IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v255y2022ics0360544222014190.html
   My bibliography  Save this article

System estimation of the SOFCs using fractional-order social network search algorithm

Author

Listed:
  • Liu, Lijun
  • Qian, Jin
  • Hua, Li
  • Zhang, Bin

Abstract

In the preset study, a new identifier system has been proposed for optimal model estimation of Solid Oxide Fuel Cell (SOFC) stacks. To afford an appropriate identification system, the voltage-current profile of the system has been considered. The main idea is to minimize the Mean Squared Error (MSE) value between the actual output voltage of the stack and the output value achieved by the proposed model. Here, the MSE minimization has been established based on a new improved metaheuristic technique, called Fractional-order Social Network Search algorithm. The main advantage of the proposed technique is that it provides better trade-off between the global optimum and local optimum values. After system designing, it has been implemented to a studied case and its results are put in comparison with several latest techniques with considering two scenarios. One scenario with constant pressure and variable temperature and one other scenario with constant pressure and variable temperature. Final results indicate that the presented identification system provides satisfying results against the other compared methods in optimum system identification of the SOFCs.

Suggested Citation

  • Liu, Lijun & Qian, Jin & Hua, Li & Zhang, Bin, 2022. "System estimation of the SOFCs using fractional-order social network search algorithm," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222014190
    DOI: 10.1016/j.energy.2022.124516
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222014190
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124516?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Paria Akbary & Mohammad Ghiasi & Mohammad Reza Rezaie Pourkheranjani & Hamidreza Alipour & Noradin Ghadimi, 2019. "Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 1-26, January.
    2. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).
    3. Nassef, Ahmed M. & Fathy, Ahmed & Sayed, Enas Taha & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Tanveer, Waqas Hassan & Olabi, A.G., 2019. "Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms," Renewable Energy, Elsevier, vol. 138(C), pages 458-464.
    4. Moslem Dehghani & Mohammad Ghiasi & Taher Niknam & Abdollah Kavousi-Fard & Mokhtar Shasadeghi & Noradin Ghadimi & Farhad Taghizadeh-Hesary, 2020. "Blockchain-Based Securing of Data Exchange in a Power Transmission System Considering Congestion Management and Social Welfare," Sustainability, MDPI, vol. 13(1), pages 1-21, December.
    5. El-Hay, E.A. & El-Hameed, M.A. & El-Fergany, A.A., 2019. "Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm," Energy, Elsevier, vol. 166(C), pages 451-461.
    6. Cai, Wei & Mohammaditab, Rasoul & Fathi, Gholamreza & Wakil, Karzan & Ebadi, Abdol Ghaffar & Ghadimi, Noradin, 2019. "Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach," Renewable Energy, Elsevier, vol. 143(C), pages 1-8.
    7. Yang, Zaoli & Ghadamyari, Mojtaba & Khorramdel, Hossein & Seyed Alizadeh, Seyed Mehdi & Pirouzi, Sasan & Milani, Muhammed & Banihashemi, Farzad & Ghadimi, Noradin, 2021. "Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    8. Wang, Jian & Xu, Yi-Peng & She, Chen & Xu, Ping & Bagal, Hamid Asadi, 2022. "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm," Energy, Elsevier, vol. 240(C).
    9. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    10. Saeideh Mahdinia & Mehrdad Rezaie & Marischa Elveny & Noradin Ghadimi & Navid Razmjooy, 2021. "Optimization of PEMFC Model Parameters Using Meta-Heuristics," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    11. Wang, Nan & Wang, Dongxuan & Xing, Yazhou & Shao, Limin & Afzal, Sadegh, 2020. "Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model," Renewable Energy, Elsevier, vol. 150(C), pages 221-233.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Hui & Zou, Zhengping & Chen, Yiming & Du, Pengcheng & Fu, Chao & Wang, Yifan, 2023. "Experimental insights into thermal performance of a microtube precooler with drastic coolant properties variation and precooling impacts on turbojet engine operation," Energy, Elsevier, vol. 278(PA).
