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Hierarchical scheduling learning optimisation of two-area active distribution system considering peak shaving demand of power grid

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Abstract

Aiming at industrial parks and business parks equipped with photovoltaic (PV) power plants and vanadium redox battery energy storage devices, this work studies the collaborative scheduling optimisation problem of real-time response of two-area active distribution system to the random peak shaving demand of large power grids. Firstly, considering the randomness of source and load, the stochastic dynamic changes of PV output, various load demands and the grid peak shaving demand are described as Gauss-Markov processes. Secondly, the hierarchical dynamic control mode is used to transform the collaborative dynamic scheduling problem of two-area active distribution system into a two-layer scheduling optimisation model. The upper layer considers the total cost of operation as the optimal goal and resolves problems related to the task assignment of peak shaving demand for active distribution systems in each area. Meanwhile, the lower-layer areas are optimised to complete the peak shaving task assigned by the upper layer and realise the economic operation of the active distribution system in each area. This study proposes a corresponding model-independent double-layer Q learning algorithm to optimise the hierarchical scheduling strategy. A simulation is conducted to verify the effectiveness of this algorithm. These results indicate that the hierarchical scheduling optimisation mechanism and double-layer Q learning algorithm can effectively solve the collaborative scheduling problem of two-area active distribution systems considering the peak shaving demand of the power grid.

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Acknowledgements

This work was supported by State Grid Corporation of China Project ”Intelligent Scheduling Technology based on Deep Learning in Flexible Enviroment (SGTYHT/19-JS-215)”

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Hao Tang, Chang Liu, Yonglun Cao, Kai Lv & Qianli Zhang

  2. China Mobile Group Anhui Co., Ltd., Hefei, 230031, China

    Chang Liu

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  1. Hao Tang
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  2. Chang Liu
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  3. Yonglun Cao
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  4. Kai Lv
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Correspondence to Hao Tang.

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This article belongs to the Topical Collection: Topical Collection on Smart Cities

Guest Editors: (Samuel) Qing-Shan Jia, Mariagrazia Dotoli, and Qian-chuan Zhao

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Tang, H., Liu, C., Cao, Y. et al. Hierarchical scheduling learning optimisation of two-area active distribution system considering peak shaving demand of power grid. Discrete Event Dyn Syst 31, 439–468 (2021). https://doi.org/10.1007/s10626-020-00335-9

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