Neural networks for power management optimal strategy in hybrid microgrid

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This paper proposes a more reasonable objective function for combined economic emission dispatch problem. To solve it, Lagrange programming neural network (LPNN) is utilized to obtain optimal scheduling of a hybrid microgrid, which includes power generation resources, variable demands and energy storage system for energy storing and supplying. Combining variable neurons with Lagrange neurons, the LPNN aims to minimize the cost function and maximize the power generated by the renewable sources. The asymptotic stability condition of the neurodynamic model is analyzed, and simulation results show that optimal power of each component with certain time interval can be obtained. In addition, a new method by radial basis function neural network is proposed to predict the power values of renewable energy and load demand, which are used as the input values in the optimal process.

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This work was supported in part by the Natural Science Foundation of China under Grant 61403313, Grant 61773320, in part by the Fundamental Research Funds for the Central Universities under Grant XDJK2016B017, Grant XDJK2017D179, in part by the China Post-Doctoral Science Foundation under Grant 2016M600144. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Correspondence to Xing He.



To obtain the upper and lower restriction of battery bank, the algorithm in the following is given. \( {\text{SCB}} \) can take three values corresponding to three different operating modes. Readers can refer to [18] for detail method to calculate the value of restriction in Fig. 17).

Fig. 17

Flowchart of algorithm for the battery system

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Wang, T., He, X. & Deng, T. Neural networks for power management optimal strategy in hybrid microgrid. Neural Comput & Applic 31, 2635–2647 (2019).

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  • Hybrid microgrid
  • RBF neural network prediction
  • Quadratic optimization
  • Lagrange programming neural network
  • Renewable energy sources