A combined neurodynamic approach to optimize the real-time price-based demand response management problem using mixed zero-one programming

Abstract

This paper presents a microgrid system model considering three types of load and the user’s satisfaction function. The objective function with mixed zero-one programming is used to maximize every user’s profit and satisfaction in the way of the demand response management under real-time price. An energy function is used to transform the constrained problem into an unconstrained problem, and two neural networks are used to find the local optimal solutions of the objective function with different rates of convergence. A neurodynamic approach is used to combine the neural networks with the particle swarm optimization to find the global optimal solution of the objective function. The simulation results show that the combined approach is effective in solving the optimal problem.

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Acknowledgements

This work is supported by Fundamental Research Funds for the Central Universities (Project No. XDJK2019B010), and supported by the Natural Science Foundation of China (Grant No.: 61773320), and also supported by the Natural Science Foundation Project of Chongqing CSTC (Grant Nos. cstc2018jcyjAX0583, cstc2018jcyjAX0810). This publication was made possible by NPRP Grant No. NPRP 7-1482-1-278 from the Qatar National Research Fund (a member of Qatar Foundation), and Research Foundation of Key laboratory of Machine Perception and Children’s Intelligence Development funded by CQUE(16xjpt07), China.

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Correspondence to Xing He.

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Xu, C., He, X., Huang, T. et al. A combined neurodynamic approach to optimize the real-time price-based demand response management problem using mixed zero-one programming. Neural Comput & Applic 32, 8799–8809 (2020). https://doi.org/10.1007/s00521-019-04283-w

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Keywords

  • Demand response
  • Neural network
  • Mixed zero-one programming
  • Particle swarm optimization