    2. Mehmood, Ammara & Raja, Muhammad Asif Zahoor & Jalili, Mahdi, 2023. "Optimization of integrated load dispatch in multi-fueled renewable rich power systems using fractal firefly algorithm," Energy, Elsevier, vol. 278(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Jian & Xu, Yi-Peng & She, Chen & Xu, Ping & Bagal, Hamid Asadi, 2022. "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm," Energy, Elsevier, vol. 240(C).
    2. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).
    3. Keyvan Karamnejadi Azar & Armin Kakouee & Morteza Mollajafari & Ali Majdi & Noradin Ghadimi & Mojtaba Ghadamyari, 2022. "Developed Design of Battle Royale Optimizer for the Optimum Identification of Solid Oxide Fuel Cell," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    4. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    5. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).
    6. Liu, Xin & Li, Yang & Lin, Xueshan & Guo, Jiqun & Shi, Yunpeng & Shen, Yunwei, 2022. "Dynamic bidding strategy for a demand response aggregator in the frequency regulation market," Applied Energy, Elsevier, vol. 314(C).
    7. Yang, Zixuan & Liu, Qian & Zhang, Leiyu & Dai, Jialei & Razmjooy, Navid, 2020. "Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm," Energy, Elsevier, vol. 212(C).
    8. Hou, Rui & Li, Shanshan & Wu, Minrong & Ren, Guowen & Gao, Wei & Khayatnezhad, Majid & gholinia, Fatemeh, 2021. "Assessing of impact climate parameters on the gap between hydropower supply and electricity demand by RCPs scenarios and optimized ANN by the improved Pathfinder (IPF) algorithm," Energy, Elsevier, vol. 237(C).
    9. Yuan, Zhi & Wang, Weiqing & Wang, Haiyun & Mizzi, Scott, 2020. "Combination of cuckoo search and wavelet neural network for midterm building energy forecast," Energy, Elsevier, vol. 202(C).
    10. Sun, Xianke & Wang, Gaoliang & Xu, Liuyang & Yuan, Honglei & Yousefi, Nasser, 2021. "Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm," Energy, Elsevier, vol. 237(C).
    11. Ren, Xiaojun & Wu, Yongtang & Hao, Dongmin & Liu, Guoxu & Zafetti, Nicholas, 2021. "Analysis of the performance of the multi-objective hybrid hydropower-photovoltaic-wind system to reduce variance and maximum power generation by developed owl search algorithm," Energy, Elsevier, vol. 231(C).
    12. Qu, Ke & Barreto, Germilly & Iten, Muriel & Wang, Yuhao & Riffat, Saffa, 2023. "Energy and thermal performance of optimised hollow fibre liquid desiccant cooling and dehumidification systems in mediterranean regions: Modelling, validation and case study," Energy, Elsevier, vol. 263(PC).
    13. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    14. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    15. Lu, Xiaohui & Li, Bing & Guo, Lin & Wang, Peifang & Yousefi, Nasser, 2021. "Exergy analysis of a polymer fuel cell and identification of its optimum operating conditions using improved Farmland Fertility Optimization," Energy, Elsevier, vol. 216(C).
    16. Yang, Bo & Guo, Zhengxun & Yang, Yi & Chen, Yijun & Zhang, Rui & Su, Keyi & Shu, Hongchun & Yu, Tao & Zhang, Xiaoshun, 2021. "Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells," Applied Energy, Elsevier, vol. 303(C).
    17. Xu, Yuhao & Luo, Xiaobing & Tu, Zhengkai & Siew Hwa Chan,, 2022. "Multi-criteria assessment of solid oxide fuel cell–combined cooling, heating, and power system model for residential application," Energy, Elsevier, vol. 259(C).
    18. Fathy, Ahmed & Rezk, Hegazy & Mohamed Ramadan, Haitham Saad, 2020. "Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process," Energy, Elsevier, vol. 207(C).
    19. Wang, Erlei & Xia, Jiangying & Li, Jia & Sun, Xianke & Li, Hao, 2022. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm," Energy, Elsevier, vol. 261(PA).
    20. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222014190. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.