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A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach

Author

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  • Taimoor Ahmad Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Amjad Ullah

    (Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Ghulam Hafeez

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Imran Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Sadia Murawwat

    (Department of Electrical Engineering, Lahore College for Women University, Lahore 54000, Pakistan)

  • Faheem Ali

    (Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Sajjad Ali

    (Department of Telecommunication Engineering, University of Engineering and Technology, Peshawar 23200, Pakistan)

  • Sheraz Khan

    (Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan)

  • Khalid Rehman

    (Department of Electrical Engineering, CECOS University of IT & Emerging Sciences, Peshawar 25100, Pakistan)

Abstract

A real-time energy management strategy using dynamic pricing mechanism by deploying a fractional order super twisting sliding mode controller (FOSTSMC) is proposed for correspondence between energy users and providers. This framework, which controls the energy demand of the smart grid’s users is managed by the pricing signal provided by the FOSTSMC, issued to the smart meters, and adjusts the users’ demand to remove the difference between energy demand and generation. For the implementation purpose, a scenario based in MATLAB/Simulink is constructed where a sample renewable energy–integrated smart microgrid is considered. For the validation of the framework, the results of FOSTSMC are compared with the benchmark PI controller’s response. The results of the benchmark PI controller are firstly compared in step response analysis, which is followed by the comparison in deploying in renewable energy–integrated smart grid scenario with multiple users. The results indicate that the FOSTSMC-based controller strategy outperformed the existing PI controller-based strategy in terms of overshoot, energy balance, and energy price regulation.

Suggested Citation

  • Taimoor Ahmad Khan & Amjad Ullah & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Faheem Ali & Sajjad Ali & Sheraz Khan & Khalid Rehman, 2022. "A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach," Energies, MDPI, vol. 15(23), pages 1-14, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9074-:d:989178
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    References listed on IDEAS

    as
    1. Xiao, Yiyong & Zhang, Yue & Kaku, Ikou & Kang, Rui & Pan, Xing, 2021. "Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Kalim Ullah & Sajjad Ali & Taimoor Ahmad Khan & Imran Khan & Sadaqat Jan & Ibrar Ali Shah & Ghulam Hafeez, 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs," Energies, MDPI, vol. 13(21), pages 1-17, November.
    3. Astriani, Yuli & Shafiullah, GM & Shahnia, Farhad, 2021. "Incentive determination of a demand response program for microgrids," Applied Energy, Elsevier, vol. 292(C).
    4. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    5. Li, Hang & Hou, Kai & Xu, Xiandong & Jia, Hongjie & Zhu, Lewei & Mu, Yunfei, 2022. "Probabilistic energy flow calculation for regional integrated energy system considering cross-system failures," Applied Energy, Elsevier, vol. 308(C).
    6. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    7. Peng, Yan & Xu, Zhibing & Wang, Min & Li, Zhongjie & Peng, Jinlin & Luo, Jun & Xie, Shaorong & Pu, Huayan & Yang, Zhengbao, 2021. "Investigation of frequency-up conversion effect on the performance improvement of stack-based piezoelectric generators," Renewable Energy, Elsevier, vol. 172(C), pages 551-563.
    8. Singh, Neeraj Kumar & Mahajan, Vasundhara, 2021. "End-User Privacy Protection Scheme from cyber intrusion in smart grid advanced metering infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
    9. Niknam, Taher & Golestaneh, Faranak & Malekpour, Ahmadreza, 2012. "Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational," Energy, Elsevier, vol. 43(1), pages 427-437.
    10. Balasubramanian, S. & Balachandra, P., 2021. "Effectiveness of demand response in achieving supply-demand matching in a renewables dominated electricity system: A modelling approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    11. Dongmin Yu & Zimeng Ma & Rijun Wang & Wen-Tsao Pan, 2022. "Efficient Smart Grid Load Balancing via Fog and Cloud Computing," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    12. Xianliang Cheng & Suzhen Feng & Yanxuan Huang & Jinwen Wang, 2021. "A New Peak-Shaving Model Based on Mixed Integer Linear Programming with Variable Peak-Shaving Order," Energies, MDPI, vol. 14(4), pages 1-15, February.
    13. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    14. Kaygusuz, Asim, 2019. "Closed loop elastic demand control by dynamic energy pricing in smart grids," Energy, Elsevier, vol. 176(C), pages 596-603.
    15. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    16. Wang, Han & Hou, Kai & Zhao, Junbo & Yu, Xiaodan & Jia, Hongjie & Mu, Yunfei, 2022. "Planning-Oriented resilience assessment and enhancement of integrated electricity-gas system considering multi-type natural disasters," Applied Energy, Elsevier, vol. 315(C).
    17. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
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    1. Tushar Kanti Roy & Amanullah Maung Than Oo & Subarto Kumar Ghosh, 2024. "Designing a High-Order Sliding Mode Controller for Photovoltaic- and Battery Energy Storage System-Based DC Microgrids with ANN-MPPT," Energies, MDPI, vol. 17(2), pages 1-22, January.

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