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Volume 8 Issue 7
Jul.  2021

IEEE/CAA Journal of Automatica Sinica

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Chentao Xu and Xing He, "A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1325-1335, July 2021. doi: 10.1109/JAS.2021.1004048
Citation: Chentao Xu and Xing He, "A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1325-1335, July 2021. doi: 10.1109/JAS.2021.1004048

A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

doi: 10.1109/JAS.2021.1004048
Funds:  This work was supported by the Natural Science Foundation of China (61773320), Fundamental Research Funds for the Central Universities (XDJK2020TY003), and also supported by the Natural Science Foundation Project of Chongqing Science and Technology Commission (cstc2018jcyjAX0583)
More Information
  • A fully distributed microgrid system model is presented in this paper. In the user side, two types of load and plug-in electric vehicles are considered to schedule energy for more benefits. The charging and discharging states of the electric vehicles are represented by the zero-one variables with more flexibility. To solve the nonconvex optimization problem of the users, a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster. A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their privacy. Simulation results and comparative experiments show that the model and algorithms are effective.

     

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    Highlights

    • This paper presents a fully distributed scheme in smart grid for optimal energy scheduling considering both user side and generator side.
    • This paper uses a zero-one variable to distinguish the charging and discharging states of electric vehicles.
    • This paper designs a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm to deal with the nonconvex optimization problem of the user side. The simulation result shows the designed algorithm has faster convergence speed than the existed neurodynamic algorithm which combines the neural network algorithm with the particle swarm optimization algorithm.
    • This paper considers a new approach to deal with the inequality constraints for the convex optimization problem of the generator side and uses a distributed algorithm to solve it. The simulation result shows the approach is effective.

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