Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Improved Model Predictive Control with Prescribed Performance for Aggregated Thermostatically Controlled Loads
Author:
Affiliation:

1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding, China;2.Innovation Center for Hebei Intelligent Grid Distribution Technology, Shijiazhuang Kelin Electric Co., Ltd., Shijiazhuang, China

Fund Project:

This work was supported by the key projects in 2018 National Key R&D Programs (No. 2018YFE0122200), the Fundamental Research Funds for the Central Universities (No. 2020MS090), and opening project of Hebei Smart Grid Distribution and Utilization Technology Innovation Center (No. 20200803).

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    Abstract:

    Aggregate thermostatically controlled loads (ATCLs) are a suitable candidate for power imbalance on demand side to smooth the power fluctuation of renewable energy. A new control scheme based on an improved bilinear aggregate model of ATCLs is investigated to suppress power imbalance. Firstly, the original bilinear aggregate model of ATCLs is extended by the second-order equivalent thermal parameter model to optimize accumulative error over a long time scale. Then, to ensure the control performance of tracking error, an improved model predictive control algorithm is proposed by integrating the Lyapunov function with the error transformation, and theoretical stability of the proposed control algorithm is proven. Finally, the simulation results demonstrate that the accuracy of the improved bilinear aggregate model is enhanced; the proposed control algorithm has faster convergence speed and better tracking accuracy in contrast with the Lyapunov function-based model predictive control without the prescribed performance.

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History
  • Received:November 30,2020
  • Revised:March 15,2021
  • Adopted:
  • Online: March 30,2022
  • Published: