, A. Kavousi-Fard, M. Shasadeghi, N. Ghadimi, and F. Taghizadeh-Hesary. 2020. Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability 13 (1):1–1. doi:10.3390/su13010090.
  • Dhiman, G., and V. Kumar. 2018. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems 159:20–50. doi:10.1016/j.knosys.2018.06.001.
  • Duan, F.,Song, F., Chen, S., Khayatnezhad, M. and Ghadimi, N. 2022. Model parameters identification of the PEMFCs using an improved design of crow search algorithm. International Journal of Hydrogen Energy 47 (79):33839–49.
  • Ebrahimi, M., and E. Derakhshan. 2018. Design and evaluation of a micro combined cooling, heating, and power system based on polymer exchange membrane fuel cell and thermoelectric cooler. Energy Conversion and Management 171:507–17. doi:10.1016/j.enconman.2018.06.007.
  • Elsisi, M. 2019. Future search algorithm for optimization. Evolutionary Intelligence 12 (1):21–31. doi:10.1007/s12065-018-0172-2.
  • Eslami, M., Moghadam, H.A, Zayandehroodi, H, and Ghadimi, N. A New formulation to reduce the number of variables and constraints to expedite SCUC in bulky power systems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 89, 311–321. 2019. https://doi.org/10.1007/s40010-017-0475-1.
  • Fan, X., H. Sun, Z. Yuan, Z. Li, R. Shi, and N. Ghadimi. 2020. High voltage gain DC/DC converter using coupled inductor and VM techniques. Institute of Electrical and Electronics Engineers Access 8:131975–87. doi:10.1109/ACCESS.2020.3002902.
  • Firouz, M. H., and N. Ghadimi. 2016. Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems 30 (2):845–59. doi:10.3233/IFS-151807.
  • Fragiacomo, P., G. Lucarelli, M. Genovese, and G. Florio. 2021. Multi-objective optimization model for fuel cell-based poly-generation energy systems. Energy 237:121823. doi:10.1016/j.energy.2021.121823.
  • Gallardo, R. P., A. M. Ríos, and J. S. Ramírez. 2020. Analysis of the solar and wind energetic complementarity in Mexico. Journal of Cleaner Production 268:122323. doi:10.1016/j.jclepro.2020.122323.
  • Ghadimi, N. 2013. A method for placement of distributed generation (DG) units using particle swarm optimization. International Journal of Physical Sciences 8 (27):1417–23.
  • Ghadimi, N., Yasoubi, E., Akbari, E, Sabzalian, M.H., Alkhazaleh, H.A, Ghadamyari, M. et al. 2023. SqueezeNet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm. Heliyon 9 (6): e16827.
  • Ghafurian, M. M., and H. Niazmand. 2018. New approach for estimating the cooling capacity of the absorption and compression chillers in a trigeneration system. International Journal of Refrigeration 86:89–106. doi:10.1016/j.ijrefrig.2017.11.026.
  • Gheydi, M., A. Nouri, and N. Ghadimi. 2016. Planning in microgrids with conservation of voltage reduction. IEEE Systems Journal 12 (3):2782–90. doi:10.1109/JSYST.2016.2633512.
  • Ghiasi, M.,Niknam, T., Wang, Z., Mehrandezh, M., Dehghani, M. and Ghadimi, N. 2023a. A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future. Electric Power Systems Research 215:108975.
  • Ghiasi, M.,Wang, Z., Mehrandezh, M., Jalilian, S. and Ghadimi, N. 2023b. Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation. IET Smart Grid 6 (1):86–102.
  • Guo, H., Gu, W., Khayatnezhad, M. and Ghadimi, N. 2022. Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm. International Journal of Hydrogen Energy 47 (57):24059–68.
  • Han, E., and N. Ghadimi. 2022. Model identification of proton-exchange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm. Sustainable Energy Technologies and Assessments 52:102005.
  • Jiang, W.,Wang, X, Huang, H., Zhang, D. and Ghadimi, N. 2022. Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm. Journal of Energy Storage 55:105311.
  • Khodaei, H., M. Hajiali, A. Darvishan, M. Sepehr, and N. Ghadimi. 2018. Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Applied Thermal Engineering 137:395–405. doi:10.1016/j.applthermaleng.2018.04.008.
  • Leng, H., X. Li, J. Zhu, H. Tang, Z. Zhang, and N. Ghadimi. 2018. A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Advanced Engineering Informatics 36:20–30. doi:10.1016/j.aei.2018.02.006.
  • Liu, J., C. Chen, Z. Liu, K. Jermsittiparsert, and N. Ghadimi. 2020. An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles. Journal of Energy Storage 27:101057. doi:10.1016/j.est.2019.101057.
