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Time series generative adversarial network controller for long-term smart generation control of microgrids

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  • Yin, Linfei
  • Zhang, Bin

Abstract

The conventional combined generation control framework of microgrids, which contains two time-scales, i.e., the time slot of economic dispatch is set to 15 min; and the total time slot of smart generation control and generation command dispatch is set to 4 s, could lead to uncoordinated problems. To avoid uncoordinated problems, this paper proposes a long-term smart generation control framework with a single time-scale to replace the conventional combined generation control framework with two time-scales, and then proposes time series generative adversarial network controller for long-term smart generation control of microgrids. The proposed time series generative adversarial network controller contains reinforcement learning, generator deep neural networks, and discriminator deep neural networks. The generator deep neural networks generate predicted states from multiple historical states, multiple historical actions, and multiple long-term actions. The discriminator deep neural networks judge whether the data from the generator deep neural networks or real-life data. This paper compares the proposed controller with conventional optimization algorithms and control algorithms, which are applied for economic dispatch, smart generation control, and generation commands dispatch in microgrids. The numerical simulation results under Hainan Power Grid, IEEE 300-bus power system, and IEEE 1951-bus power system verify that the proposed time series generative adversarial network controller can simultaneously obtain higher control performance and smaller economic cost than conventional combined control algorithm and optimization algorithms in the long-term. Consequently, the uncoordinated problem of economic dispatch, smart generation control, and generation commands dispatch can be solved by the proposed approach with one single long-term time-scale.

Suggested Citation

  • Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
  • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920314975
    DOI: 10.1016/j.apenergy.2020.116069
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    References listed on IDEAS

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    1. Yin, Linfei & Yu, Tao & Zhang, Xiaoshun & Yang, Bo, 2018. "Relaxed deep learning for real-time economic generation dispatch and control with unified time scale," Energy, Elsevier, vol. 149(C), pages 11-23.
    2. AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Li, Yan & Adamowski, Jan F., 2018. "Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting," Applied Energy, Elsevier, vol. 217(C), pages 422-439.
    3. Chan, C.M. & Bai, H.L. & He, D.Q., 2018. "Blade shape optimization of the Savonius wind turbine using a genetic algorithm," Applied Energy, Elsevier, vol. 213(C), pages 148-157.
    4. Christos-Spyridon Karavas & Konstantinos Arvanitis & George Papadakis, 2017. "A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids," Energies, MDPI, vol. 10(11), pages 1-22, November.
    5. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    6. Ghasemi-Marzbali, Ali, 2020. "Multi-area multi-source automatic generation control in deregulated power system," Energy, Elsevier, vol. 201(C).
    7. Xi, Lei & Chen, Jianfeng & Huang, Yuehua & Xu, Yanchun & Liu, Lang & Zhou, Yimin & Li, Yudan, 2018. "Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel," Energy, Elsevier, vol. 153(C), pages 977-987.
    8. Jiang, C.X. & Jing, Z.X. & Cui, X.R. & Ji, T.Y. & Wu, Q.H., 2018. "Multiple agents and reinforcement learning for modelling charging loads of electric taxis," Applied Energy, Elsevier, vol. 222(C), pages 158-168.
    9. Han, Xuefeng & He, Hongwen & Wu, Jingda & Peng, Jiankun & Li, Yuecheng, 2019. "Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 254(C).
    10. Zhang, Xiaoshun & Tan, Tian & Yang, Bo & Wang, Jingbo & Li, Shengnan & He, Tingyi & Yang, Lei & Yu, Tao & Sun, Liming, 2020. "Greedy search based data-driven algorithm of centralized thermoelectric generation system under non-uniform temperature distribution," Applied Energy, Elsevier, vol. 260(C).
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    Cited by:

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    10. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).

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