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Energy management of controllable loads in multi-area power systems with wind power penetration based on new supervisor fuzzy nonlinear sliding mode control

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  • Elsisi, Mahmoud
  • Bazmohammadi, Najmeh
  • Guerrero, Josep M.
  • Ebrahim, Mohamed A.

Abstract

The distributed controllable loads (DCLs) are employed to achieve energy management for hybrid renewable energy-based multi-area power systems (HREPSs). The DCLs are suggested as a cheaper solution instead of the expensive energy storage systems. Due to the nonlinearity, high variability and uncertain nature of HREPSs based on DCLs, the need for an artificial intelligence-based nonlinear energy management system becomes mandatory. This paper suggests a hybrid control methodology based on fuzzy logic and nonlinear sliding mode control (FL-NLSM) to manage the energy of DCLs in a smart grid. The proposed hybrid control strategy merges the unique properties of both FL and NLSM to handle the system nonlinearities and to improve the damping characteristics of the system response against the uncertainties of the parameters; as well as the high variability of renewable energy resources such as wind power and the load demand fluctuations. Moreover, the gains of the proposed FL-NLSM controller are optimized by the imperialist competitive algorithm that is considered a powerful artificial intelligent technique. The modified benchmark IEEE-39 bus test system is utilized to accomplish this study. The output results confirm that the proposed FL-NLSM can diminish the deviations of the system frequency and tie-line power between different areas effectively.

Suggested Citation

  • Elsisi, Mahmoud & Bazmohammadi, Najmeh & Guerrero, Josep M. & Ebrahim, Mohamed A., 2021. "Energy management of controllable loads in multi-area power systems with wind power penetration based on new supervisor fuzzy nonlinear sliding mode control," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s036054422100116x
    DOI: 10.1016/j.energy.2021.119867
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    References listed on IDEAS

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    Cited by:

    1. Mahmoodabadi, M.J., 2023. "An optimal robust fuzzy adaptive integral sliding mode controller based upon a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    2. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    3. Hui, Jiuwu & Yuan, Jingqi, 2022. "Load following control of a pressurized water reactor via finite-time super-twisting sliding mode and extended state observer techniques," Energy, Elsevier, vol. 241(C).
    4. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    5. Yu, Min Gyung & Pavlak, Gregory S., 2022. "Extracting interpretable building control rules from multi-objective model predictive control data sets," Energy, Elsevier, vol. 240(C).
    6. Abulanwar, Sayed & Ghanem, Abdelhady & Rizk, Mohammad E.M. & Hu, Weihao, 2021. "Adaptive synergistic control strategy for a hybrid AC/DC microgrid during normal operation and contingencies," Applied Energy, Elsevier, vol. 304(C).
    7. Balvender Singh & Adam Slowik & Shree Krishan Bishnoi & Mandeep Sharma, 2023. "Frequency Regulation Strategy of Two-Area Microgrid System with Electric Vehicle Support Using Novel Fuzzy-Based Dual-Stage Controller and Modified Dragonfly Algorithm," Energies, MDPI, vol. 16(8), pages 1-24, April.
    8. Mahmoud Elsisi & Hatim G. Zaini & Karar Mahmoud & Shimaa Bergies & Sherif S. M. Ghoneim, 2021. "Improvement of Trajectory Tracking by Robot Manipulator Based on a New Co-Operative Optimization Algorithm," Mathematics, MDPI, vol. 9(24), pages 1-21, December.
    9. Cao, Qian & Wei, Du Qu, 2023. "Dynamic surface sliding mode control of chaos in the fourth-order power system," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    10. Stanek, Wojciech, 2022. "Thermo-Ecological Cost (TEC) –comparison of energy-ecological efficiency of renewable and non-renewable energy technologies," Energy, Elsevier, vol. 261(PA).

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