  • Li, N., X. Zhao, X. Shi, Z. Pei, H. Mu, and F. Taghizadeh-Hesary. 2021. Integrated energy systems with CCHP and hydrogen supply: A new outlet for curtailed wind power. Applied Energy 303:117619. doi:10.1016/j.apenergy.2021.117619.
  • Lü, X., Y. Qu, Y. Wang, C. Qin, and G. Liu. 2018. A comprehensive review on hybrid power system for PEMFC-HEV: Issues and strategies. Energy Conversion and Management 171:1273–91. doi:10.1016/j.enconman.2018.06.065.
  • Mahdinia, S., M. Rezaie, M. Elveny, N. Ghadimi, and N. Razmjooy. 2021. Optimization of PEMFC model parameters using meta-heuristics. Sustainability 13 (22):12771. doi:10.3390/su132212771.
  • Meyer, L., G. TSATSARONIS, J. BUCHGEISTER, and L. SCHEBEK. 2009. Exergoenvironmental analysis for evaluation of the environmental impact of energy conversion systems. Energy 34 (1):75–89. doi:10.1016/j.energy.2008.07.018.
  • Napoli, R., M. Gandiglio, A. Lanzini, and M. Santarelli. 2015. Techno-economic analysis of PEMFC and SOFC micro-CHP fuel cell systems for the residential sector. Energy and Buildings 103:131–46. doi:10.1016/j.enbuild.2015.06.052.
  • Pires, E. S., J. A. Tenreiro Machado, P. B. de Moura Oliveira, J. Boaventura Cunha, and L. Mendes. 2010. Particle swarm optimization with fractional-order velocity. Nonlinear Dynamics 61 (1):295–301. doi:10.1007/s11071-009-9649-y.
  • Ramezani, M., D. Bahmanyar, and N. Razmjooy. 2021. A New improved model of marine predator algorithm for optimization problems. Arabian Journal for Science and Engineering 46 (9):1–24. doi:10.1007/s13369-021-05688-3.
  • Razmjooy, N., M. Ashourian, and Z. Foroozandeh. Metaheuristics and optimization in computer and electrical engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-56689-0.
  • Rezaie, M., Akbari, E., Ghadimi, N., Razmjooy, N. and Ghadamyari, M. 2022. Model parameters estimation of the proton exchange membrane fuel cell by a modified golden jackal optimization. Sustainable Energy Technologies and Assessments 53:102657.
  • Sun, X., G. Wang, L. Xu, H. Yuan, and N. Yousefi. 2021. Optimal performance of a combined heat-power system with a proton exchange membrane fuel cell using a developed marine predators algorithm. Journal of Cleaner Production 284:124776. doi:10.1016/j.jclepro.2020.124776.
  • Yazdani, M., and F. Jolai. 2016. Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering 3 (1):24–36. doi:10.1016/j.jcde.2015.06.003.
  • Yuan, Z., W. Wang, H. Wang, and N. Ghadimi. 2020. Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Generation, Transmission and Distribution 14 (17):3478–87. doi:10.1049/iet-gtd.2019.1625.
  • Zahedi, A., H. A. Z. AL-Bonsrulah, and M. Tafavogh. 2023. Conceptual design and simulation of a stand-alone wind/PEM fuel cell/hydrogen storage energy system for off-grid regions, a case study in Kuhin, Iran. Sustainable Energy Technologies and Assessments 57:103142. doi:10.1016/j.seta.2023.103142.
  • Zhang, J., M. Khayatnezhad, and N. Ghadimi. 2022. Optimal model evaluation of the proton-exchange membrane fuel cells based on deep learning and modified African vulture optimization algorithm. Energy Sources, Part A Recovery, Utilization, & Environmental Effects 44 (1):287–305.
  • Zhao, J., H. Chang, X. Luo, Z. Tu, and S. H. Chan. 2022. Dynamic analysis of a CCHP system based on fuel cells integrated with methanol-reforming and dehumidification for data centers. Applied Energy 309:118496. doi:10.1016/j.apenergy.2021.118496.
  • Zhao, J., S. Li, and Z. Tu. 2023. Development of practical empirically and statistically-based equations for predicting the temperature characteristics of PEMFC applied in the CCHP system. International Journal of Hydrogen Energy. doi:10.1016/j.ijhydene.2022.12.180.
  • Zhi, Y., W. Weiqing, W. Haiyun, and H. Khodaei. 2020. Improved butterfly optimization algorithm for CCHP driven by PEMFC. Applied Thermal Engineering 173:114766. doi:10.1016/j.applthermaleng.2019.114766.
  • Zhu, L.,Zhang, F., Zhang, Q., Chen, Y., Khayatnezhad, M, and Ghadimi, N. 2023. Multi-criteria evaluation and optimization of a novel thermodynamic cycle based on a wind farm, Kalina cycle and storage system: An effort to improve efficiency and sustainability. Sustainable Cities and Society 96: 104718.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.