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Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

Author

Listed:
  • Zhengwei Qu

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Chenglin Xu

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Kai Ma

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Zongxu Jiao

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

Abstract

In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

Suggested Citation

  • Zhengwei Qu & Chenglin Xu & Kai Ma & Zongxu Jiao, 2019. "Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation," Energies, MDPI, vol. 12(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2463-:d:243081
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    References listed on IDEAS

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    1. Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
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    Cited by:

    1. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
    2. Balvender Singh & Adam Slowik & Shree Krishan Bishnoi, 2023. "Review on Soft Computing-Based Controllers for Frequency Regulation of Diverse Traditional, Hybrid, and Future Power Systems," Energies, MDPI, vol. 16(4), pages 1-30, February.
    3. Ibrahim Ali Kachalla & Christian Ghiaus, 2024. "Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions," Energies, MDPI, vol. 17(2), pages 1-32, January.
    4. Nina Strobel & Daniel Fuhrländer-Völker & Matthias Weigold & Eberhard Abele, 2020. "Quantifying the Demand Response Potential of Inherent Energy Storages in Production Systems," Energies, MDPI, vol. 13(16), pages 1-22, August.

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